It doesn’t. First, a database is a collection of related data, so I assume you mean DBMS or database language.
Second, pagination is generally a function of the front-end and/or middleware, not the database layer.
But some database languages provide helpful facilities that aide in implementing pagination. For example, many SQL dialects provide LIMIT and OFFSET clauses that can be used to emit up to n rows starting at a given row number. I.e., a “page” of rows. If the query results are sorted via ORDER BY and are generally unchanged between successive invocations, then that can be used to implement pagination.
That may not be the most efficient or effective implementation, though.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)
On context of web apps , let’s say there are 100 mn users. One cannot dump all the users in response.
Cache database query results in the middleware layer using Redis or similar and serve out pages of rows from that.
What if you have 30, 000 rows plus, do you fetch all of that from the database and cache in Redis?
I feel the most efficient solution is still offset and limit. It doesn’t make sense to use a database and then end up putting all of your data in Redis especially data that changes a lot. Redis is not for storing all of your data.
If you have large data set, you should use offset and limit, getting only what is needed from the database into main memory (and maybe caching those in Redis) at any point in time is very efficient.
With 30,000 rows in a table, if offset/limit is the only viable or appropriate restriction, then that’s sometimes the way to go.
More often, there’s a much better way of restricting 30,000 rows via some search criteria that significantly reduces the displayed volume of rows — ideally to a single page or a few pages (which are appropriate to cache in Redis.)
It’s unlikely (though it does happen) that users really want to casually browse 30,000 rows, page by page. More often, they want this one record, or these small number of records.
I know for MySQL there is LIMIT offset,size; and for Oracle there is ‘ROW_NUMBER’ or something like that.
But when such ‘paginated’ queries are called back to back, does the database engine actually do the entire ‘select’ all over again and then retrieve a different subset of results each time? Or does it do the overall fetching of results only once, keeps the results in memory or something, and then serves subsets of results from it for subsequent queries based on offset and size?
If it does the full fetch every time, then it seems quite inefficient.
If it does full fetch only once, it must be ‘storing’ the query somewhere somehow, so that the next time that query comes in, it knows that it has already fetched all the data and just needs to extract next page from it. In that case, how will the database engine handle multiple threads? Two threads executing the same query?
something will be quick or slow without taking measurements, and complicate the code in advance to download 12 pages at once and cache them because “it seems to me that it will be faster”.
Answer: First of all, do not make assumptions in advance whether something will be quick or slow without taking measurements, and complicate the code in advance to download 12 pages at once and cache them because “it seems to me that it will be faster”.
YAGNI principle – the programmer should not add functionality until deemed necessary. Do it in the simplest way (ordinary pagination of one page), measure how it works on production, if it is slow, then try a different method, if the speed is satisfactory, leave it as it is.
From my own practice – an application that retrieves data from a table containing about 80,000 records, the main table is joined with 4-5 additional lookup tables, the whole query is paginated, about 25-30 records per page, about 2500-3000 pages in total. Database is Oracle 12c, there are indexes on a few columns, queries are generated by Hibernate. Measurements on production system at the server side show that an average time (median – 50% percentile) of retrieving one page is about 300 ms. 95% percentile is less than 800 ms – this means that 95% of requests for retrieving a single page is less that 800ms, when we add a transfer time from the server to the user and a rendering time of about 0.5-1 seconds, the total time is less than 2 seconds. That’s enough, users are happy.
And some theory – see this answer to know what is purpose of Pagination pattern
hi, i don't know much about using dBs in prod with lots of data so asking for advice on which to use. My usecase is that people are having conversations with ai models. I first check if the person is an authorised user based on an id. I then save their message and the response they get. I also save the timestamp. I'm storing tones of chats with this information. I want the check for authorised users to be as fast as possible so the response can be generated asap. I'm also doing a lot of queries over the saved chats, in which there are thousands of messages. Whats the best dB to use as storage? I already have a small start working with supabase but I'm not sure if thats the way to go. Any advice is appreciated 🙂 submitted by /u/lostlifon [link] [comments]
To join tables in PostgreSQL using the WITH clause (also known as Common Table Expressions or CTEs), you first need to define these tables…Continue reading on Medium »
This SQL query targets a PostgreSQL database and performs a join between two system views, pg_stat_ssl, and pg_stat_activity, using the…Continue reading on Medium »
PieCloudDB April updates include enhanced local cache file life cycle, Interconnect Proxy multi-process support, Flink Connector support…Continue reading on PieCloudDB Database »
In PostgreSQL, the transaction_id, or transaction identifier, is a crucial mechanism for ensuring data integrity and isolating…Continue reading on Medium »
How persistence with Compacted Topic helps Apache Kafka projects to store events long term without the need for another database.Continue reading on Medium »
I wonder if in Warehouse management system is it valid to have one generic "document" table that can have multiple types like order, sales, purchase, issue or should there be separate table for each type. What do you think? submitted by /u/FollowingMajestic161 [link] [comments]
PGDSAT (Postgresql Database Security Assessment Tool - https://github.com/HexaCluster/pgdsat) has been released. That sounds like fantastic news for PostgreSQL users! Having a dedicated security assessment tool like PGDSAT can greatly enhance the security posture of PostgreSQL clusters. With support for around 80 security controls, including CIS compliance benchmarks, it provides users with a comprehensive solution to identify and address potential security vulnerabilities. I've already tested and provided the summary of checks. You may run it without installation. It requires to install perl-bignum and perl-Math-BigRat packages as a prerequisite on Oracle Linux 8. Congrats to the HexaCluster team and Gilles Darold for their hard work and dedication in developing this valuable tool for the PostgreSQL community. Efforts like PGDSAT contribute significantly to enhancing the security and reliability of open-source software. submitted by /u/riddinck [link] [comments]
Every article I look at claims horizontal scaling to be the biggest advantage in favour of NoSQL databases, but that's not even true since there are multiple solutions to horizontal scaling of relational databases if I am not mistaken. So what advantages do they actually have? Does it depend on the specific implementation of NoSQL database? Like graph database being able to handle relationships better, wide-column being better when you mainly work with specific columns within a row etc.... Or is it the fact that they handle unstructured data better? But isn't it possible to optimise relational database for the same purposes? submitted by /u/Vraenir [link] [comments]
Link to Diagram - https://imgur.com/a/PEWQkMJ I am working on creating a database for a project I am doing for my friend. It will be used by a .NET Web API eventually. To try to keep it as simple as possible, I have left off fields that aren't related to the concerns. Project Overview Essentially the database is to store ticket purchases. There will be multiple purchases by the same purchaser across various seasons/leagues/teams. Each purchase could be for one game, or for all of the games. A purchase for a game could have different seats with different pricing for each combination of Section/Row/Seat. A purchase could also be for every game within a given season. If a purchase if for every game, it is likely they would have the same tickets for each game, but I do need to account for the possibility they wont. Things to keep in mind I have a Home Game table because I only care about the home games for the team the purchase is actually for. The League/Season/Team/HomeGame tables will all be synced automatically by an external API that will return the schedules for all teams in a given league. A Purchaser is stored in the DB and they can have multiple Phone Numbers/Emails/Addresses Home Game Table Questions I am worried about the redundancy of my relationships here. I currently have a reference to the League table here, which can be inferred by the Season Table or Team Table. Is the way I currently have it setup not the most ideal way to handle this? Purchase Table Questions When I actually query on this table I need to know how many games this purchase was for, which I am not sure if I can actually do that at the moment. I feel there is a ton of redundancy here but I am unsure how to avoid it. For example I am linking to all 4 of the Purchaser tables here which seems like a bit much. I am really open to any sort of feedback here, database design has always been a struggle of mine. Any tips or any sort of direction you believe I should go in, I am totally open to. submitted by /u/Nickt1596 [link] [comments]
I’m trying to find a solution for work that would allow us to replace hand written QC forms with digital forms so the information can be more easily stored and accessed using a database. I’m sure this exists but I can’t seem to google the right combination of words to find it. Anyone seen an app like this before? submitted by /u/supermoto07 [link] [comments]
I'm working on an ecommerce website for my thesis project. Of course, that means I will also need a database for storing the products, orders, customers, etc. I decided to go with Postgres. I came up with this schema. Does this look okay? Are there any pitfalls I did not consider? I also stored the SQL for the creation of the tables here. If you guys have any tips for improvements I would love to hear that. Some parts that may be worth explaining: Order is called purchase since order is a reserved word in SQL Customer and address is one to many, since a customer can have more than one address. Every customer has only one default address, however. Shipping status in the purchase table shows a very short summary of the status, while the status updates store the exact events that happened to the purchase. Purchase and product table has the price in it due to the fact that the price of the product may change after the order, so we need to keep a copy of the original price A subcategory can only belong to one category submitted by /u/dont_mess_with_tx [link] [comments]
Hello folks, this is my setting: Azure SQL Server, a star Schema Table with a few metadata columns for indexing and one big varchar(max) column filled with a JSON Data. We run a typical data warehousing application, relatively few inserts, practically no updates, but many bulk reads. The table is approaching a million rows. I am overall very happy with functionality and performance, but I am constantly looking for things to improve. One bottleneck on very large bulk reads is I/O, which is quite expensive in Azure. Of course, the best way would be avoiding these, but I have no influence on that. I was reading into CCI and some of MS articles and documentation praise it for huge compression gains (which I don't care about) but also vast improvements of I/O performance, which I do care about. My understanding is the column is compressed in partitions, but how does that help I/O? Sure, less disk reads because the data is highly compressed, but in the end, if the client requests the data, it must be unpacked and delivered, so at some point the unpacked data must pass the I/O barrier, before it is compressed for transport again anyway. Also, if a query requests data from different partitions, doesn't that create a huge overhead for the DBMS? Is it worth a try? Am I on the wrong path? Does anybody have experience with a similar setting? submitted by /u/roboblocky [link] [comments]
Greetings, As per the title, what convention's does one prefer when naming bridging tables for many-to-many relationships? I'm going with a simple approach, but I'm open to hearing of better. CREATE TABLE ${table1}_${table2} .... Cheers, submitted by /u/lickety-split1800 [link] [comments]
Hello Database community, We at Xinnor have been working on optimizing the performance of PostgreSQL databases, especially in virtual environments. We built a high-performance storage solution with Kioxia PCIe5 NVMe drives, motherboard by Supermicro and our RAID engine. I thought it might be of interest to this community. Have you tried any similar solutions? It would be great to learn more about your experience as well. More details in our blog post - https://xinnor.io/blog/high-performance-storage-solution-for-postgresql-database-in-virtual-environment-boosted-by-xiraid-engine-and-kioxia-pcie5-drives/ submitted by /u/PltnvS [link] [comments]
I've been testing CloudBeaver these last few days, and it seems great so far, but to the best of my knowledge, Google Authentication is not available on the Community Edition. As we're keen on integrating Google Authentication for user convenience and security, I'm exploring alternatives that better suit our team's needs. Specifically, I'm looking for a free solution that supports Google Authentication for user login and can connect to MySQL and Clickhouse databases. It would be great if it offers similar features to Cloudbeaver in terms of database management and querying capabilities. Does anyone here have recommendations for such a tool? Your insights would be immensely helpful! Thanks in advance for your suggestions! submitted by /u/matthieukhl [link] [comments]
so im currently doing a college work and generative ai just gave me a code where it uses CLOB instead of Varchar, is it going to put my college's connection in any danger? (I really need to write paragraphs inside serveral lines so...) submitted by /u/Mostardu [link] [comments]
Hi guys, I have a bunch of data in an excel sheet that I update with monthly stats, then I run tableau on top of it. The data is sensitive and can’t be on my local machine anymore. So I have access to a container running red hat on it. I want to start putting the data into a db and visualize that data. Trying to figure out what’s the simplest and easiest database to learn? Data is not really complex, it’s just rows and columns with data regarding how well my platform is being adopted submitted by /u/wake_the_dragan [link] [comments]
Assuming you wanted a document database,what are some good ones? I hear a lot about how mongodb is awful but not about what is a good alternative to the job ig is supposed to do. submitted by /u/Effective_Will_1801 [link] [comments]
What languages are typically used for the expressions in computed database columns? I did a little research into a few relational databases, and I noticed that the computed columns are often defined using SQL expressions, usually with some restrictions (such as resulting in a scalar). That is, if the language used is mentioned at all (it often is not). This seems fine, but what if you have a non-relational database or document store, or if you have complex SQL in your computed columns that could result in performance problems? I was thinking that this could be especially problematic if users are able to type code into a textbox and the code gets executed with each update. IIRC the "Dataverse" technology from Microsoft allows users to type arbitrary code in an Excel-like language to define computed columns. Hopefully, there are no technologies out there that allow users to define computed columns with expressions like: while(true) Then every update to the database risks taking down the server (maybe). In a way, it almost seems like computed columns are really small "serverless" applications. If the performance of the computed column is left entirely to the database designer, without any help from the system, then it seems it could be very tricky to get it right, and if the original database designer leaves the team/is laid off, they take their knowledge with them. For a while now, I've been developing a new programming language named Moirai (MIT License), an interpreted language where the worst case execution time of each script is calculated before execution begins. I always thought of the language as being useful for microservices and serverless applications, but then I learned about computed columns in databases and it made me wonder if the language could be used there as well. submitted by /u/tsikhe [link] [comments]
Here is the example I'm thinking of. This relates to pretty much nothing, I'm just having some trouble drawing the line for 5NF. I am new to normalization, so I appreciate any and all help: Say I have a list of plants that I'm growing. Each row has a plant ID, plant species ID, Common Name, //other stuff here that isn't relevant, and a temperature range. Should I do this: The row has a minTemp & maxTemp column, with the range defined in this plants table OR: The row has a Temp ID (FK) column, and there is an additional "Temperature" table, with defined temperature ranges. OR: Neither (please explain). Here's where I'm stuck. I feel like a range should not be defined in the primary table (the "plants" table), but if I make a "Temperature" table, then I have to do either: A) Every possible temperature range (At least the ones that life can survive in), OR B) Create an arbitrary set of "common(?) temperature ranges" or something like that, which would essentially be made-up data. Thanks in advance! submitted by /u/TheNumber473 [link] [comments]
In this blog post, I will show you how to seamlessly level up your Patroni cluster environment in an air-gapped environment, without an internet connection. You'll learn how to navigate through each step with ease, from adding essential extensions to installing the latest PostgreSQL 16 release. https://insanedba.blogspot.com/2024/04/howtoupgrade-postgresql.html submitted by /u/riddinck [link] [comments]
Hello! Does anyone know articles regarding databases and schema comparison state-of-the-art? I am searching but haven't been able to find anything but the tools' features (e.g. Schema Compare from SSDT). I need some articles regarding the state-of-the-art of these tools. Thank you in advance submitted by /u/Wonderful_Self_2285 [link] [comments]
What are the corresponding Azure and Google Cloud services for each of the AWS services?
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?
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.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)
Tags: #AlexaSkillsKit, #MicrosoftBotFramework, #GoogleAssistant Differences: One major advantage Google gets over Alexa is that Google Assistant is available to almost all Android devices.
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).
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.
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.
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.
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
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.
Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular 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.
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.
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.
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.
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.
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.
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.
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.
Deploy orchestrated containerized applications with Kubernetes. Simplify monitoring and cluster management through auto upgrades and a built-in operations console.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
Application Gateway is a layer 7 load balancer. It supports SSL termination, cookie-based session affinity, and round robin for load-balancing traffic.
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.
Provides analysis of cloud resource configuration and security so subscribers can ensure they’re making use of best practices and optimum configurations.
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.
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.
Provides managed domain services such as domain join, group policy, LDAP, and Kerberos/NTLM authentication that are fully compatible with Windows Server Active Directory.
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.
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.
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).
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.
An automated security assessment service that improves the security and compliance of applications. Automatically assess applications for vulnerabilities or deviations from best practices.
Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.
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.
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.
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.
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.
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.
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
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.