Data is both a powerful asset and a potential challenge to manage. It holds the keys to understanding the world around us, but without the right tools, it can also lead to confusion. With this emerges three leading software solutions designed to handle the complexities of data management and analysis - Snowflake, Databricks, and AWS Redshift.
However, coming to a detailed and comprehensive comparison between the three can be tough without a guide. This article aims to break down each software in terms of core features, integration, pricing, and more to help you to make an informed decision about which solution best fits your needs.
Overview of Snowflake vs. Databricks vs. AWS Redshift
The global data warehousing market could reach $51.18 billion by 2028, growing at a CAGR of 10.7% from 2020 to 2028, and Snowflake is among the key players. Looking into Databricks vs. Snowflake vs. AWS Redshift will help you find the best tool to help you store, centralize, transform, and analyze data.
What Is Snowflake?
Snowflake is a cloud data platform and a modular and scalable data warehouse or repository for nearly all industries. That includes healthcare, gaming, media & advertising, financial services, and more. The Snowflake technology is a DWaaS that operates on AWS with no infrastructure to manage or knobs to adjust.
It is meant to solve challenges that conventional (legacy, cloud, and on-premises) data systems can't. It also eliminates a big data platform's administrative and management burdens.
With Snowflake, you get a competitive enterprise-level data warehousing service.
What Is Databricks?
Databricks is a unified data analytics solution that combines data engineering and science across the machine learning lifecycle, from data preparation to ML configuration management.
Its unique and extensive features help firms harness AI. Meanwhile, Databricks SQL vs. Snowflake cloud services allow customers to operate a multi-cloud lakehouse architecture. The software is ideal for firms in energy and utilities, financial services, advertising & marketing.
It works well also with the public sector, telecom, healthcare & life science, and many others.
What Is AWS Redshift?
AWS Redshift is Amazon's cloud-based data storage solution. It uses SQL to query petabytes of organized and semi-structured data across your data warehouse, operational database, and data lake.
Because the solution is fully linked with AWS, you can save your query results to S3 in open formats. Like many AWS services, it can be set up with a few clicks and has several data import possibilities. Redshift data is encrypted for protection.
Credit: Lukas
Snowflake vs. Databricks vs. AWS Redshift - Detailed Comparison
Let's put Snowflake, Databricks, and AWS Redshift side-by-side to check their features, functionalities, and prices to help you determine which one will best address your requirements.
Core Features
Snowflake’s architecture allows database features, solid support offerings, security features and validations, and integrations.
Databricks provides collaboration, interactive exploration, Databricks runtime, task scheduling, dashboards, integrated identity management, audits, notebook workflows, visualization, and more.
AWS Redshift allows column-oriented databases, massively parallel processing (MPP), end-to-end data encryption, network isolation, fault tolerance, concurrency limits, etc.
Data Structure
Snowflake’s cloud data warehouse allows the upload and storage of structured and semi-structured files. There’s no need to first organize them with an ETL tool before loading them into the EDW. Snowflake data types are immediately converted into an internally organized format.
Databricks can work with any type of data in its original format. It could be used as an ETL tool to structure unstructured data before processing by other tools such as Snowflake and Redshift.
AWS Redshift supports three primary methods for extracting and loading data from a source: creating your ETL workflow, using Amazon's managed ETL service, or using one of several third-party cloud ETL services that work with Redshift. Redshift then stores data in columns, with each column's data stored together.
Integration
Snowflake can integrate with business systems and applications like Looker, AWS, Tableau, Talend, and Fivetran, to mention a few.
Databricks also integrates with other business systems and apps like Looker, Amazon Redshift, Tableau, Talend, Pentaho, Alteryx, Redis, Cassandra, MongoDB, etc.
AWS Redshift integrates with AWS Partners via the Cluster details page on the AWS Redshift console. It can integrate with Datacoral, Etleap, Fivetran, Informatica, SnapLogic, etc.
Security
Snowflake provides two-factor authentication, always-on enterprise-grade encryption, and PCI compliance, accessible starting with the Business Critical plan. It includes encryption and VPC/VPN network isolation options.
Databricks features a software development lifecycle (SDLC) that includes security in all processes, from feature requests to production monitoring. Accessing key infrastructure consoles like cloud service provider consoles requires multifactor authentication.
AWS Redshift has two-factor authentication. As part of AWS, Redshift can employ the internal identity and access management (IAM) role. It also has customizable end-to-end encryption, virtual private cloud (VPC), and AWS CloudTrail audits that satisfy regulatory standards.
Pricing
Snowflake pricing operates on a time-based model and charges based on how long queries take to run. It provides four enterprise-level options: standard, premier, enterprise, and enterprise for sensitive data.
Databricks pricing, unlike other Databricks competitors, involves the billing of clusters based on "VM cost + DBU cost," not on time spent running the Spark application or any notebook runs or jobs. Also, it provides users with three enterprise pricing options. These options are Databricks for data engineering workloads, Databricks for data analytics workloads, and Databricks enterprise plans.
AWS Redshift charges by the instance/cluster or by the capacity used. You specify how much computing power you require and pay a flat fee regardless of whether or not you use it. It provides both pay-as-you-go and on-demand pricing options.
Key Reasons for Using Snowflake vs. Databricks vs. AWS Redshift
In a survey that Statista reported, 83% of US warehousing and logistics providers were using WMS from 2015 to 2021. Companies may have varying reasons for choosing the solutions they use. Here are a few of them:
Why Snowflake?
- Snowflake is a fully managed, cloud-deployed data warehouse (DWH) that requires minimal setup.
- Auto scalability and auto suspend provide flexibility, performance optimization, and cost management.
- It features separate storage and compute, which is rare in cloud data warehousing.
- Snowflake allows the creation of separate compute resources, known as virtual warehouses, enabling simultaneous execution of ETL workflows, BI reports, and other analytical queries.
- It supports fully-structured and semi-structured data types, such as JSON, Parquet, XML, and ORC.
- Snowflake competes with other leading data warehouses like AWS Redshift and Google BigQuery.
- It can integrate heterogeneous clouds from different vendors.
Why Databricks?
- Unified data analytics platform for engineers, scientists, analysts, and business analysts.
- Flexibility across AWS, GCP, and Azure.
- Delta Lake ensures data reliability and scalability.
- Supports sci-kit-learn, TensorFlow, Keras, libraries (matplotlib, pandas, NumPy), scripting languages (R, Python, Scala, SQL), tools, and IDEs (JupyterLab, RStudio).
- MLFLOW uses AutoML and model lifecycle management.
- Built-in visualizations.
- HYPEROPT allows hyperparameter tuning.
- Compatible with GitHub and Bitbucket.
- 10X faster than other ETLs.
Why AWS Redshift?
- MPP architecture loads and queries fast for analytics and reporting.
- Columnar storage reduces disk I/O, thus improving performance.
- Horizontally scalable.
- Moves data between old and new clusters during scaling.
- Transparent pricing.
- Query engine based on ParAccel, with the same interface as PostgreSQL.
- Options for network isolation, access control, and data encryption.
- Clusters can launch in a VPC.
- Grant privileges to specific users or maintain database-level access using AWS's Access Control system.
Learn more about Snowflake on our blog:
- Snowflake and HIPAA Compliance
- Why Use Snowflake for HIPAA Compliant Data Infrastructure
- Getting Started with Snowflake Data Cloud
What is the difference between Snowflake and AWS Redshift?
Both Snowflake and Redshift are cloud data warehouses, but different in how they're tailored to diverse requirements.
Snowflake provides a fully managed service, separating storage and compute for independent scaling, with a pay-as-you-go model.
Redshift, that lives within the AWS cloud, grants greater control over hardware configurations, ideal for experienced users.
Snowflake is known for its simplicity and ease-of-use, while Redshift offers more customization options for advanced users.
Is AWS Redshift faster than Snowflake?
The performance comparison between AWS Redshift and Snowflake depends on various factors such as workload, data structure, and optimization.
Redshift's integration with AWS and Snowflake's separate scaling of storage and compute offer distinct performance advantages.
Evaluating your use cases and conducting performance tests will help you choose the platform that best fits your needs.
Limitations of Snowflake vs. Databricks vs. AWS Redshift
You must consider some limitations between Snowflake vs. AWS Redshift vs. Databricks.
Snowflake
- Reliance on AWS, Azure, or GCS is a problem if one of these cloud servers goes down.
- Lacks unstructured data support.
- Has few geospatial data options.
Databricks
- The manual process requires advanced technical and programming skills.
- Integrating Azure and Databricks is time-consuming.
- Can move data only between Azure Databricks and Synapse; transferring data to another Cloud platform requires integration with that platform.
AWS Redshift
- Can't enforce data uniqueness.
- Sort and distribution keys determine how Redshift stores and indexes data.
- Fast for large data queries, reporting, and analytics, not live web apps.
- Cloud-based data could raise security issues.
Key Takeaways
Pitting Snowflake, Databricks, and AWS Redshift against each other is not enough to find the best solution for your data needs.
You have to match these solutions' features to your business requirements. The solution with the most features may be a lot for you in the sense that you may end up paying for features you don't need.
So, which solution should you go for? Whether you choose to implement Snowflake, Databricks, or AWS Redshift, the one that meets your requirements at a price you are willing to pay is always your best answer. Discover the perfect data solution tailored to your needs.
Get in touch with us to make an informed decision for optimized performance and cost-effectiveness today.
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