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Amazon Athena vs Azure Functions: What are the differences?
Key Differences between Amazon Athena and Azure Functions
Amazon Athena and Azure Functions are both cloud-based services provided by Amazon Web Services (AWS) and Microsoft Azure respectively. While they are both used in the cloud computing domain, they have some key differences.
Purpose and Functionality: Amazon Athena is an interactive query service that allows users to analyze data in Amazon S3 using SQL. It is primarily used for ad-hoc querying and analysis of data. On the other hand, Azure Functions is a serverless computing service that allows developers to run event-driven code in Azure. It enables the development of scalable, event-based applications and offers various triggers and bindings to integrate with other Azure services.
Language Support: Amazon Athena supports SQL as its query language, making it accessible to users familiar with SQL. However, Azure Functions supports a wider range of programming languages including C#, JavaScript, PowerShell, Python, and TypeScript. This gives developers more flexibility in choosing the language they are most comfortable with.
Billing Model: Amazon Athena follows a pay-per-query model, where users are charged based on the amount of data scanned by their queries. This provides cost-effectiveness for occasional and small-scale queries. On the other hand, Azure Functions follows a pay-as-you-go pricing model, where users are charged based on the number of executions and the resources utilized by their functions. This allows users to pay only for the actual usage of their functions.
Event Triggers: Azure Functions supports a wide range of triggers such as HTTP requests, timers, message queues, and database events. These triggers can automatically invoke the functions when specific events occur. In contrast, Amazon Athena does not have built-in event triggers. It relies on users manually invoking queries through the Athena API or the AWS Management Console.
Scalability and Infrastructure: Azure Functions automatically scales based on the number of incoming requests and the configured scale settings. It abstracts away the underlying infrastructure, allowing developers to focus on writing code. Amazon Athena, on the other hand, is built on top of Amazon S3 and uses a distributed query engine to process queries. It scales automatically to handle large datasets but may require users to optimize their queries for better performance.
Integration with Other Services: Azure Functions has tight integration with various Azure services like Azure Storage, Azure Event Hubs, Azure Cosmos DB, and Azure Service Bus. This enables seamless integration and data processing between different services. Amazon Athena can integrate with other AWS services such as AWS Glue, Amazon QuickSight, and Amazon QuickSight to form a comprehensive data analytics stack within the AWS ecosystem.
In summary, Amazon Athena is primarily focused on ad-hoc querying and analysis of data using SQL, while Azure Functions is geared towards building event-driven applications with support for multiple programming languages and a wide range of event triggers.
Need advice on what platform, systems and tools to use.
Evaluating whether to start a new digital business for which we will need to build a website that handles all traffic. Website only right now. May add smartphone apps later. No desktop app will ever be added. Website to serve various countries and languages. B2B and B2C type customers. Need to handle heavy traffic, be low cost, and scale well.
We are open to either build it on AWS or on Microsoft Azure.
Apologies if I'm leaving out some info. My first post. :) Thanks in advance!
I recommend this : -Spring reactive for back end : the fact it's reactive (async) it consumes half of the resources that a sync platform needs (so less CPU -> less money). -Angular : Web Front end ; it's gives you the possibility to use PWA which is a cheap replacement for a mobile app (but more less popular). -Docker images. -Kubernetes to orchestrate all the containers. -I Use Jenkins / blueocean, ansible for my CI/CD (with Github of course) -AWS of course : u can run a K8S cluster there, make it multi AZ (availability zones) to be highly available, use a load balancer and an auto scaler and ur good to go. -You can store data by taking any managed DB or u can deploy ur own (cheap but risky).
You pay less money, but u need some technical 2 - 3 guys to make that done.
Good luck
My advice will be Front end: React Backend: Language: Java, Kotlin. Database: SQL: Postgres, MySQL, Aurora NOSQL: Mongo db. Caching: Redis. Public : Spring Webflux for async public facing operation. Admin api: Spring boot, Hibrernate, Rest API. Build Container image. Kuberenetes: AWS EKS, AWS ECS, Google GKE. Use Jenkins for CI/CD pipeline. Buddy works is good for AWS. Static content: Host on AWS S3 bucket, Use Cloudfront or Cloudflare as CDN.
Serverless Solution: Api gateway Lambda, Serveless Aurora (SQL). AWS S3 bucket.
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of Amazon Athena
- Use SQL to analyze CSV files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
Pros of Azure Functions
- Pay only when invoked14
- Great developer experience for C#11
- Multiple languages supported9
- Great debugging support7
- Can be used as lightweight https service5
- Easy scalability4
- WebHooks3
- Costo3
- Event driven2
- Azure component events for Storage, services etc2
- Poor developer experience for C#2
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Cons of Amazon Athena
Cons of Azure Functions
- No persistent (writable) file system available1
- Poor support for Linux environments1
- Sporadic server & language runtime issues1
- Not suited for long-running applications1
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What is Amazon Athena?
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