Snowflake vs Redshift vs Google BigQuery

Share on facebook
Share on twitter
Share on linkedin

Cloud data warehouses: The future of data management

What is BigQuery?

It is a Google Cloud Platform to an enterprise data warehouse for analytics. It is good for analyzing a huge amount of data to meet big data processing requirements. The provided data is encrypted, durable, and highly available. It offers Exabyte-scale storage and petabyte-scale SQL queries. Managing ever-growing data for enterprises is tough. This focus can be reshifted to analyze business-critical data. Dremel is a powerful query engine developed by Google that is used to execute queries in BigQuery.

What is Redshift?

Redshift can be described as a fully-managed, cloud-ready petabyte-scale data warehouse service that can be seamlessly integrated with business intelligence tools. Extract, transform, and load has to be done to make business smarter. To launch a cloud data warehouse, a set of nodes has to be launched called the Redshift cluster. Regardless of the size of data, one can take advantage of fast query performance.

What is Snowflake?

Snowflake is a powerful relational database management system. It’s an analytical data warehouse for both structured and semi-structured data that follows the SaaS model. It is fast, user-friendly, and offers more flexibility than traditional Warehouses. It uses an SQL database engine with a unique architecture specifically designed for clouds.

Comparision between Amazon Redshift, BigQuery and Snowflake

  • Highlights
  • Core Competencies
  • Integration
  • Sharing
Attributes BigQuery Redshift Snowflake
G2 Rating
 More time in optimizing queries.
 Partitions and sorts data in a background.
 Some operations need to be manual.
 Query planner still relies on table statistics for updating the stats.
 Little to maintain.
 It supports automatic clustering.
 It has depressed manual clustering.
Data Sources
One can set up a connection to some external data storage like Cloud SQL
We can connect to data sitting on S3.
Act as an intermediate compute layer.
The data has to be stored within Snowflake
Provides extra table functionality.
Native streaming
No native streaming
No native streaming. Microbatching via Snowpipe from data sitting on Google Cloud.
Caches queries and has an adjustable intermediate cache.
Caches queries and results depending on the node type.
Hot and warm query caches in intermediate storage.
Materialise View
Currently in GA
Has good support for materialized view
Full support for the materialized view.
Supports writing UDF's in SQL and Javascript.
UDFs can be written in SQL and PYTHON
Support for functions in SQL and Javascript.
Query Language
Offers two main dialect Legacy SQL Standard SQL
The SQL syntax is also an ANSI complaint.
ANSI complaint. Simple to use.
Encrypted using Google-managed encryption key management system(KMS)
End-to-End encrypted by default
Encrypted at rest using the AWS management system.
Only charged for queries you run
Recently induced RA3 node offers both elastic resize or classic resize
Pause, resume semantics both manual and automated based on workload.
2 cents per GB for warm data, 1 cent per GB for colder data
Node type (ds2/dc2/RA3, avoid d*1 node type. The Use of Redshift spectrum may incur additional charges.
With the number and size of warehouses, you will get cost per credit.
Cloud Deployment
Multicloud analytic solution. It is Google Cloud fully managed ware house.
Fully managed petabyte scale data ware house service in Cloud.
With it’s Cloud data platform live data can be shared.
Proprietary compression that is opaque to the user
Transparent compression by implementing open algorithms
It provides its compression layer that is opaque to the user.
Core Competencies BigQuery Redshift Snowflake
Data Integrations
Read data using streaming mode or batch mode.
Advanced ETL tool helps you effortlessly by collecting data.
ETL/ELT concept in data integration.
Data Compression
In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files.
Columnar compression
Gzip compression efficiency.
Data Quality
Advanced data quality with SQL.
Python data quality for amazon shift.
With tools like Talend provide data management with real time speed.
Built-In Data Analytics
Fully manages enterprise data for large scale data analytics.
Know is a BI tool used for Amazon Redshift.
A single platform that creates cloud.
In-Database Machine Learning
Bigquery ML let you create and execute machine. learning models using SQL queries.
Create data source wizard is used in Amazon Machine Learning to create data source object.
SQL dialect like
‘Intelligent Miner’ and ‘Oracle’ is being used.
Data Lake Analytics
Uses Identity and Access Management (IAM) manage access to resources to analyse data.
Uses amazon S3. It is cost efficient and stores unlimited data.
Global snowflake turns data lake into data ocean.
Integration BigQuery Redshift Snowflake
AI/ ML Integration
Use bigquery ML to evaluate ML models.
Create data source wizard in (Amazon ML).
Driveless A1
Automated machine learning inflows.
BI Tool Integration
BI is responsible for (RLS) Row Level Security and applying user permissions.
Know is a BI tool used in Redshift.
Built -for -cloud warehouse deliver efficient BI solution.
Data lake Integration
Data like API system use Google Cloud composer to schedule Bigquery Processing.
Integrated with data lake to offer 3x performance.
It is a modern data lake.
Sharing BigQuery Redshift Snowflake
Securely access and share analytical insights in few clicks.
Share data in Apache Parquet Format.
Enables sharing through shares between read-only.
Data Governance
Using google cloud that allows customers to abide by GDPR , CCTA and over regulations.
Data Lienage using Token.
Data governance experts like Talend provides perfect data governace.
Data Security
Security model based on Google Clouds. IAM capability.Column level security.
Network isolation to control access to data warehouse cluster. SSL and AES 256 encryption end – to – end encryption.
Role Based Access Control (RBAC) authorization.
Data Storage
Nearline storage
Columnar storage
Uses new SQL database.
Backup & recovery
Automatically backed up.
Automatically backed up.
Does with virtual warehouse and querying from clone.


Want more information about how to solve your biggest data warehousing challenges? Visit our resource center to explore all of our informative and educational ebooks, case studies, white papers, videos and much more.

How Lyftrondata helps

  • Lyftrondata provides cumulative data from a different source and brings it down to the data pipeline.
  • It works on the pain-points of data preparation, thus avoiding project delays.
  • It also converts the complex data into the normalized one.
  • It eliminates traditional bottlenecks related to data.
  • It works at solving problems such as huge time consumption to generate reports, waiting to get new reports, real-time data, and data inconsistency.
  • It democratizes data management.
  • It helps in combining other data sources to the target data warehouse.
  • It perfectly integrates the data and enables data masking and encryption to handle sensitive data.
  • It provides a data management platform for rapid data preparation with agility, combining it with the modern data pipeline.
  • It empowers business users to solve data-driven business problems.
  • It reduces the workload of prototyping tools while optimizing offload data.

Lyftrondata use cases

  • Data Lake:

    Lyftrondata combines the power of high-level performance and cloud data warehousing to build a modern, enterprise-ready data lake.

  • Data Migration:

    Lyftrondata allows you to migrate a legacy data warehouse either as a single LIFT-SHIFT-MODERNIZE operation or as a staged approach.

  • BI Acceleration:

    Limitlessly scale your BI. Query any amount of data from any source and drive valuable insights for critical decision making and business growth.

  • Master Data Management:

    Lyftrondata enables you to work with chosen web service platforms and manage large data volumes at an unprecedented low cost and effort.

  • Application Acceleration:

    With Lyftrondata, you can boost the performance of your application at an unprecedented speed, high security, and substantially lower costs.

  • IoT:

    Powerful analytics and decision making at the scale of IoT. Drive instant insights and value from all the data that IoT devices generate.

  • Data Governance:

    With Lyftrondata, you get a well-versed data governance framework to gain full control of your data, better data availability and enhanced security.

Lyftrondata delivers a data management platform that combines a modern data pipeline with agility for rapid data preparation. Lyftrondata supports you with 300+ data integrations such as ServiceNowZendeskShopifyPaylocity, etc. to software as a service SaaS platforms. Lyftrondata connectors automatically convert any source data into the normalized, ready-to-query relational format and provide search capability on your enterprise data catalog. It eliminates traditional ETL/ELT bottlenecks with automatic data pipelines and makes data instantly accessible to BI users with the modern cloud compute of Spark & Snowflake.

It helps easily migrate data from any source to cloud data warehouses. If you have ever experienced a lack of needed data, your report generation is too time consuming, and the queue for your BI expert is too long, then consider Lyftrondata.


Schedule a free, no strings attached demo to discover how Lyftrondata can radically simplify data lake ETL in your organization.

Recent Posts