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  5. Amazon Athena vs Azure Synapse

Amazon Athena vs Azure Synapse

OverviewDecisionsComparisonAlternatives

Overview

Amazon Athena
Amazon Athena
Stacks519
Followers840
Votes49
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Amazon Athena vs Azure Synapse: What are the differences?

Introduction

In this article, we will discuss the key differences between Amazon Athena and Azure Synapse, two popular cloud-based analytics services. Markdown code will be used to format the content for a website.

  1. Integration with Cloud Ecosystem: Amazon Athena is part of the Amazon Web Services (AWS) ecosystem, while Azure Synapse is part of the Microsoft Azure ecosystem. This means that Athena is tightly integrated with other AWS services like S3 for data storage and AWS Glue for data cataloging. On the other hand, Azure Synapse seamlessly integrates with other Azure services such as Azure Data Lake Storage and Azure SQL Data Warehouse, providing a unified experience within the Azure environment.

  2. Query Execution Engine: Athena uses Presto, an open-source distributed SQL query engine, for executing queries on data stored in Amazon S3. Synapse, on the other hand, utilizes a combination of massively parallel processing (MPP) architecture through an optimized version of SQL Server and Apache Spark for processing big data workloads. This difference in query execution engines may lead to variations in performance and query capabilities.

  3. Data Warehousing Capability: Azure Synapse is primarily designed as a data warehousing solution, providing features like columnar storage, data optimization, and workload management. It offers integration with other Azure services like Azure Machine Learning and Power BI, facilitating a comprehensive analytics and data exploration experience. In contrast, Amazon Athena focuses on providing ad-hoc query capabilities on data stored in S3, without specifically targeting data warehousing functionalities.

  4. Pricing Model: Both Amazon Athena and Azure Synapse have different pricing models. Athena follows a pay-per-query pricing model, where users are billed based on the amount of data scanned by each query. This can be cost-effective for sporadic querying but may become expensive for heavy workloads. Azure Synapse, on the other hand, offers different pricing tiers based on the size and performance level of the provisioned resources, allowing users to tailor their pricing based on their specific requirements.

  5. Data Integration and ETL: Azure Synapse provides built-in data integration and ETL (extract, transform, load) capabilities through the Azure Data Factory, enabling seamless data movement and transformation between various data sources. Amazon Athena, on the other hand, relies on AWS Glue for data cataloging and integration tasks, requiring additional configuration and setup for managing data pipelines.

  6. Scalability and Workload Management: Scaling capabilities differ between Amazon Athena and Azure Synapse. Athena automatically scales query execution based on the input data size, optimizing performance as the workload increases. Azure Synapse offers both serverless and provisioned options, allowing users to scale performance and resources based on their workload demands. It provides more fine-grained control over scaling and workload management compared to Athena.

In summary, Amazon Athena is tightly integrated with the AWS ecosystem, focuses on ad-hoc querying on S3 data, and follows a pay-per-query pricing model. Azure Synapse, part of the Azure ecosystem, targets data warehousing with comprehensive analytics capabilities, offers data integration and ETL functionalities, and provides flexibility in scaling and workload management.

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Advice on Amazon Athena, Azure Synapse

Pavithra
Pavithra

Mar 12, 2020

Needs adviceonAmazon S3Amazon S3Amazon AthenaAmazon AthenaAmazon RedshiftAmazon Redshift

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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Detailed Comparison

Amazon Athena
Amazon Athena
Azure Synapse
Azure Synapse

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.

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
519
Stacks
104
Followers
840
Followers
230
Votes
49
Votes
10
Pros & Cons
Pros
  • 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
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Dictionary Size Limitation - CCI
  • 1
    Concurrency
Integrations
Amazon S3
Amazon S3
Presto
Presto
No integrations available

What are some alternatives to Amazon Athena, Azure Synapse?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Amazon Redshift

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.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

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