Amazon Athena vs Azure Data Factory

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Amazon Athena

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Amazon Athena vs Azure Data Factory: What are the differences?

Introduction

Amazon Athena and Azure Data Factory are two popular cloud-based data analytics services offered by Amazon Web Services (AWS) and Microsoft Azure respectively. While both services provide capabilities for processing and analyzing data, there are key differences between them that determine their suitability for specific use cases.

  1. Connectivity and Integration: When it comes to connectivity and integration, Amazon Athena is designed to work directly with data stored in Amazon S3, which means it does not require any specific data ingestion or transformation operations. On the other hand, Azure Data Factory offers more flexibility in terms of connectivity as it can integrate with various data sources and services within the Azure ecosystem, allowing data to be sourced from different platforms and processed.

  2. Data Transformation Capabilities: While both services support data transformation operations, Azure Data Factory provides a more comprehensive set of data transformation capabilities compared to Amazon Athena. Azure Data Factory offers a visual interface for developing data transformation pipelines and supports built-in activities for data wrangling and transformation. In contrast, Amazon Athena primarily focuses on querying and analyzing data, with limited transformation capabilities.

  3. Data Processing and Computing: Amazon Athena leverages the serverless processing capabilities of AWS Glue, which allows users to analyze data without the need for infrastructure provisioning or management. AWS Glue automatically handles the underlying compute resources required for query execution. On the other hand, Azure Data Factory uses Azure Data Lake Analytics or Azure HDInsight for data processing, which provides a distributed computing environment for executing complex data processing tasks.

  4. Pricing Model: The pricing model of Amazon Athena is based on the amount of data scanned during query execution. Users are charged on per-TB basis for the amount of data processed. Azure Data Factory, on the other hand, follows a more flexible pricing model based on the number of data movement and transformation activities executed. Users pay for the number of activities executed and the runtime of those activities.

  5. Managed Service Offering: Both Amazon Athena and Azure Data Factory are offered as managed services, but Amazon Athena is a serverless offering that fully manages the underlying infrastructure and resources. Azure Data Factory, on the other hand, provides more control and flexibility over the infrastructure configuration as it allows users to choose between serverless or dedicated integration runtimes.

  6. Third-Party Ecosystem: Azure Data Factory has better integration capabilities with various third-party services and data connectors available in the Azure ecosystem. It provides built-in connectors for popular data sources, databases, and data platforms, making it easier to ingest and process data from those services. Amazon Athena, although it can work with external data catalogs and external tables, has a more limited set of built-in connectors and integrations.

In summary, Amazon Athena and Azure Data Factory differ in terms of connectivity and integration options, data transformation capabilities, data processing and computing environments, pricing models, managed service offerings, and third-party ecosystem integrations. These differences influence the suitability and flexibility of the services for specific use cases and requirements.

Advice on Amazon Athena and Azure Data Factory
Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 256.5K views

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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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?

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Replies (4)

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

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Carlos Acedo
Data Technologies Manager at SDG Group Iberia · | 5 upvotes · 247.2K views
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Amazon RedshiftAmazon Redshift

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.

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Alexis Blandin
Recommends
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Amazon AthenaAmazon Athena

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

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you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved

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Pros of Amazon Athena
Pros of Azure Data Factory
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 6
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
  • 3
    Easy integration with QuickSight
  • 2
    Query and analyse CSV,parquet,json files in sql
  • 2
    Also glue and athena use same data catalog
  • 1
    No configuration required
  • 0
    Ad hoc checks on data made easy
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    What is Amazon Athena?

    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

    What is Azure Data Factory?

    It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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    What are some alternatives to Amazon Athena and Azure Data Factory?
    Presto
    Distributed SQL Query Engine for Big Data
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Spectrum
    The community platform for the future.
    See all alternatives