Amazon Athena vs Google BigQuery

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Amazon Athena

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Google BigQuery

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Amazon Athena vs Google BigQuery: What are the differences?

Introduction:

Amazon Athena and Google BigQuery are both cloud-based data warehousing and analytics services. While they have similarities in terms of providing scalable and cost-effective solutions for processing large datasets, there are key differences between the two platforms. The following paragraphs highlight some of these differences:

  1. Data Storage and Structure: Amazon Athena stores data in Amazon S3, allowing users to directly query the data without the need for complex ETL processes. On the other hand, Google BigQuery has its own dedicated storage system, which eliminates the need for a separate storage service. It also supports a wide range of data formats, including native support for nested and repeated fields.

  2. Query Execution and Performance: Amazon Athena uses Presto, an open-source distributed SQL query engine, for query execution. This makes it suitable for running ad-hoc queries and provides flexibility in terms of data formats. Google BigQuery, on the other hand, uses a proprietary query engine optimized for large-scale data processing. It employs a columnar storage format and parallel processing techniques, which generally contribute to faster query performance.

  3. Pricing Structure: Amazon Athena follows a pay-per-query pricing model, where users are billed based on the amount of data scanned by the queries. This can provide cost savings for occasional or small-scale usage. In contrast, Google BigQuery has tiered pricing based on storage and query usage, which may be more suitable for users with predictable or high-volume workloads.

  4. Data Partitioning and Clustering: Amazon Athena supports partitioning and clustering of data, allowing users to optimize query performance by reducing the amount of data scanned. This is particularly useful for table scans on large datasets. Google BigQuery, on the other hand, automatically partitions and clusters data based on the table's schema and query patterns, further enhancing query performance.

  5. Integration with Ecosystem: Amazon Athena integrates well with other AWS services, making it a good choice for users already utilizing the AWS ecosystem. It also has seamless integration with AWS Glue, allowing for automated and scalable ETL processes. Google BigQuery integrates well with other Google Cloud Platform services, providing a cohesive experience for users in the Google Cloud ecosystem.

  6. Data Transfer and Loading: Amazon Athena supports querying data directly from external sources like Amazon Redshift and Amazon DynamoDB, making it easier to analyze data across different services. Google BigQuery, on the other hand, offers native connectors for various Google services like Google Sheets and Google Analytics, simplifying data loading and analysis within the Google Cloud environment.

In Summary, Amazon Athena and Google BigQuery differ in terms of data storage, query execution, pricing structure, data partitioning and clustering, ecosystem integration, and data transfer/loading capabilities. The choice between the two depends on specific use cases, existing infrastructure, and preferences.

Advice on Amazon Athena and Google BigQuery

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 · 239.4K views
Recommends
on
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
on
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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Recommends

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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Decisions about Amazon Athena and Google BigQuery
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Pros of Amazon Athena
Pros of Google BigQuery
  • 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
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn

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Cons of Amazon Athena
Cons of Google BigQuery
    Be the first to leave a con
    • 1
      You can't unit test changes in BQ data

    Sign up to add or upvote consMake informed product decisions

    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 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.

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    What companies use Amazon Athena?
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    See which teams inside your own company are using Amazon Athena or Google BigQuery.
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    What tools integrate with Amazon Athena?
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    What are some alternatives to Amazon Athena and Google BigQuery?
    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