Amazon Redshift vs Google BigQuery vs Snowflake

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Amazon Redshift
Amazon Redshift

846
586
+ 1
87
Google BigQuery
Google BigQuery

663
484
+ 1
98
Snowflake
Snowflake

183
157
+ 1
0
- No public GitHub repository available -
- No public GitHub repository available -
- No public GitHub repository available -

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.

What is Snowflake?

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
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Why do developers choose Amazon Redshift?
Why do developers choose Google BigQuery?
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        What companies use Amazon Redshift?
        What companies use Google BigQuery?
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        What are some alternatives to Amazon Redshift, Google BigQuery, and Snowflake?
        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.
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        Decisions about Amazon Redshift, Google BigQuery, and Snowflake
        Ankit Sobti
        Ankit Sobti
        CTO at Postman Inc · | 11 upvotes · 167.4K views
        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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        Google BigQuery
        Google BigQuery
        Amazon Redshift
        Amazon Redshift
        Amazon Athena
        Amazon Athena
        Amazon S3
        Amazon S3

        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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        Interest over time
        Reviews of Amazon Redshift, Google BigQuery, and Snowflake
        How developers use Amazon Redshift, Google BigQuery, and Snowflake
        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 Flux Work
        Flux Work uses SnowflakeSnowflake

        Data warehouse solution that fully separates compute and storage. Better management facility than directly using S3.

        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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