Snowflake vs Redshift vs Google BigQuery

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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 the 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. With the growth of business managing data becomes a tough task. 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, 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 Red Shift 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 SaaS model. It is fast, User friendly and offers more flexibility than traditional Warehouse. It uses an SQL database engine with a unique architecture specially designed for clouds.

Comparision between Amazon Redshift, BigQuery and Snowflake

  • Highlights
  • Core Competencies
  • Integration
  • Sharing
Attributes BigQuery Redshift Snowflake
G2 Rating
Maintenance
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.
3-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.
Streaming
Native streaming
No native streaming
No native streaming. Microbatching via Snowpipe from data sitting on Google Cloud.
Caching
Catches 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.
UDF's
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.
Encryption
Encrypted using Google-managed encryption key management system(KMS)
End-to-End encrypted by default
Encrypted at rest using the AWS management system.
Scalability
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.
Pricing
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.
Compression
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
Sharing
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.

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  • Lyftron provides cumulative data from a different source and brings down to the data pipeline.
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  • It works at solving problems like huge time consumption to generate reports, waiting to get new reports, real-time data, and data inconsistency.
  • It democratized data management.
  • It helps in combining other data sources to the target data Warehouse.
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Lyftron use cases

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Lyftron delivers a data management platform that combines a modern data pipeline with agility for rapid data preparation. Lyftron 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 migrate data from any source easily to cloud data warehouses. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Lyftron.

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