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Amazon Redshift
Amazon Redshift

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Google BigQuery
Google BigQuery

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Amazon Redshift vs Google BigQuery: What are the differences?

What is Amazon Redshift? Fast, fully managed, petabyte-scale data warehouse service. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets 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.

What is Google BigQuery? Analyze terabytes of data in seconds. 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..

Amazon Redshift and Google BigQuery can be primarily classified as "Big Data as a Service" tools.

Some of the features offered by Amazon Redshift are:

  • Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
  • Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.
  • No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.

On the other hand, Google BigQuery provides the following key features:

  • All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
  • Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
  • Affordable big data- The first Terabyte of data processed each month is free.

"Data Warehousing" is the top reason why over 27 developers like Amazon Redshift, while over 17 developers mention "High Performance" as the leading cause for choosing Google BigQuery.

According to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks; compared to Google BigQuery, which is listed in 160 company stacks and 41 developer stacks.

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What is 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.

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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Why do developers choose Amazon Redshift?
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    What are some alternatives to Amazon Redshift and Google BigQuery?
    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.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    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.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Amazon EMR
    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
    See all alternatives
    Decisions about Amazon Redshift and Google BigQuery
    Ankit Sobti
    Ankit Sobti
    CTO at Postman Inc · | 11 upvotes · 84.9K views
    atPostmanPostman
    Looker
    Looker
    Stitch
    Stitch
    Amazon Redshift
    Amazon Redshift
    dbt
    dbt

    Looker , Stitch , Amazon Redshift , dbt

    We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

    For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

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    Google BigQuery
    Google BigQuery
    Snowflake
    Snowflake

    I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

    What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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    Reviews of Amazon Redshift and Google BigQuery
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    How developers use Amazon Redshift and Google BigQuery
    Avatar of ShareThis
    ShareThis uses Google BigQueryGoogle BigQuery

    BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.

    Avatar of Lyndon Wong
    Lyndon Wong uses Google BigQueryGoogle BigQuery

    Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level.

    Avatar of Olo
    Olo uses Amazon RedshiftAmazon Redshift

    Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.

    Avatar of Blue Shell Games
    Blue Shell Games uses Google BigQueryGoogle BigQuery

    Google's insanely fast, feature-rich, zero-maintenance column store. Used for real-time customer data queries.

    Avatar of Christian Moeller
    Christian Moeller uses Amazon RedshiftAmazon Redshift

    Connected to BI (Pentaho)

    Avatar of Kovid Rathee
    Kovid Rathee uses Amazon RedshiftAmazon Redshift

    OLAP and BI

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