Big Data and Data Analytics 101 – Top 20 AWS Certified Data Analytics – Specialty Questions and Answers Dumps

AWS Certified Security – Specialty Questions and Answers Dumps

In this blog, we talk about big data and data analytics; we also give you the last updated top 20 AWS Certified Data Analytics – Specialty Questions and Answers Dumps

I- The AWS Certified Data Analytics – Specialty (DAS-C01) examination is intended for individuals who perform in a data analytics-focused role. This exam validates an examinee’s comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.

aws big data analytics certification specialty questions and answers dump
aws big data analytics certification specialty questions and answers dump

The AWS Certified Data Analytics – Specialty (DAS-C01) covers the following domains:

Domain 1: Collection 18%

Domain 2: Storage and Data Management 22%

Domain 3: Processing 24%

Domain 4: Analysis and Visualization 18%

Domain 5: Security 18%

data analytics specialty
data analytics specialty

Below are the Top 20 AWS Certified Data Analytics – Specialty Questions and Answers Dumps and References –

Question1: What combination of services do you need for the following requirements: accelerate petabyte-scale data transfers, load streaming data, and the ability to create scalable, private connections. Select the correct answer order.

A) Snowball, Kinesis Firehose, Direct Connect

B) Data Migration Services, Kinesis Firehose, Direct Connect

C) Snowball, Data Migration Services, Direct Connect

D) Snowball, Direct Connection, Kinesis Firehose

ANSWER1:

A

Notes/Hint1:

AWS has many options to help get data into the cloud, including secure devices like AWS Import/Export Snowball to accelerate petabyte-scale data transfers, Amazon Kinesis Firehose to load streaming data, and scalable private connections through AWS Direct Connect.

Reference1: Big Data Analytics Options 

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ANSWER2:

C

Notes/Hint2:

Reference1: Relationalize PySpark

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Question 3: There is a five-day car rally race across Europe. The race coordinators are using a Kinesis stream and IoT sensors to monitor the movement of the cars. Each car has a sensor and data is getting back to the stream with the default stream settings. On the last day of the rally, data is sent to S3. When you go to interpret the data in S3, there is only data for the last day and nothing for the first 4 days. Which of the following is the most probable cause of this?

A) You did not have versioning enabled and would need to create individual buckets to prevent the data from being overwritten.

B) Data records are only accessible for a default of 24 hours from the time they are added to a stream.

C) One of the sensors failed, so there was no data to record.

D) You needed to use EMR to send the data to S3; Kinesis Streams are only compatible with DynamoDB.

ANSWER3:

B

Notes/Hint3: 

Streams support changes to the data record retention period of your stream. An Amazon Kinesis stream is an ordered sequence of data records, meant to be written to and read from in real-time. Data records are therefore stored in shards in your stream temporarily. The period from when a record is added to when it is no longer accessible is called the retention period. An Amazon Kinesis stream stores records for 24 hours by default, up to 168 hours.

Reference3: Kinesis Extended Reading

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Question 4:  A publisher website captures user activity and sends clickstream data to Amazon Kinesis Data Streams. The publisher wants to design a cost-effective solution to process the data to create a timeline of user activity within a session. The solution must be able to scale depending on the number of active sessions.
Which solution meets these requirements?

A) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on Amazon EC2 instances in an EC2 Auto Scaling group.

B) Include a variable in the clickstream to maintain a counter for each user action during their session. Use the action type as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the
counter. Deploy the consumer application on AWS Lambda.

C) Include a session identifier in the clickstream data from the publisher website and use as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Deploy the consumer application on Amazon EC2 instances in an
EC2 Auto Scaling group. Use an AWS Lambda function to reshard the stream based upon Amazon CloudWatch alarms.

D) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on AWS Lambda.

ANSWER4:

C

Notes/Hint4: 

Partitioning by the session ID will allow a single processor to process all the actions for a user session in order. An AWS Lambda function can call the UpdateShardCount API action to change the number of shards in the stream. The KCL will automatically manage the number of processors to match the number of shards. Amazon EC2 Auto Scaling will assure the correct number of instances are running to meet the processing load.

Reference4: UpdateShardCount API

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Question 5: Your company has two batch processing applications that consume financial data about the day’s stock transactions. Each transaction needs to be stored durably and guarantee that a record of each application is delivered so the audit and billing batch processing applications can process the data. However, the two applications run separately and several hours apart and need access to the same transaction information. After reviewing the transaction information for the day, the information no longer needs to be stored. What is the best way to architect this application?

A) Use SQS for storing the transaction messages; when the billing batch process performs first and consumes the message, write the code in a way that does not remove the message after consumed, so it is available for the audit application several hours later. The audit application can consume the SQS message and remove it from the queue when completed.

B)  Use Kinesis to store the transaction information. The billing application will consume data from the stream and the audit application can consume the same data several hours later.

C) Store the transaction information in a DynamoDB table. The billing application can read the rows while the audit application will read the rows then remove the data.

D) Use SQS for storing the transaction messages. When the billing batch process consumes each message, have the application create an identical message and place it in a different SQS for the audit application to use several hours later.

SQS would make this more difficult because the data does not need to persist after a full day.

ANSWER5:

B

Notes/Hint5: 

Kinesis appears to be the best solution that allows multiple consumers to easily interact with the records.

Reference5: Amazon Kinesis

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Question 6: A company is currently using Amazon DynamoDB as the database for a user support application. The company is developing a new version of the application that will store a PDF file for each support case ranging in size from 1–10 MB. The file should be retrievable whenever the case is accessed in the application.
How can the company store the file in the MOST cost-effective manner?

A) Store the file in Amazon DocumentDB and the document ID as an attribute in the DynamoDB table.

B) Store the file in Amazon S3 and the object key as an attribute in the DynamoDB table.

C) Split the file into smaller parts and store the parts as multiple items in a separate DynamoDB table.

D) Store the file as an attribute in the DynamoDB table using Base64 encoding.

ANSWER6:

B

Notes/Hint6: 

Use Amazon S3 to store large attribute values that cannot fit in an Amazon DynamoDB item. Store each file as an object in Amazon S3 and then store the object path in the DynamoDB item.

Reference6: S3 Storage Cost –  DynamODB Storage Cost

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Question 7: Your client has a web app that emits multiple events to Amazon Kinesis Streams for reporting purposes. Critical events need to be immediately captured before processing can continue, but informational events do not need to delay processing. What solution should your client use to record these types of events without unnecessarily slowing the application?

A) Log all events using the Kinesis Producer Library.

B) Log critical events using the Kinesis Producer Library, and log informational events using the PutRecords API method.

C) Log critical events using the PutRecords API method, and log informational events using the Kinesis Producer Library.

D) Log all events using the PutRecords API method.

ANSWER2:

C

Notes/Hint7: 

The PutRecords API can be used in code to be synchronous; it will wait for the API request to complete before the application continues. This means you can use it when you need to wait for the critical events to finish logging before continuing. The Kinesis Producer Library is asynchronous and can send many messages without needing to slow down your application. This makes the KPL ideal for the sending of many non-critical alerts asynchronously.

Reference7: PutRecords API

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Question 8: You work for a start-up that tracks commercial delivery trucks via GPS. You receive coordinates that are transmitted from each delivery truck once every 6 seconds. You need to process these coordinates in near real-time from multiple sources and load them into Elasticsearch without significant technical overhead to maintain. Which tool should you use to digest the data?

A) Amazon SQS

B) Amazon EMR

C) AWS Data Pipeline

D) Amazon Kinesis Firehose

ANSWER8:

D

Notes/Hint8: 

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service, enabling near real-time analytics with existing business intelligence tools and dashboards.

Reference8: Amazon Kinesis Firehose

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Question 9: A company needs to implement a near-real-time fraud prevention feature for its ecommerce site. User and order details need to be delivered to an Amazon SageMaker endpoint to flag suspected fraud. The amount of input data needed for the inference could be as much as 1.5 MB.
Which solution meets the requirements with the LOWEST overall latency?

A) Create an Amazon Managed Streaming for Kafka cluster and ingest the data for each order into a topic. Use a Kafka consumer running on Amazon EC2 instances to read these messages and invoke the Amazon SageMaker endpoint.

B) Create an Amazon Kinesis Data Streams stream and ingest the data for each order into the stream. Create an AWS Lambda function to read these messages and invoke the Amazon SageMaker endpoint.

C) Create an Amazon Kinesis Data Firehose delivery stream and ingest the data for each order into the stream. Configure Kinesis Data Firehose to deliver the data to an Amazon S3 bucket. Trigger an AWS Lambda function with an S3 event notification to read the data and invoke the Amazon SageMaker endpoint.

D) Create an Amazon SNS topic and publish the data for each order to the topic. Subscribe the Amazon SageMaker endpoint to the SNS topic.


ANSWER9:

A

Notes/Hint9: 

An Amazon Managed Streaming for Kafka cluster can be used to deliver the messages with very low latency. It has a configurable message size that can handle the 1.5 MB payload.

Reference9: Amazon Managed Streaming for Kafka cluster

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Question 10: You need to filter and transform incoming messages coming from a smart sensor you have connected with AWS. Once messages are received, you need to store them as time series data in DynamoDB. Which AWS service can you use?

A) IoT Device Shadow Service

B) Redshift

C) Kinesis

D) IoT Rules Engine

ANSWER10:

D

Notes/Hint10: 

The IoT rules engine will allow you to send sensor data over to AWS services like DynamoDB

Reference10: The IoT rules engine

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Question 11: A media company is migrating its on-premises legacy Hadoop cluster with its associated data processing scripts and workflow to an Amazon EMR environment running the latest Hadoop release. The developers want to reuse the Java code that was written for data processing jobs for the on-premises cluster.
Which approach meets these requirements?

A) Deploy the existing Oracle Java Archive as a custom bootstrap action and run the job on the EMR cluster.

B) Compile the Java program for the desired Hadoop version and run it using a CUSTOM_JAR step on the EMR cluster.

C) Submit the Java program as an Apache Hive or Apache Spark step for the EMR cluster.

D) Use SSH to connect the master node of the EMR cluster and submit the Java program using the AWS CLI.


ANSWER11:

B

Notes/Hint11: 

A CUSTOM JAR step can be configured to download a JAR file from an Amazon S3 bucket and execute it. Since the Hadoop versions are different, the Java application has to be recompiled.

Reference11:  Automating analytics workflows on EMR

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Question 12: You currently have databases running on-site and in another data center off-site. What service allows you to consolidate to one database in Amazon?

A) AWS Kinesis

B) AWS Database Migration Service

C) AWS Data Pipeline

D) AWS RDS Aurora

ANSWER12:

B

Notes/Hint12: 

AWS Database Migration Service can migrate your data to and from most of the widely used commercial and open source databases. It supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle to Amazon Aurora. Migrations can be from on-premises databases to Amazon RDS or Amazon EC2, databases running on EC2 to RDS, or vice versa, as well as from one RDS database to another RDS database.

Reference12: DMS

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Question 13:  An online retail company wants to perform analytics on data in large Amazon S3 objects using Amazon EMR. An Apache Spark job repeatedly queries the same data to populate an analytics dashboard. The analytics team wants to minimize the time to load the data and create the dashboard.
Which approaches could improve the performance? (Select TWO.)
A) Copy the source data into Amazon Redshift and rewrite the Apache Spark code to create analytical reports by querying Amazon Redshift.

B) Copy the source data from Amazon S3 into Hadoop Distributed File System (HDFS) using s3distcp.

C) Load the data into Spark DataFrames.

D) Stream the data into Amazon Kinesis and use the Kinesis Connector Library (KCL) in multiple Spark jobs to perform analytical jobs.

E) Use Amazon S3 Select to retrieve the data necessary for the dashboards from the S3 objects.

ANSWER13:

C and E

Notes/Hint13: 

One of the speed advantages of Apache Spark comes from loading data into immutable dataframes, which can be accessed repeatedly in memory. Spark DataFrames organizes distributed data into columns. This makes summaries and aggregates much quicker to calculate. Also, instead of loading an entire large Amazon S3 object, load only what is needed using Amazon S3 Select. Keeping the data in Amazon S3 avoids loading the large dataset into HDFS.

Reference13: Spark DataFrames 

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Question 14: You have been hired as a consultant to provide a solution to integrate a client’s on-premises data center to AWS. The customer requires a 300 Mbps dedicated, private connection to their VPC. Which AWS tool do you need?

A) VPC peering

B) Data Pipeline

C) Direct Connect

D) EMR

ANSWER14:

C

Notes/Hint14: 

Direct Connect will provide a dedicated and private connection to an AWS VPC.

Reference14: Direct Connect


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Question 15: Your organization has a variety of different services deployed on EC2 and needs to efficiently send application logs over to a central system for processing and analysis. They’ve determined it is best to use a managed AWS service to transfer their data from the EC2 instances into Amazon S3 and they’ve decided to use a solution that will do what?

A) Installs the AWS Direct Connect client on all EC2 instances and uses it to stream the data directly to S3.

B) Leverages the Kinesis Agent to send data to Kinesis Data Streams and output that data in S3.

C) Ingests the data directly from S3 by configuring regular Amazon Snowball transactions.

D) Leverages the Kinesis Agent to send data to Kinesis Firehose and output that data in S3.

ANSWER15:

D

Notes/Hint15: 

Kinesis Firehose is a managed solution, and log files can be sent from EC2 to Firehose to S3 using the Kinesis agent.

Reference15: Kinesis Firehose

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Question 16: A data engineer needs to create a dashboard to display social media trends during the last hour of a large company event. The dashboard needs to display the associated metrics with a latency of less than 1 minute.
Which solution meets these requirements?

A) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Use Kinesis Data Analytics for SQL Applications to perform a sliding window analysis to compute the metrics and output the results to a Kinesis Data Streams data stream. Configure an AWS Lambda function to save the stream data to an Amazon DynamoDB table. Deploy a real-time dashboard hosted in an Amazon S3 bucket to read and display the metrics data stored in the DynamoDB table.

B) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Elasticsearch Service cluster with a buffer interval of 0 seconds. Use Kibana to perform the analysis and display the results.

C) Publish the raw social media data to an Amazon Kinesis Data Streams data stream. Configure an AWS Lambda function to compute the metrics on the stream data and save the results in an Amazon S3 bucket. Configure a dashboard in Amazon QuickSight to query the data using Amazon Athena and display the results.

D) Publish the raw social media data to an Amazon SNS topic. Subscribe an Amazon SQS queue to the topic. Configure Amazon EC2 instances as workers to poll the queue, compute the metrics, and save the results to an Amazon Aurora MySQL database. Configure a dashboard in Amazon QuickSight to query the data in Aurora and display the results.


ANSWER16:

A

Notes/Hint16: 

Amazon Kinesis Data Analytics can query data in a Kinesis Data Firehose delivery stream in near-real time using SQL. A sliding window analysis is appropriate for determining trends in the stream. Amazon S3 can host a static webpage that includes JavaScript that reads the data in Amazon DynamoDB and refreshes the dashboard.

Reference16: Amazon Kinesis Data Analytics can query data in a Kinesis Data Firehose delivery stream in near-real time using SQL

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Question 17: A real estate company is receiving new property listing data from its agents through .csv files every day and storing these files in Amazon S3. The data analytics team created an Amazon QuickSight visualization report that uses a dataset imported from the S3 files. The data analytics team wants the visualization report to reflect the current data up to the previous day. How can a data analyst meet these requirements?

A) Schedule an AWS Lambda function to drop and re-create the dataset daily.

B) Configure the visualization to query the data in Amazon S3 directly without loading the data into SPICE.

C) Schedule the dataset to refresh daily.

D) Close and open the Amazon QuickSight visualization.

ANSWER17:

B

Notes/Hint17:

Datasets created using Amazon S3 as the data source are automatically imported into SPICE. The Amazon QuickSight console allows for the refresh of SPICE data on a schedule.

Reference17: Amazon QuickSight and SPICE


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Question 18: You need to migrate data to AWS. It is estimated that the data transfer will take over a month via the current AWS Direct Connect connection your company has set up. Which AWS tool should you use?

A) Establish additional Direct Connect connections.

B) Use Data Pipeline to migrate the data in bulk to S3.

C) Use Kinesis Firehose to stream all new and existing data into S3.

D) Snowball

ANSWER18:

D

Notes/Hint18:

As a general rule, if it takes more than one week to upload your data to AWS using the spare capacity of your existing Internet connection, then you should consider using Snowball. For example, if you have a 100 Mb connection that you can solely dedicate to transferring your data and need to transfer 100 TB of data, it takes more than 100 days to complete a data transfer over that connection. You can make the same transfer by using multiple Snowballs in about a week.

Reference18: Snowball

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Question 19: You currently have an on-premises Oracle database and have decided to leverage AWS and use Aurora. You need to do this as quickly as possible. How do you achieve this?

A) It is not possible to migrate an on-premises database to AWS at this time.

B) Use AWS Data Pipeline to create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switchover of your production environment to the new database once the target database is caught up with the source database.

C) Use AWS Database Migration Services and create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switch-over of your production environment to the new database once the target database is caught up with the source database.

D) Use AWS Glue to crawl the on-premises database schemas and then migrate them into AWS with Data Pipeline jobs.

https://aws.amazon.com/dms/faqs/

ANSWER2:

C

Notes/Hint19: 

DMS can efficiently support this sort of migration using the steps outlined. While AWS Glue can help you crawl schemas and store metadata on them inside of Glue for later use, it isn't the best tool for actually transitioning a database over to AWS itself. Similarly, while Data Pipeline is great for ETL and ELT jobs, it isn't the best option to migrate a database over to AWS.

Reference19: DMS


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Question 20: A financial company uses Amazon EMR for its analytics workloads. During the company’s annual security audit, the security team determined that none of the EMR clusters’ root volumes are encrypted. The security team recommends the company encrypt its EMR clusters’ root volume as soon as possible.
Which solution would meet these requirements?

A) Enable at-rest encryption for EMR File System (EMRFS) data in Amazon S3 in a security configuration. Re-create the cluster using the newly created security configuration.

B) Specify local disk encryption in a security configuration. Re-create the cluster using the newly created security configuration.

C) Detach the Amazon EBS volumes from the master node. Encrypt the EBS volume and attach it back to the master node.

D) Re-create the EMR cluster with LZO encryption enabled on all volumes.

ANSWER20:

B

Notes/Hint20: 

Local disk encryption can be enabled as part of a security configuration to encrypt root and storage volumes.

Reference20: EMR Cluster Local disk encryption

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II- SOURCE:

1- Djamga Big Data – Data Analytics Youtube Playlist

2- Prepare for Your AWS Certification Exam

3- LinuxAcademy

III- Big Data – Data Analytics Jobs:

III- Big Data – Data Analytics – Data Sciences Latest News:


IV- DATA ANALYTICS Q&A:

What are the corresponding Azure and Google Cloud services for each of the AWS services?

Azure Administrator AZ-104 Exam Questions and Answers Dumps

What are unique distinctions and similarities between AWS, Azure and Google Cloud services? For each AWS service, what is the equivalent Azure and Google Cloud service? For each Azure service, what is the corresponding Google Service? AWS Services vs Azure vs Google Services? Side by side comparison between AWS, Google Cloud and Azure Service?

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AWS vs Azure vs Google
AWS vs Azure vs Google Mobile App
Cloud Practitioner Exam Prep:  AWS vs Azure vs Google
Cloud Practitioner Exam Prep: AWS vs Azure vs Google

1

Category: Marketplace
Easy-to-deploy and automatically configured third-party applications, including single virtual machine or multiple virtual machine solutions.
References:
[AWS]:AWS Marketplace
[Azure]:Azure Marketplace
[Google]:Google Cloud Marketplace
Tags: #AWSMarketplace, #AzureMarketPlace, #GoogleMarketplace
Differences: They are both digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on their respective cloud platform.

3

Category: AI and machine learning
Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular services.
References:
[AWS]:Alexa Skills Kit (enables a developer to build skills, also called conversational applications, on the Amazon Alexa artificial intelligence assistant.)
[Azure]:Microsoft Bot Framework (building enterprise-grade conversational AI experiences.)
[Google]:Google Assistant Actions ( developer platform that lets you create software to extend the functionality of the Google Assistant, Google’s virtual personal assistant,)

Tags: #AlexaSkillsKit, #MicrosoftBotFramework, #GoogleAssistant
Differences: One major advantage Google gets over Alexa is that Google Assistant is available to almost all Android devices.

4

Category: AI and machine learning
Description:API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.
References:
[AWS]:Amazon Lex (building conversational interfaces into any application using voice and text.)
[Azure]:Azure Speech Services(unification of speech-to-text, text-to-speech, and speech translation into a single Azure subscription)
[Google]:Google APi.ai, AI Hub (Hosted repo of plug-and-play AI component), AI building blocks(for developers to add sight, language, conversation, and structured data to their applications.), AI Platform(code-based data science development environment, lets ML developers and data scientists quickly take projects from ideation to deployment.), DialogFlow (Google-owned developer of human–computer interaction technologies based on natural language conversations. ), TensorFlow(Open Source Machine Learning platform)

Tags: #AmazonLex, #CogintiveServices, #AzureSpeech, #Api.ai, #DialogFlow, #Tensorflow
Differences: api.ai provides us with such a platform which is easy to learn and comprehensive to develop conversation actions. It is a good example of the simplistic approach to solving complex man to machine communication problem using natural language processing in proximity to machine learning. Api.ai supports context based conversations now, which reduces the overhead of handling user context in session parameters. On the other hand in Lex this has to be handled in session. Also, api.ai can be used for both voice and text based conversations (assistant actions can be easily created using api.ai).

5

Category: AI and machine learning
Description:Computer Vision: Extract information from images to categorize and process visual data.
References:
[AWS]:Amazon Rekognition (based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily. It requires no machine learning expertise to use.)
[Azure]:Cognitive Services(bring AI within reach of every developer—without requiring machine-learning expertise.)
[Google]:Google Vision (offers powerful pre-trained machine learning models through REST and RPC APIs.)
Tags: AmazonRekognition, #GoogleVision, #AzureSpeech
Differences: For now, only Google Cloud Vision supports batch processing. Videos are not natively supported by Google Cloud Vision or Amazon Rekognition. The Object Detection functionality of Google Cloud Vision and Amazon Rekognition is almost identical, both syntactically and semantically.
Differences:
Google Cloud Vision and Amazon Rekognition offer a broad spectrum of solutions, some of which are comparable in terms of functional details, quality, performance, and costs.

7

Category: Big data and analytics: Data warehouse
Description: Apache Spark-based analytics platform. Managed Hadoop service. Data orchestration, ETL, Analytics and visualization
References:
[AWS]:EMR, Data Pipeline, Kinesis Stream, Kinesis Firehose, Glue, QuickSight, Athena, CloudSearch
[Azure]:Azure Databricks, Data Catalog Cortana Intelligence, HDInsight, Power BI, Azure Datafactory, Azure Search, Azure Data Lake Anlytics, Stream Analytics, Azure Machine Learning
[Google]:Cloud DataProc, Machine Learning, Cloud Datalab
Tags:#EMR, #DataPipeline, #Kinesis, #Cortana, AzureDatafactory, #AzureDataAnlytics, #CloudDataProc, #MachineLearning, #CloudDatalab
Differences: All three providers offer similar building blocks; data processing, data orchestration, streaming analytics, machine learning and visualisations. AWS certainly has all the bases covered with a solid set of products that will meet most needs. Azure offers a comprehensive and impressive suite of managed analytical products. They support open source big data solutions alongside new serverless analytical products such as Data Lake. Google provide their own twist to cloud analytics with their range of services. With Dataproc and Dataflow, Google have a strong core to their proposition. Tensorflow has been getting a lot of attention recently and there will be many who will be keen to see Machine Learning come out of preview.

8

Category: Virtual servers
Description:Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes.
Batch: Run large-scale parallel and high-performance computing applications efficiently in the cloud.
References:
[AWS]:Elastic Compute Cloud (EC2), Amazon Bracket(Explore and experiment with quantum computing), Amazon Ec2 M6g Instances (Achieve up to 40% better price performance), Amazon Ec2 Inf1 instancs (Deliver cost-effective ML inference), AWS Graviton2 Processors (Optimize price performance for cloud workloads), AWS Batch, AWS AutoScaling, VMware Cloud on AWS, AWS Local Zones (Run low latency applications at the edge), AWS Wavelength (Deliver ultra-low latency applications for 5G devices), AWS Nitro Enclaves (Further protect highly sensitive data), AWS Outposts (Run AWS infrastructure and services on-premises)
[Azure]:Azure Virtual Machines, Azure Batch, Virtual Machine Scale Sets, Azure VMware by CloudSimple
[Google]:Compute Engine, Preemptible Virtual Machines, Managed instance groups (MIGs), Google Cloud VMware Solution by CloudSimple
Tags: #AWSEC2, #AWSBatch, #AWSAutoscaling, #AzureVirtualMachine, #AzureBatch, #VirtualMachineScaleSets, #AzureVMWare, #ComputeEngine, #MIGS, #VMWare
Differences: There is very little to choose between the 3 providers when it comes to virtual servers. Amazon has some impressive high end kit, on the face of it this sound like it would make AWS a clear winner. However, if your only option is to choose the biggest box available you will need to make sure you have very deep pockets, and perhaps your money may be better spent re-architecting your apps for horizontal scale.Azure’s remains very strong in the PaaS space and now has a IaaS that can genuinely compete with AWS
Google offers a simple and very capable set of services that are easy to understand. However, with availability in only 5 regions it does not have the coverage of the other players.

9

Category: Containers and container orchestrators
Description: A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.
Container orchestration is all about managing the lifecycles of containers, especially in large, dynamic environments.
References:
[AWS]:EC2 Container Service (ECS), Fargate(Run containers without anaging servers or clusters), EC2 Container Registry(managed AWS Docker registry service that is secure, scalable, and reliable.), Elastic Container Service for Kubernetes (EKS: runs the Kubernetes management infrastructure across multiple AWS Availability Zones), App Mesh( application-level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure)
[Azure]:Azure Container Instances, Azure Container Registry, Azure Kubernetes Service (AKS), Service Fabric Mesh
[Google]:Google Container Engine, Container Registry, Kubernetes Engine
Tags:#ECS, #Fargate, #EKS, #AppMesh, #ContainerEngine, #ContainerRegistry, #AKS
Differences: Google Container Engine, AWS Container Services, and Azure Container Instances can be used to run docker containers. Google offers a simple and very capable set of services that are easy to understand. However, with availability in only 5 regions it does not have the coverage of the other players.

10

Category: Serverless
Description: Integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.
References:
[AWS]:AWS Lambda
[Azure]:Azure Functions
[Google]:Google Cloud Functions
Tags:#AWSLAmbda, #AzureFunctions, #GoogleCloudFunctions
Differences: Both AWS Lambda and Microsoft Azure Functions and Google Cloud Functions offer dynamic, configurable triggers that you can use to invoke your functions on their platforms. AWS Lambda, Azure and Google Cloud Functions support Node.js, Python, and C#. The beauty of serverless development is that, with minor changes, the code you write for one service should be portable to another with little effort – simply modify some interfaces, handle any input/output transforms, and an AWS Lambda Node.JS function is indistinguishable from a Microsoft Azure Node.js Function. AWS Lambda provides further support for Python and Java, while Azure Functions provides support for F# and PHP. AWS Lambda is built from the AMI, which runs on Linux, while Microsoft Azure Functions run in a Windows environment. AWS Lambda uses the AWS Machine architecture to reduce the scope of containerization, letting you spin up and tear down individual pieces of functionality in your application at will.

11

Category: Relational databases
Description: Managed relational database service where resiliency, scale, and maintenance are primarily handled by the platform.
References:
[AWS]:AWS RDS(MySQL and PostgreSQL-compatible relational database built for the cloud,), Aurora(MySQL and PostgreSQL-compatible relational database built for the cloud)
[Azure]:SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL
[Google]:Cloud SQL
Tags: #AWSRDS, #AWSAUrora, #AzureSQlDatabase, #AzureDatabaseforMySQL, #GoogleCloudSQL
Differences: All three providers boast impressive relational database offering. RDS supports an impressive range of managed relational stores while Azure SQL Database is probably the most advanced managed relational database available today. Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.

12

Category: NoSQL, Document Databases
Description:A globally distributed, multi-model database that natively supports multiple data models: key-value, documents, graphs, and columnar.
References:
[AWS]:DynamoDB (key-value and document database that delivers single-digit millisecond performance at any scale.), SimpleDB ( a simple web services interface to create and store multiple data sets, query your data easily, and return the results.), Managed Cassandra Services(MCS)
[Azure]:Table Storage, DocumentDB, Azure Cosmos DB
[Google]:Cloud Datastore (handles sharding and replication in order to provide you with a highly available and consistent database. )
Tags:#AWSDynamoDB, #SimpleDB, #TableSTorage, #DocumentDB, AzureCosmosDB, #GoogleCloudDataStore
Differences:DynamoDB and Cloud Datastore are based on the document store database model and are therefore similar in nature to open-source solutions MongoDB and CouchDB. In other words, each database is fundamentally a key-value store. With more workloads moving to the cloud the need for NoSQL databases will become ever more important, and again all providers have a good range of options to satisfy most performance/cost requirements. Of all the NoSQL products on offer it’s hard not to be impressed by DocumentDB; Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.

13

Category:Caching
Description:An in-memory–based, distributed caching service that provides a high-performance store typically used to offload non transactional work from a database.
References:
[AWS]:AWS ElastiCache (works as an in-memory data store and cache to support the most demanding applications requiring sub-millisecond response times.)
[Azure]:Azure Cache for Redis (based on the popular software Redis. It is typically used as a cache to improve the performance and scalability of systems that rely heavily on backend data-stores.)
[Google]:Memcache (In-memory key-value store, originally intended for caching)
Tags:#Redis, #Memcached
<Differences: They all support horizontal scaling via sharding.They all improve the performance of web applications by allowing you to retrive information from fast, in-memory caches, instead of relying on slower disk-based databases.”, “Differences”: “ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn’t— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it’ll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys. ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn’t— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it’ll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys.

14

Category: Security, identity, and access
Description:Authentication and authorization: Allows users to securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.
References:
[AWS]:Identity and Access Management (IAM), AWS Organizations, Multi-Factor Authentication, AWS Directory Service, Cognito(provides solutions to control access to backend resources from your app), Amazon Detective (Investigate potential security issues), AWS IAM Access Analyzer(Easily analyze resource accessibility)
[Azure]:Azure Active Directory, Azure Subscription Management + Azure RBAC, Multi-Factor Authentication, Azure Active Directory Domain Services, Azure Active Directory B2C, Azure Policy, Management Groups
[Google]:Cloud Identity, Identity Platform, Cloud IAM, Policy Intelligence, Cloud Resource Manager, Cloud Identity-Aware Proxy, Context-aware accessManaged Service for Microsoft Active Directory, Security key enforcement, Titan Security Key
Tags: #IAM, #AWSIAM, #AzureIAM, #GoogleIAM, #Multi-factorAuthentication
Differences: One unique thing about AWS IAM is that accounts created in the organization (not through federation) can only be used within that organization. This contrasts with Google and Microsoft. On the good side, every organization is self-contained. On the bad side, users can end up with multiple sets of credentials they need to manage to access different organizations. The second unique element is that every user can have a non-interactive account by creating and using access keys, an interactive account by enabling console access, or both. (Side note: To use the CLI, you need to have access keys generated.)

15

Category: Object Storage and Content delivery
Description:Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
References:
[AWS]:Simple Storage Services (S3), Import/Export(used to move large amounts of data into and out of the Amazon Web Services public cloud using portable storage devices for transport.), Snowball( petabyte-scale data transport solution that uses devices designed to be secure to transfer large amounts of data into and out of the AWS Cloud), CloudFront( content delivery network (CDN) is massively scaled and globally distributed), Elastic Block Store (EBS: high performance block storage service), Elastic File System(shared, elastic file storage system that grows and shrinks as you add and remove files.), S3 Infrequent Access (IA: is for data that is accessed less frequently, but requires rapid access when needed. ), S3 Glacier( long-term storage of data that is infrequently accessed and for which retrieval latency times of 3 to 5 hours are acceptable.), AWS Backup( makes it easy to centralize and automate the back up of data across AWS services in the cloud as well as on-premises using the AWS Storage Gateway.), Storage Gateway(hybrid cloud storage service that gives you on-premises access to virtually unlimited cloud storage), AWS Import/Export Disk(accelerates moving large amounts of data into and out of AWS using portable storage devices for transport)
[Azure]:
Azure Blob storage, File Storage, Data Lake Store, Azure Backup, Azure managed disks, Azure Files, Azure Storage cool tier, Azure Storage archive access tier, Azure Backup, StorSimple, Import/Export
[Google]:
Cloud Storage, GlusterFS, CloudCDN
Tags:#S3, #AzureBlobStorage, #CloudStorage
Differences:
Source: All providers have good object storage options and so storage alone is unlikely to be a deciding factor when choosing a cloud provider. The exception perhaps is for hybrid scenarios, in this case Azure and AWS clearly win. AWS and Google’s support for automatic versioning is a great feature that is currently missing from Azure; however Microsoft’s fully managed Data Lake Store offers an additional option that will appeal to organisations who are looking to run large scale analytical workloads. If you are prepared to wait 4 hours for your data and you have considerable amounts of the stuff then AWS Glacier storage might be a good option. If you use the common programming patterns for atomic updates and consistency, such as etags and the if-match family of headers, then you should be aware that AWS does not support them, though Google and Azure do. Azure also supports blob leasing, which can be used to provide a distributed lock.

16

Category:Internet of things (IoT)
Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.
References:
[AWS]:AWS IoT (Internet of Things), AWS Greengrass, Kinesis Firehose, Kinesis Streams, AWS IoT Things Graph
[Azure]:Azure IoT Hub, Azure IoT Edge, Event Hubs, Azure Digital Twins, Azure Sphere
[Google]:Google Cloud IoT Core, Firebase, Brillo, Weave, CLoud Pub/SUb, Stream Analysis, Big Query, Big Query Streaming API
Tags:#IoT, #InternetOfThings, #Firebase
Differences:AWS and Azure have a more coherent message with their products clearly integrated into their respective platforms, whereas Google Firebase feels like a distinctly separate product.

17

Category:Web Applications
Description:Managed hosting platform providing easy to use services for deploying and scaling web applications and services. API Gateway is a a turnkey solution for publishing APIs to external and internal consumers. Cloudfront is a global content delivery network that delivers audio, video, applications, images, and other files.
References:
[AWS]:Elastic Beanstalk (for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS), AWS Wavelength (for delivering ultra-low latency applications for 5G), API Gateway (makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.), CloudFront (web service that speeds up distribution of your static and dynamic web content, such as .html, .css, .js, and image files, to your users. CloudFront delivers your content through a worldwide network of data centers called edge locations.),Global Accelerator ( improves the availability and performance of your applications with local or global users. It provides static IP addresses that act as a fixed entry point to your application endpoints in a single or multiple AWS Regions, such as your Application Load Balancers, Network Load Balancers or Amazon EC2 instances.)AWS AppSync (simplifies application development by letting you create a flexible API to securely access, manipulate, and combine data from one or more data sources: GraphQL service with real-time data synchronization and offline programming features. )
[Azure]:App Service, API Management, Azure Content Delivery Network, Azure Content Delivery Network
[Google]:App Engine, Cloud API, Cloud Enpoint, APIGee
Tags: #AWSElasticBeanstalk, #AzureAppService, #GoogleAppEngine, #CloudEnpoint, #CloudFront, #APIgee
Differences: With AWS Elastic Beanstalk, developers retain full control over the AWS resources powering their application. If developers decide they want to manage some (or all) of the elements of their infrastructure, they can do so seamlessly by using Elastic Beanstalk’s management capabilities. AWS Elastic Beanstalk integrates with more apps than Google App Engines (Datadog, Jenkins, Docker, Slack, Github, Eclipse, etc..). Google App Engine has more features than AWS Elastic BEanstalk (App Identity, Java runtime, Datastore, Blobstore, Images, Go Runtime, etc..). Developers describe Amazon API Gateway as “Create, publish, maintain, monitor, and secure APIs at any scale”. Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. On the other hand, Google Cloud Endpoints is detailed as “Develop, deploy and manage APIs on any Google Cloud backend”. An NGINX-based proxy and distributed architecture give unparalleled performance and scalability. Using an Open API Specification or one of our API frameworks, Cloud Endpoints gives you the tools you need for every phase of API development and provides insight with Google Cloud Monitoring, Cloud Trace, Google Cloud Logging and Cloud Trace.

18

Category:Encryption
Description:Helps you protect and safeguard your data and meet your organizational security and compliance commitments.
References:
[AWS]:Key Management Service AWS KMS, CloudHSM
[Azure]:Key Vault
[Google]:Encryption By Default at Rest, Cloud KMS
Tags:#AWSKMS, #Encryption, #CloudHSM, #EncryptionAtRest, #CloudKMS
Differences: AWS KMS, is an ideal solution for organizations that want to manage encryption keys in conjunction with other AWS services. In contrast to AWS CloudHSM, AWS KMS provides a complete set of tools to manage encryption keys, develop applications and integrate with other AWS services. Google and Azure offer 4096 RSA. AWS and Google offer 256 bit AES. With AWs, you can bring your own key

19

Category:Internet of things (IoT)
Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.
References:
[AWS]:AWS IoT, AWS Greengrass, Kinesis Firehose ( captures and loads streaming data in storage and business intelligence (BI) tools to enable near real-time analytics in the AWS cloud), Kinesis Streams (for rapid and continuous data intake and aggregation.), AWS IoT Things Graph (makes it easy to visually connect different devices and web services to build IoT applications.)
[Azure]:Azure IoT Hub, Azure IoT Edge, Event Hubs, Azure Digital Twins, Azure Sphere
[Google]:Google Cloud IoT Core, Firebase, Brillo, Weave, CLoud Pub/SUb, Stream Analysis, Big Query, Big Query Streaming API
Tags:#IoT, #InternetOfThings, #Firebase
Differences:AWS and Azure have a more coherent message with their products clearly integrated into their respective platforms, whereas Google Firebase feels like a distinctly separate product.

20

Category:Object Storage and Content delivery
Description: Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
References:
[AWS]:Simple Storage Services (S3), Import/Export Snowball, CloudFront, Elastic Block Store (EBS), Elastic File System, S3 Infrequent Access (IA), S3 Glacier, AWS Backup, Storage Gateway, AWS Import/Export Disk, Amazon S3 Access Points(Easily manage access for shared data)
[Azure]:Azure Blob storage, File Storage, Data Lake Store, Azure Backup, Azure managed disks, Azure Files, Azure Storage cool tier, Azure Storage archive access tier, Azure Backup, StorSimple, Import/Export
[Google]:Cloud Storage, GlusterFS, CloudCDN
Tags:#S3, #AzureBlobStorage, #CloudStorage
Differences:All providers have good object storage options and so storage alone is unlikely to be a deciding factor when choosing a cloud provider. The exception perhaps is for hybrid scenarios, in this case Azure and AWS clearly win. AWS and Google’s support for automatic versioning is a great feature that is currently missing from Azure; however Microsoft’s fully managed Data Lake Store offers an additional option that will appeal to organisations who are looking to run large scale analytical workloads. If you are prepared to wait 4 hours for your data and you have considerable amounts of the stuff then AWS Glacier storage might be a good option. If you use the common programming patterns for atomic updates and consistency, such as etags and the if-match family of headers, then you should be aware that AWS does not support them, though Google and Azure do. Azure also supports blob leasing, which can be used to provide a distributed lock.

21

Category: Backend process logic
Description: Cloud technology to build distributed applications using out-of-the-box connectors to reduce integration challenges. Connect apps, data and devices on-premises or in the cloud.
References:
[AWS]:AWS Step Functions ( lets you build visual workflows that enable fast translation of business requirements into technical requirements. You can build applications in a matter of minutes, and when needs change, you can swap or reorganize components without customizing any code.)
[Azure]:Logic Apps (cloud service that helps you schedule, automate, and orchestrate tasks, business processes, and workflows when you need to integrate apps, data, systems, and services across enterprises or organizations.)
[Google]:Dataflow ( fully managed service for executing Apache Beam pipelines within the Google Cloud Platform ecosystem.)
Tags:#AWSStepFunctions, #LogicApps, #Dataflow
Differences: AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly. AWS Step Functions belongs to \”Cloud Task Management\” category of the tech stack, while Google Cloud Dataflow can be primarily classified under \”Real-time Data Processing\”. According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to AWS Step Functions, which is listed in 19 company stacks and 7 developer stacks.

22

Category: Enterprise application services
Description:Fully integrated Cloud service providing communications, email, document management in the cloud and available on a wide variety of devices.
References:
[AWS]:Amazon WorkMail, Amazon WorkDocs, Amazon Kendra (Sync and Index)
[Azure]:Office 365
[Google]:G Suite
Tags: #AmazonWorkDocs, #Office365, #GoogleGSuite
Differences: G suite document processing applications like Google Docs are far behind Office 365 popular Word and Excel software, but G Suite User interface is intuite, simple and easy to navigate. Office 365 is too clunky. Get 20% off G-Suite Business Plan with Promo Code: PCQ49CJYK7EATNC

23

Category: Networking
Description: Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.
References:
[AWS]:Virtual Private Cloud (VPC), Cloud virtual networking, Subnets, Elastic Network Interface (ENI), Route Tables, Network ACL, Secutity Groups, Internet Gateway, NAT Gateway, AWS VPN Gateway, AWS Route 53, AWS Direct Connect, AWS Network Load Balancer, VPN CloudHub, AWS Local Zones, AWS Transit Gateway network manager (Centrally manage global networks)
[Azure]:Virtual Network(provide services for building networks within Azure.),Subnets (network resources can be grouped by subnet for organisation and security.), Network Interface (Each virtual machine can be assigned one or more network interfaces (NICs)), Network Security Groups (NSG: contains a set of prioritised ACL rules that explicitly grant or deny access), Azure VPN Gateway ( allows connectivity to on-premise networks), Azure DNS, Traffic Manager (DNS based traffic routing solution.), ExpressRoute (provides connections up to 10 Gbps to Azure services over a dedicated fibre connection), Azure Load Balancer, Network Peering, Azure Stack (Azure Stack allows organisations to use Azure services running in private data centers.), Azure Load Balancer , Azure Log Analytics, Azure DNS,
[Google]:Cloud Virtual Network, Subnets, Network Interface, Protocol fowarding, Cloud VPN, Cloud DNS, Virtual Private Network, Cloud Interconnect, CDN interconnect, Cloud DNS, Stackdriver, Google Cloud Load Balancing,
Tags:#VPC, #Subnets, #ACL, #VPNGateway, #CloudVPN, #NetworkInterface, #ENI, #RouteTables, #NSG, #NetworkACL, #InternetGateway, #NatGateway, #ExpressRoute, #CloudInterConnect, #StackDriver
Differences: Subnets group related resources, however, unlike AWS and Azure, Google do not constrain the private IP address ranges of subnets to the address space of the parent network. Like Azure, Google has a built in internet gateway that can be specified from routing rules.

24

Category: Management
Description: A unified management console that simplifies building, deploying, and operating your cloud resources.
References:
[AWS]: AWS Management Console, Trusted Advisor, AWS Usage and Billing Report, AWS Application Discovery Service, Amazon EC2 Systems Manager, AWS Personal Health Dashboard, AWS Compute Optimizer (Identify optimal AWS Compute resources)
[Azure]:Azure portal, Azure Advisor, Azure Billing API, Azure Migrate, Azure Monitor, Azure Resource Health
[Google]:Google CLoud Platform, Cost Management, Security Command Center, StackDriver
Tags: #AWSConsole, #AzurePortal, #GoogleCloudConsole, #TrustedAdvisor, #AzureMonitor, #SecurityCommandCenter
Differences: AWS Console categorizes its Infrastructure as a Service offerings into Compute, Storage and Content Delivery Network (CDN), Database, and Networking to help businesses and individuals grow. Azure excels in the Hybrid Cloud space allowing companies to integrate onsite servers with cloud offerings. Google has a strong offering in containers, since Google developed the Kubernetes standard that AWS and Azure now offer. GCP specializes in high compute offerings like Big Data, analytics and machine learning. It also offers considerable scale and load balancing – Google knows data centers and fast response time.

25

Category: DevOps and application monitoring
Description: Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments; Cloud services for collaborating on code development; Collection of tools for building, debugging, deploying, diagnosing, and managing multiplatform scalable apps and services; Fully managed build service that supports continuous integration and deployment.
References:
[AWS]:AWS CodePipeline(orchestrates workflow for continuous integration, continuous delivery, and continuous deployment), AWS CloudWatch (monitor your AWS resources and the applications you run on AWS in real time. ), AWS X-Ray (application performance management service that enables a developer to analyze and debug applications in aws), AWS CodeDeploy (automates code deployments to Elastic Compute Cloud (EC2) and on-premises servers. ), AWS CodeCommit ( source code storage and version-control service), AWS Developer Tools, AWS CodeBuild (continuous integration service that compiles source code, runs tests, and produces software packages that are ready to deploy. ), AWS Command Line Interface (unified tool to manage your AWS services), AWS OpsWorks (Chef-based), AWS CloudFormation ( provides a common language for you to describe and provision all the infrastructure resources in your cloud environment.), Amazon CodeGuru (for automated code reviews and application performance recommendations)
[Azure]:Azure Monitor, Azure DevOps, Azure Developer Tools, Azure CLI Azure PowerShell, Azure Automation, Azure Resource Manager , VM extensions , Azure Automation
[Google]:DevOps Solutions (Infrastructure as code, Configuration management, Secrets management, Serverless computing, Continuous delivery, Continuous integration , Stackdriver (combines metrics, logs, and metadata from all of your cloud accounts and projects into a single comprehensive view of your environment)
Tags: #CloudWatch, #StackDriver, #AzureMonitor, #AWSXray, #AWSCodeDeploy, #AzureDevOps, #GoogleDevopsSolutions
Differences: CodeCommit eliminates the need to operate your own source control system or worry about scaling its infrastructure. Azure DevOps provides unlimited private Git hosting, cloud build for continuous integration, agile planning, and release management for continuous delivery to the cloud and on-premises. Includes broad IDE support.

SageMakerAzure Machine Learning Studio

A collaborative, drag-and-drop tool to build, test, and deploy predictive analytics solutions on your data.

Alexa Skills KitMicrosoft Bot Framework

Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular services.

Amazon LexSpeech Services

API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.

Amazon LexLanguage Understanding (LUIS)

Allows your applications to understand user commands contextually.

Amazon Polly, Amazon Transcribe | Azure Speech Services

Enables both Speech to Text, and Text into Speech capabilities.
The Speech Services are the unification of speech-to-text, text-to-speech, and speech-translation into a single Azure subscription. It’s easy to speech enable your applications, tools, and devices with the Speech SDK, Speech Devices SDK, or REST APIs.
Amazon Polly is a Text-to-Speech (TTS) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice. With dozens of lifelike voices across a variety of languages, you can select the ideal voice and build speech-enabled applications that work in many different countries.
Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to their applications. Using the Amazon Transcribe API, you can analyze audio files stored in Amazon S3 and have the service return a text file of the transcribed speech.

Amazon RekognitionCognitive Services

Computer Vision: Extract information from images to categorize and process visual data.
Amazon Rekognition is a simple and easy to use API that can quickly analyze any image or video file stored in Amazon S3. Amazon Rekognition is always learning from new data, and we are continually adding new labels and facial recognition features to the service.

Face: Detect, identy, and analyze faces in photos.

Emotions: Recognize emotions in images.

Alexa Skill SetAzure Virtual Assistant

The Virtual Assistant Template brings together a number of best practices we’ve identified through the building of conversational experiences and automates integration of components that we’ve found to be highly beneficial to Bot Framework developers.

Big data and analytics

Data warehouse

AWS RedshiftSQL Data Warehouse

Cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.

Big data processing
EMR | Azure Databricks
Apache Spark-based analytics platform.

EMR HDInsight

Managed Hadoop service. Deploy and manage Hadoop clusters in Azure.

Data orchestration / ETL

AWS Data Pipeline, AWS Glue | Data Factory

Processes and moves data between different compute and storage services, as well as on-premises data sources at specified intervals. Create, schedule, orchestrate, and manage data pipelines.

AWS GlueData Catalog

A fully managed service that serves as a system of registration and system of discovery for enterprise data sources

Analytics and visualization

AWS Kinesis Analytics | Stream Analytics

Data Lake Analytics | Data Lake Store

Storage and analysis platforms that create insights from large quantities of data, or data that originates from many sources.

QuickSightPower BI

Business intelligence tools that build visualizations, perform ad hoc analysis, and develop business insights from data.

CloudSearchAzure Search

Delivers full-text search and related search analytics and capabilities.

Amazon AthenaAzure Data Lake Analytics

Provides a serverless interactive query service that uses standard SQL for analyzing databases.

Compute

Virtual servers

Elastic Compute Cloud (EC2)Azure Virtual Machines

Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes.

AWS BatchAzure Batch

Run large-scale parallel and high-performance computing applications efficiently in the cloud.

AWS Auto ScalingVirtual Machine Scale Sets

Allows you to automatically change the number of VM instances. You set defined metric and thresholds that determine if the platform adds or removes instances.

VMware Cloud on AWSAzure VMware by CloudSimple

Redeploy and extend your VMware-based enterprise workloads to Azure with Azure VMware Solution by CloudSimple. Keep using the VMware tools you already know to manage workloads on Azure without disrupting network, security, or data protection policies.

Containers and container orchestrators

EC2 Container Service (ECS), FargateAzure Container Instances

Azure Container Instances is the fastest and simplest way to run a container in Azure, without having to provision any virtual machines or adopt a higher-level orchestration service.

EC2 Container RegistryAzure Container Registry

Allows customers to store Docker formatted images. Used to create all types of container deployments on Azure.

Elastic Container Service for Kubernetes (EKS)Azure Kubernetes Service (AKS)

Deploy orchestrated containerized applications with Kubernetes. Simplify monitoring and cluster management through auto upgrades and a built-in operations console.

App MeshService Fabric Mesh

Fully managed service that enables developers to deploy microservices applications without managing virtual machines, storage, or networking.
AWS App Mesh is a service mesh that provides application-level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure. App Mesh standardizes how your services communicate, giving you end-to-end visibility and ensuring high-availability for your applications.

Serverless

AWS Lambda | Azure Functions

Integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.
AWS Lambda is an event-driven, serverless computing platform provided by Amazon as a part of the Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code

Database

Relational database

AWS RDS | SQL Database Azure Database for MySQL Azure Database for PostgreSQL

Managed relational database service where resiliency, scale, and maintenance are primarily handled by the platform.
Amazon Relational Database Service is a distributed relational database service by Amazon Web Services. It is a web service running “in the cloud” designed to simplify the setup, operation, and scaling of a relational database for use in applications. Administration processes like patching the database software, backing up databases and enabling point-in-time recovery are managed automatically. Scaling storage and compute resources can be performed by a single API call as AWS does not offer an ssh connection to RDS instances.

NoSQL / Document

DynamoDB and SimpleDBAzure Cosmos DB

A globally distributed, multi-model database that natively supports multiple data models: key-value, documents, graphs, and columnar.

Caching

AWS ElastiCache | Azure Cache for Redis

An in-memory–based, distributed caching service that provides a high-performance store typically used to offload non transactional work from a database.
Amazon ElastiCache is a fully managed in-memory data store and cache service by Amazon Web Services. The service improves the performance of web applications by retrieving information from managed in-memory caches, instead of relying entirely on slower disk-based databases. ElastiCache supports two open-source in-memory caching engines: Memcached and Redis.

Database migration

AWS Database Migration ServiceAzure Database Migration Service

Migration of database schema and data from one database format to a specific database technology in the cloud.
AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. The AWS Database Migration Service can migrate your data to and from most widely used commercial and open-source databases.

DevOps and application monitoring

AWS CloudWatch, AWS X-Ray | Azure Monitor

Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.
Amazon CloudWatch is a monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. CloudWatch provides you with data and actionable insights to monitor your applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services that run on AWS and on-premises servers.
AWS X-Ray is an application performance management service that enables a developer to analyze and debug applications in the Amazon Web Services (AWS) public cloud. A developer can use AWS X-Ray to visualize how a distributed application is performing during development or production, and across multiple AWS regions and accounts.

AWS CodeDeploy, AWS CodeCommit, AWS CodePipeline | Azure DevOps

A cloud service for collaborating on code development.
AWS CodeDeploy is a fully managed deployment service that automates software deployments to a variety of compute services such as Amazon EC2, AWS Fargate, AWS Lambda, and your on-premises servers. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications.
AWS CodePipeline is a fully managed continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updates. CodePipeline automates the build, test, and deploy phases of your release process every time there is a code change, based on the release model you define.
AWS CodeCommit is a source code storage and version-control service for Amazon Web Services’ public cloud customers. CodeCommit was designed to help IT teams collaborate on software development, including continuous integration and application delivery.

AWS Developer ToolsAzure Developer Tools

Collection of tools for building, debugging, deploying, diagnosing, and managing multiplatform scalable apps and services.
The AWS Developer Tools are designed to help you build software like Amazon. They facilitate practices such as continuous delivery and infrastructure as code for serverless, containers, and Amazon EC2.

AWS CodeBuild | Azure DevOps

Fully managed build service that supports continuous integration and deployment.

AWS Command Line Interface | Azure CLI Azure PowerShell

Built on top of the native REST API across all cloud services, various programming language-specific wrappers provide easier ways to create solutions.
The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services. With just one tool to download and configure, you can control multiple AWS services from the command line and automate them through scripts.

AWS OpsWorks (Chef-based)Azure Automation

Configures and operates applications of all shapes and sizes, and provides templates to create and manage a collection of resources.
AWS OpsWorks is a configuration management service that provides managed instances of Chef and Puppet. Chef and Puppet are automation platforms that allow you to use code to automate the configurations of your servers.

AWS CloudFormation | Azure Resource Manager , VM extensions , Azure Automation

Provides a way for users to automate the manual, long-running, error-prone, and frequently repeated IT tasks.
AWS CloudFormation provides a common language for you to describe and provision all the infrastructure resources in your cloud environment. CloudFormation allows you to use a simple text file to model and provision, in an automated and secure manner, all the resources needed for your applications across all regions and accounts.

Networking

Area

Cloud virtual networking, Virtual Private Cloud (VPC) | Virtual Network

Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.

Cross-premises connectivity

AWS VPN Gateway | Azure VPN Gateway

Connects Azure virtual networks to other Azure virtual networks, or customer on-premises networks (Site To Site). Allows end users to connect to Azure services through VPN tunneling (Point To Site).

DNS management

AWS Route 53 | Azure DNS

Manage your DNS records using the same credentials and billing and support contract as your other Azure services

Route 53 | Traffic Manager

A service that hosts domain names, plus routes users to Internet applications, connects user requests to datacenters, manages traffic to apps, and improves app availability with automatic failover.

Dedicated network

AWS Direct Connect | ExpressRoute

Establishes a dedicated, private network connection from a location to the cloud provider (not over the Internet).

Load balancing

AWS Network Load Balancer | Azure Load Balancer

Azure Load Balancer load-balances traffic at layer 4 (TCP or UDP).

Application Load Balancer | Application Gateway

Application Gateway is a layer 7 load balancer. It supports SSL termination, cookie-based session affinity, and round robin for load-balancing traffic.

Internet of things (IoT)

AWS IoT | Azure IoT Hub

A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale.

AWS Greengrass | Azure IoT Edge

Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.

Kinesis Firehose, Kinesis Streams | Event Hubs

Services that allow the mass ingestion of small data inputs, typically from devices and sensors, to process and route the data.

AWS IoT Things Graph | Azure Digital Twins

Azure Digital Twins is an IoT service that helps you create comprehensive models of physical environments. Create spatial intelligence graphs to model the relationships and interactions between people, places, and devices. Query data from a physical space rather than disparate sensors.

Management

Trusted Advisor | Azure Advisor

Provides analysis of cloud resource configuration and security so subscribers can ensure they’re making use of best practices and optimum configurations.

AWS Usage and Billing Report | Azure Billing API

Services to help generate, monitor, forecast, and share billing data for resource usage by time, organization, or product resources.

AWS Management Console | Azure portal

A unified management console that simplifies building, deploying, and operating your cloud resources.

AWS Application Discovery Service | Azure Migrate

Assesses on-premises workloads for migration to Azure, performs performance-based sizing, and provides cost estimations.

Amazon EC2 Systems Manager | Azure Monitor

Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.

AWS Personal Health Dashboard | Azure Resource Health

Provides detailed information about the health of resources as well as recommended actions for maintaining resource health.

Security, identity, and access

Authentication and authorization

Identity and Access Management (IAM) | Azure Active Directory

Allows users to securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.

Identity and Access Management (IAM) | Azure Role Based Access Control

Role-based access control (RBAC) helps you manage who has access to Azure resources, what they can do with those resources, and what areas they have access to.

AWS Organizations | Azure Subscription Management + Azure RBAC

Security policy and role management for working with multiple accounts.

Multi-Factor Authentication | Multi-Factor Authentication

Safeguard access to data and applications while meeting user demand for a simple sign-in process.

AWS Directory Service | Azure Active Directory Domain Services

Provides managed domain services such as domain join, group policy, LDAP, and Kerberos/NTLM authentication that are fully compatible with Windows Server Active Directory.

Cognito | Azure Active Directory B2C

A highly available, global, identity management service for consumer-facing applications that scales to hundreds of millions of identities.

AWS Organizations | Azure Policy

Azure Policy is a service in Azure that you use to create, assign, and manage policies. These policies enforce different rules and effects over your resources, so those resources stay compliant with your corporate standards and service level agreements.

AWS Organizations | Management Groups

Azure management groups provide a level of scope above subscriptions. You organize subscriptions into containers called “management groups” and apply your governance conditions to the management groups. All subscriptions within a management group automatically inherit the conditions applied to the management group. Management groups give you enterprise-grade management at a large scale, no matter what type of subscriptions you have.

Encryption

Server-side encryption with Amazon S3 Key Management Service | Azure Storage Service Encryption

Helps you protect and safeguard your data and meet your organizational security and compliance commitments.

Key Management Service AWS KMS, CloudHSM | Key Vault

Provides security solution and works with other services by providing a way to manage, create, and control encryption keys stored in hardware security modules (HSM).

Firewall

Web Application Firewall | Application Gateway – Web Application Firewall

A firewall that protects web applications from common web exploits.

Web Application Firewall | Azure Firewall

Provides inbound protection for non-HTTP/S protocols, outbound network-level protection for all ports and protocols, and application-level protection for outbound HTTP/S.

Security

Inspector | Security Center

An automated security assessment service that improves the security and compliance of applications. Automatically assess applications for vulnerabilities or deviations from best practices.

Certificate Manager | App Service Certificates available on the Portal

Service that allows customers to create, manage, and consume certificates seamlessly in the cloud.

GuardDuty | Azure Advanced Threat Protection

Detect and investigate advanced attacks on-premises and in the cloud.

AWS Artifact | Service Trust Portal

Provides access to audit reports, compliance guides, and trust documents from across cloud services.

AWS Shield | Azure DDos Protection Service

Provides cloud services with protection from distributed denial of services (DDoS) attacks.

Storage

Object storage

Simple Storage Services (S3) | Azure Blob storage

Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.

Virtual server disks

Elastic Block Store (EBS) | Azure managed disks

SSD storage optimized for I/O intensive read/write operations. For use as high-performance Azure virtual machine storage.

Shared files

Elastic File System | Azure Files

Provides a simple interface to create and configure file systems quickly, and share common files. Can be used with traditional protocols that access files over a network.

Archiving and backup

S3 Infrequent Access (IA) | Azure Storage cool tier

Cool storage is a lower-cost tier for storing data that is infrequently accessed and long-lived.

S3 Glacier | Azure Storage archive access tier

Archive storage has the lowest storage cost and higher data retrieval costs compared to hot and cool storage.

AWS Backup | Azure Backup

Back up and recover files and folders from the cloud, and provide offsite protection against data loss.

Hybrid storage

Storage Gateway | StorSimple

Integrates on-premises IT environments with cloud storage. Automates data management and storage, plus supports disaster recovery.

Bulk data transfer

AWS Import/Export Disk | Import/Export

A data transport solution that uses secure disks and appliances to transfer large amounts of data. Also offers data protection during transit.

AWS Import/Export Snowball, Snowball Edge, Snowmobile | Azure Data Box

Petabyte- to exabyte-scale data transport solution that uses secure data storage devices to transfer large amounts of data to and from Azure.

Web applications

Elastic Beanstalk | App Service

Managed hosting platform providing easy to use services for deploying and scaling web applications and services.

API Gateway | API Management

A turnkey solution for publishing APIs to external and internal consumers.

CloudFront | Azure Content Delivery Network

A global content delivery network that delivers audio, video, applications, images, and other files.

Global Accelerator | Azure Front Door

Easily join your distributed microservice architectures into a single global application using HTTP load balancing and path-based routing rules. Automate turning up new regions and scale-out with API-driven global actions, and independent fault-tolerance to your back end microservices in Azure—or anywhere.

Miscellaneous

Backend process logic

AWS Step Functions | Logic Apps

Cloud technology to build distributed applications using out-of-the-box connectors to reduce integration challenges. Connect apps, data and devices on-premises or in the cloud.

Enterprise application services

Amazon WorkMail, Amazon WorkDocs | Office 365

Fully integrated Cloud service providing communications, email, document management in the cloud and available on a wide variety of devices.

Gaming

GameLift, GameSparks | PlayFab

Managed services for hosting dedicated game servers.

Media transcoding

Elastic Transcoder | Media Services

Services that offer broadcast-quality video streaming services, including various transcoding technologies.

Workflow

Simple Workflow Service (SWF) | Logic Apps

Serverless technology for connecting apps, data and devices anywhere, whether on-premises or in the cloud for large ecosystems of SaaS and cloud-based connectors.

Hybrid

Outposts | Azure Stack

Azure Stack is a hybrid cloud platform that enables you to run Azure services in your company’s or service provider’s datacenter. As a developer, you can build apps on Azure Stack. You can then deploy them to either Azure Stack or Azure, or you can build truly hybrid apps that take advantage of connectivity between an Azure Stack cloud and Azure.

How does a business decide between Microsoft Azure or AWS?

Basically, it all comes down to what your organizational needs are and if there’s a particular area that’s especially important to your business (ex. serverless, or integration with Microsoft applications).

Some of the main things it comes down to is compute options, pricing, and purchasing options.

Here’s a brief comparison of the compute option features across cloud providers:

Here’s an example of a few instances’ costs (all are Linux OS):

Each provider offers a variety of options to lower costs from the listed On-Demand prices. These can fall under reservations, spot and preemptible instances and contracts.

Both AWS and Azure offer a way for customers to purchase compute capacity in advance in exchange for a discount: AWS Reserved Instances and Azure Reserved Virtual Machine Instances. There are a few interesting variations between the instances across the cloud providers which could affect which is more appealing to a business.

Another discounting mechanism is the idea of spot instances in AWS and low-priority VMs in Azure. These options allow users to purchase unused capacity for a steep discount.

With AWS and Azure, enterprise contracts are available. These are typically aimed at enterprise customers, and encourage large companies to commit to specific levels of usage and spend in exchange for an across-the-board discount – for example, AWS EDPs and Azure Enterprise Agreements.

You can read more about the differences between AWS and Azure to help decide which your business should use in this blog post

Source: AWS to Azure services comparison – Azure Architecture