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      SnowFlake, RedShift, and BigQuery: A Comparative Analysis

      Jun 29, 2020

      3 minute read

      Did you know that 175 trillion gigabytes of new data will be generated in 2025?

      Now that’s big data.

      When dealing with large amounts of data, you need a database that can handle large data-sets.

      With rapidly-changing data needs, you also need to bring in new data-sets for analysis.

      A data warehouse helps you improve data quality & consistency, and power decision-making with enhanced business intelligence.

      In this blog post, we’ll compare SnowFlake, Amazon RedShift, and Google BigQuery to help you power business growth with accelerated cloud analytics.

      Snowflake, Amazon RedShift, and Google BigQuery: A Comparative Analysis

      SnowFlake, RedShift & BigQuery: A Comparative Analysis

      Hybrid architecture

      • SnowFlake: It can be a struggle to manage a hybrid environment that supports on-premises software and cloud-based services.
      • Amazon RedShift: It can also be difficult to manage a hybrid environment.
      • Google BigQuery: This a cloud-native data warehouse.

      Storage and computing

      • SnowFlake: This data warehouse separates data storage, processing, and consumption.
      • Amazon RedShift: It does not separate storage from computing resources.
      • Google BigQuery: There is a complete separation of storage and computing resources.


      • SnowFlake: This data warehouse enables you to slice up trillions of rows. In addition to this, you can scale storage and computing independently as well as instantly, with the automatic scaling of the metadata service.
      • Amazon RedShift: With local storage configuration, you will be unable to independently scale resources. Moreover, it can take several hours to resize machine instance types.
      • Google BigQuery: You get full elasticity, as well as greater computing resources to handle large data loads.


      • SnowFlake: Security standards are fair for a cloud-based data warehousing solution.
      • Amazon RedShift: With a stronger focus on security, you get sign-in credentials, SSL connections, as well as load data encryption.
      • Google BigQuery: All data is encrypted at rest, as well as in transit, by default.

      Dynamic caching system

      • SnowFlake: You can store all query results for 24 hours to 90 days (for enterprise customers) or until the underlying data changes.
      • Amazon RedShift: Cached results are returned instantly as opposed to having to re-run the query when there is no change in data.
      • Google BigQuery: The results of your deterministically-written SQL query will automatically be cached for 24 hours if there is no change in data.
      At Grazitti Interactive, the data analytics team knows just how important it is to power business growth with data-driven insights.

      Make smarter business decisions with smarter data analytics!

      SQL capabilities

      • SnowFlake: The solution supports JSON, XML, Avro, as well as Parquet, using a special data type. Get standard and extended SQL support for:
      1. Most DDL and DML defined in SQL:1999
      2. Multi-table INSERT, MERGE, and multi-merge
      3. Temporary and transient tables
      4. Lateral views
      5. Statistical aggregate functions
      6. Windowing functions and grouping sets
      7. Scalar and tabular user-defined functions
      8. Information schema (Data dictionary)
      • Amazon RedShift: Certain operations are unavailable, such as:
      1. Table partitioning
      2. Constraints (unique, foreign keys, primary keys, checks)
      3. Indexes
      4. Array and row constructors
      5. Stored procedures and triggers
      6. Full-text search
      • Google BigQuery: Get ANSI 2011 SQL compatibility with nested and repeated data types as well as support for JSON and XML data type storage.


      • SnowFlake: This data warehouse requires low-maintenance. You get automatic and quick provision for greater computing resources.
      • Amazon RedShift: With complexities in integration, you will need to periodically vacuum/analyze tables.
      • Google BigQuery: While the data warehouse requires low-maintenance, it is limited by the absence of indexes, column constraints, and performance tuning capabilities.


      • SnowFlake: There is no requirement to manage the data warehouse.
      • Amazon RedShift: It may be difficult to manage, with updation requiring many hours.
      • Google BigQuery: The provider handles the backend configuration and tuning.


      • SnowFlake: Get cheap storage and more compute for every dollar spent.
      • Amazon RedShift: Get low-cost pricing and pay-as-you-go. In addition to this, get discounts for optional term commitments.
      • Google BigQuery: Get a pay-as-you-go option for the data imported, followed by the cost per query. A fixed pricing option is also available.

      Wrapping up

      And there you have it!

      A comparative analysis of SnowFlake, Amazon RedShift, and Google Big Query to help you power business growth with accelerated cloud analytics.

      By enabling you to store historical information and analyzing data over a specific period of time, a data warehouse helps you make informed business decisions with improved business intelligence.

      Power Business Growth With Enterprise Data Warehousing! Contact us.

      At Grazitti Interactive, the data analytics team is dedicated to helping you drive business success with data-driven insights.

      Get started today!

      You can also write to us [email protected] and we will take it from there!

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