snowflakes vs redshift vs databricks

Snowflake Vs Redshift Vs Databricks – Comparing Popular Data Management Technologies

snowflakes vs redshift vs databricks

Data is the new oil and can offer its best only if it is managed properly. Utilization of proper tools and technologies is a must for leveraging data to its finest. Organizations today, are looking at keeping their data architecture intact and modernized so that they can operate their businesses with agility and maximum returns. What is needed is top-notch data management solutions of data warehouse solutions. 

And when we talk about managing the data and creating data platforms, especially in the cloud, there are three popular technologies that are foremost in the run – Snowflake Vs Redshift Vs Databricks. All of them have proven their mettle in the world of data management and data warehousing. 

This article aims to compare the three technology stalwarts with respect to their features, offerings, pros and cons, integration, organizations using them, etc. Before we view them together, let us individually peep into their introduction.

What is Snowflake?

Snowflake offers a cloud-based data storage and analytics service, generally termed “data-as-a-service”. It allows corporate users to store and analyze data using cloud-based hardware and software.

It is a fully managed service offering a unified platform for data lakes, data warehousing, data science, data engineering, and data application development. It solves the problems that traditional systems are not able to, lessening the burden on the management. It offers a competitive edge to enterprise-wide systems.

Key Features of Snowflake

  • Separation of computing and storage
  • Data cloning and sharing
  • Support for third-party tools
  • Semi-structured data
  • Cloud provider agnostic
  • Nearly nil administration
  • Concurrency and workload separation

Good Read: Simplifying Feature Engineering With Data Vault On Snowflake

What is Redshift?

Amazon Redshift is a data warehouse product that forms part of the larger cloud-computing platform Amazon Web Services. It makes use of SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, with the use of AWS-designed hardware and machine learning.

A Redshift database is a cloud-driven, big data warehouse solution that offers a storage system that can store petabytes of data, which is easily accessible and can be queried simultaneously. Each data warehouse is completely managed with automated tasks like security, configuration, etc. 

Key Features of Redshift

  • Effective storage and security
  • High-performance query processing
  • Low cost and easy compatibility with other services
  • Massive parallel processing
  • Easy to setup, deploy and manage
  • Complete data encryption
  • Network isolation

What is Databricks?

Databricks develops a web-based platform for working with Spark, that provides automated cluster management and IPython-style notebooks. Databricks is a simple, open, and multi-cloud platform that can join data warehousing and AI use cases on a single platform, create open-source standards, and offers a consistent platform across clouds.

The Databricks Lakehouse Platform offers a unified set of tools to create, deploy, share, and maintain enterprise-level data solutions at scale. It integrates well with cloud security and storage in the cloud account for effective management and deployment of cloud infrastructure. 

Key Features of Databricks

  • Access control and security
  • Inherent scalability to huge data bulks
  • Fast and cost-effective
  • Visualization and compliance
  • Accessibility on all major clouds
  • Industry-specific accelerators
  • Integrates engineering, data science, and operations

Snowflake Vs Redshift Vs Databricks – Pros and Cons



  • Scalable and cost-effective
  • Data science and analytics
  • Straightforward to use
  • Minimal setup and fully managed
  • Combines heterogeneous clouds from different vendors
  • Cons
  • No code reusability
  • No unit testing
  • Lacks unstructured data support



  • Ease of use and accessible
  • Faster query speed upgrades
  • High performance with quick loading
  • Horizontally scalable
  • Columnar storage reduces disk I/O


  • Billing by seconds 
  • Not 100% managed 
  • Can’t apply data individuality



  • Easy versioning of datasets
  • An extensive list of data sources
  • Familiar languages and environment
  • Flexibility across AWS, GCP, and Azure
  • Data reliability and scalability


  • The code is not production friendly
  • Needs programming skills
  • Time-consuming integration





Founded In

in 2012

in 1994

in 2013


Upload and store data files with automatic conversion into an
organized format

Stores data in columns, with all the data unified after the ETL process,
service, and workflow

Works with any kind of data in its basic format, used as an ETL tool


Talend, Tableau, AWS, Fivetran, Kubit, Immuta, Noteable, Polytomic, Bigeye

SnapLogic, Etleap, Datacoral, Informatica, Lightdash, Bigeye, Ataccama ONE

Talend, Tableau, Redshift, MongoDB, Lyftrondata, Feast, Polytomic


Two-factor authentication, encryption, VPC/VPN network isolation

Identity and access management, encryption, Virtual Private Cloud

Production monitoring, feature requests, multifactor authentication


pricing model – standard, premier, enterprise

, and on-demand pricing model

for data engineering, data analytics, enterprise

Using Them

Microsoft, Amazon, Allianz, Google, Capital One, Door Dash, jetBlue,
Warner Music Group, NetSuite Inc.
, etc.

Lyft, Amazon, Figma, CRED, Nubank, Tech Stack, Bitpanda, Delivery Hero, Coursera, Nasdaq, VOO, etc.

SEGA, Riot Games, Paramount, Disney, Acxiom, Salesforce, HP, Shell,
Viacom, Radius, Regeneron, etc.

Summing It Up

In the world of data management and data warehousing, comparing the three technology stalwarts – Snowflake Vs Redshift Vs Databricks is like choosing the better out of the best. Each of them has a popularity quotient in the industry and has a fan following too. Be it any, they lead you to a road for enhanced business intelligence and thereby, a better future. What is most important is to get all of your data, structured or unstructured, to its appropriate destination. 

It all depends upon certain organizational parameters like budget, skilled expertise, business requirements, project timelines, daily usage patterns, and the amount of data you will have to handle, etc., that can help in deciding which one to choose. Select either, you are at a gain! Yes, it will surely make a difference if you choose an apt IT solution partner to help you decide on one and then assist you in implementing it the right way. 

Once the system is implemented, it is a time-consuming decision to make, special if there are many data sources. Integrating data from all sources, cleaning, transforming, and loading it to a cloud data warehouse is basically considered apt for business analysis. These challenges call for an IT expert organization to assist in leveraging the data management tool.

Are you keen to implement an effective data warehousing solution in your organization? Contact us and we will be pleased to assist you. 


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