Snowflake vs. Databricks vs. AWS Redshift: Choosing the Right Data Platform for Your Needs

As organizations grow and scale their data infrastructure, the choice of the right data platform becomes crucial. Today, three of the most popular cloud-based data solutions are Snowflake, Databricks, and AWS Redshift (there are other providers as well which is not included here). Each of these platforms offers unique strengths, targeting different types of workloads, from data warehousing and business intelligence to big data processing and machine learning.

In this blog, we’ll explore the key differences between Snowflake, Databricks, and AWS Redshift, focusing on their core functionalities, performance, ease of use, job orchestration, data transformation, and more. By the end, you should have a clearer understanding of which platform best suits your organization's data and analytics needs.

Core Focus: Data Warehousing vs. Data Lakes vs. Distributed Computing

Each platform is designed with specific use cases in mind:

Snowflake: Snowflake is a cloud-native data warehouse optimized for structured and semi-structured data. It’s designed for SQL-based analytics, with a focus on simplicity, scalability, and performance.
Databricks: Databricks is built on Apache Spark and focuses on data lakehouse architecture, merging the capabilities of data lakes and data warehouses. It excels at big data processing, data engineering, and machine learning.
AWS Redshift: AWS Redshift is Amazon’s fully managed data warehouse solution, designed to scale large datasets for SQL-based analytics and BI. It offers deep integration with the broader AWS ecosystem, making it an appealing choice for AWS users.

When to Choose Snowflake:

When you need a fully managed cloud data warehouse with strong support for SQL analytics and structured data.

When to Choose Databricks:

When your focus is on large-scale distributed data processing, machine learning, and advanced data engineering workflows.

When to Choose AWS Redshift:

When you’re heavily invested in the AWS ecosystem and need a cloud-based data warehouse for SQL querying and deep integration with AWS services.

Architecture: Different Approaches to Data Management

Each platform uses a different approach to managing and processing data.

Snowflake: Snowflake uses a unique multi-cluster, shared-data architecture, which separates compute and storage. This allows for independent scaling of both layers, making it highly flexible and cost-efficient for various workloads. Snowflake is also cloud-agnostic, meaning it can run on AWS, Azure, and Google Cloud.
Databricks: Databricks is based on Apache Spark, offering a distributed computing architecture ideal for processing large datasets in parallel across clusters. It supports both data lake and data warehouse capabilities through its Delta Lake format, which ensures ACID transactions for reliable data handling.
AWS Redshift: AWS Redshift uses a shared-nothing MPP (Massively Parallel Processing) architecture. It’s optimized for data warehousing on AWS, distributing data across multiple nodes to parallelize queries and processing. Redshift recently introduced Aqua (Advanced Query Accelerator), which improves query performance by offloading certain workloads to specialized hardware.

When to Choose Snowflake:

If you need a cloud-native, scalable, and managed data warehouse with compute and storage separated for cost control.

When to Choose Databricks:

If you need a robust platform for distributed data processing, especially for machine learning and big data analytics, where Spark's parallelism is key.

When to Choose AWS Redshift:

If your organization is AWS-centric and you need a high-performance data warehouse that integrates seamlessly with other AWS services like S3, Glue, and Lambda.

Performance: Comparing Query and Processing Speeds

Performance varies across the platforms, depending on the workload type.

Snowflake: Snowflake excels at fast, SQL-based querying, particularly for structured and semi-structured data. It automatically optimizes performance through result caching, automatic clustering, and parallel query execution. Snowflake is designed for read-heavy analytics workloads and handles concurrent queries efficiently.
Databricks: Databricks is optimized for big data processing and machine learning using Apache Spark’s distributed computing engine. It can process enormous datasets in parallel, making it ideal for complex ETL (Extract, Transform, Load) tasks. Databricks is more focused on high-performance transformations and machine learning workloads rather than straightforward SQL queries.
AWS Redshift: AWS Redshift performs well for SQL-based analytics and is highly optimized for massive datasets. Redshift uses columnar storage and MPP to speed up large, complex queries. The introduction of Aqua has further improved query speed for specific workloads, but Redshift can struggle with real-time or streaming data processing compared to Databricks.

When to Choose Snowflake:

If your focus is on high-speed SQL queries over structured data, particularly for BI and data reporting.

When to Choose Databricks:

If you need to handle complex data transformations, big data processing, and distributed machine learning workflows.

When to Choose AWS Redshift:

If you're focused on high-performance, SQL-based querying and are working with massive datasets within the AWS ecosystem.

Ease of Use: Managed Services vs. Flexibility

Ease of use can be a deciding factor for many organizations, depending on their team’s expertise and technical requirements.

Snowflake: Snowflake prioritizes simplicity. The platform abstracts infrastructure management, offering a fully managed environment that automates many tasks, including scaling, partitioning, and optimization. Snowflake’s SQL interface is user-friendly, making it accessible for analysts and teams who want an easy-to-use data warehouse without the need for deep technical expertise.
Databricks: Databricks is more technical and flexible but requires familiarity with Apache Spark and distributed computing concepts. The platform offers Notebooks where teams can write code in multiple languages (Python, Scala, R) and collaborate in real-time. This flexibility allows for advanced customization but comes with a steeper learning curve, particularly for those without Spark experience.
AWS Redshift: AWS Redshift is relatively easy to use within the AWS ecosystem. It provides a familiar SQL interface, and thanks to its close integration with AWS services like S3, Glue, and Lambda, users can build data pipelines and analytics workflows within the broader AWS environment. However, Redshift requires manual management of cluster sizing, node provisioning, and performance tuning, which adds complexity compared to Snowflake.

When to Choose Snowflake:

If you need a user-friendly, fully managed environment where SQL-based querying and ease of use are top priorities.

When to Choose Databricks:

If you have a technically skilled team that needs the flexibility of distributed computing and the ability to work with complex machine learning and data science workflows.

When to Choose AWS Redshift:

If you are deeply integrated into the AWS ecosystem and need a manageable, SQL-friendly data warehouse that works seamlessly with AWS services.

Job Orchestration: Managing Data Workflows

Data pipeline orchestration is crucial for automating data workflows, scheduling tasks, and managing dependencies.

Snowflake: Snowflake offers basic orchestration with Tasks and Streams, allowing for the scheduling of SQL-based workflows or triggering them based on certain conditions (such as data changes). Snowflake integrates well with external orchestration tools like Airflow and dbt for more complex workflows.
Databricks: Databricks offers powerful job orchestration through Databricks Jobs and Workflows API. You can orchestrate multi-step workflows directly within the platform, executing notebooks, JAR files, or Python scripts. Delta Live Tables further simplifies pipeline automation by declaratively building and managing data pipelines. Databricks also integrates with Airflow for external orchestration.
AWS Redshift: AWS Redshift doesn’t have native orchestration tools, but it integrates deeply with other AWS services for pipeline management. For example, you can use AWS Glue to handle data transformations and AWS Step Functions or Amazon Managed Workflows for Apache Airflow (MWAA) for complex job orchestration.

When to Choose Snowflake:

If you need basic orchestration for SQL workflows but rely on external tools like Airflow or dbt for more complex orchestration needs.

When to Choose Databricks:

If you require advanced built-in job orchestration for complex data engineering and machine learning workflows.

When to Choose AWS Redshift:

If you're already leveraging AWS services like Glue or Step Functions for job orchestration and pipeline management.

Data Transformation: SQL vs. Spark vs. Redshift Spectrum

The data transformation capabilities of each platform are distinct and suited to different types of workflows.

Snowflake: Data transformation in Snowflake is mostly SQL-based. You can run transformations through SQL queries, create materialized views, or use stored procedures. For more complex transformations, Snowflake integrates with ETL tools like dbt, Matillion, and Fivetran. Snowflake is optimized for structured data transformations.
Databricks: Databricks is built on Apache Spark, allowing for distributed data transformations at scale. It supports transformations in multiple languages (Python, Scala, R) and offers the DataFrame API for powerful, parallel data manipulation. Databricks also integrates with Delta Lake, offering ACID-compliant transactions and ensuring reliability even in large, continuously changing datasets.
AWS Redshift: AWS Redshift offers SQL-based transformations, similar to Snowflake. It also has the Redshift Spectrum

feature, which allows you to query and transform data directly from S3 without having to load it into Redshift, thus extending its capabilities to handle data lake-like scenarios.

When to Choose Snowflake:

If your data transformations are mostly SQL-based and you want simple, fast transformations over structured data.

When to Choose Databricks:

If you need to perform complex, distributed transformations at scale, especially when working with large datasets or real-time streaming data.

When to Choose AWS Redshift:

If you need SQL-based transformations, especially in conjunction with Redshift Spectrum for querying data stored in Amazon S3.

Security and Governance: Managing Data at Scale

All three platforms offer strong security and governance capabilities, with varying degrees of flexibility and integration with cloud services.

Snowflake: Snowflake provides enterprise-grade security with features like role-based access control (RBAC), data encryption, data masking, and integration with SSO/SAML. Snowflake complies with standards like SOC 2, GDPR, and HIPAA, offering a highly secure environment with minimal configuration required by the user.
Databricks: Databricks offers customizable security features such as fine-grained access control, encryption, and audit logging. Security can be tailored to meet specific enterprise needs, making it well-suited for organizations with complex security requirements, especially in environments handling machine learning and big data.
AWS Redshift: Redshift offers security features integrated with AWS Identity and Access Management (IAM), VPCs, encryption at rest and in transit, and auditing with CloudTrail. Redshift is well-suited for AWS-heavy organizations that rely on AWS’s comprehensive security and compliance offerings, including HIPAA, FedRAMP, and SOC.

When to Choose Snowflake:

If you want enterprise-grade security with minimal setup and strong compliance features, especially if you operate across multiple cloud platforms.

When to Choose Databricks:

If you need flexible security features that can be tailored for advanced workflows, especially in environments where large-scale data processing and machine learning are critical.

When to Choose AWS Redshift:

If you are already deeply embedded in the AWS ecosystem and want tight integration with AWS security tools like IAM, CloudTrail, and KMS.

Pricing Model: Consumption vs. VM-based Pricing

Each platform has a distinct pricing model, and cost can be a critical deciding factor depending on your workload.

Snowflake: Snowflake uses a consumption-based pricing model where you are charged separately for compute and storage. Compute resources scale automatically based on workload, and you only pay for what you use. This can result in predictable costs but may become expensive with large-scale compute requirements.
Databricks: Databricks charges based on VM hours used for data processing, with separate pricing for Databricks SQL, interactive clusters, and Delta Lake operations. Databricks' flexible pricing can scale up or down depending on your workload, offering high granularity but requiring careful monitoring to avoid cost overruns.
AWS Redshift: AWS Redshift offers an on-demand pricing model based on the number of hours a node is running, as well as reserved instance pricing for longer-term cost savings. Redshift’s RA3 nodes with managed storage allow you to scale compute and storage separately, similar to Snowflake.

When to Choose Snowflake:

If you prefer a predictable, usage-based pricing model that charges separately for compute and storage, with automatic scaling.

When to Choose Databricks:

If you need flexible, VM-based pricing that can scale up or down based on the complexity of your workloads, especially in machine learning or big data environments.

When to Choose AWS Redshift:

If you're already using AWS and prefer a familiar, node-based pricing model with options for reserved instances and savings plans.

Conclusion: Snowflake, Databricks, or AWS Redshift?

The decision between Snowflake, Databricks, and AWS Redshift depends heavily on your organization’s unique needs:

Choose Snowflake if you’re focused on ease of use, fast SQL-based analytics, and require a fully managed, cloud-agnostic data warehouse.
Choose Databricks if your workloads involve large-scale data processing, machine learning, and advanced data engineering, especially with Apache Spark.
Choose AWS Redshift if your organization is deeply integrated into the AWS ecosystem and needs a scalable data warehouse that works seamlessly with other AWS services.

Each platform is powerful in its own right, and the best choice ultimately depends on your specific use cases, team expertise, and long-term data strategy.

If you have any questions or experiences to share about working with these different types of Data warehousing solution, tell me which one is your favorite to implement and for what kind of data, feel free to drop a comment below!
Looking to supercharge your team with a seasoned Data Engineer? Let’s connect on LinkedIn or drop me a message—I’d love to explore how I can help drive your data success!