Databricks vs. Redshift: Data Platform Comparison

Databricks and Redshift are two powerful data management solutions that offer unique features and capabilities for organizations looking to analyze and process large volumes of data. While both platforms are popular choices for enterprise data processing, they differ in their approach and strengths.

Redshift and Databricks provide the volume, speed, and quality demanded by business intelligence (BI) applications. But there are as many similarities as there are differences between these two data leaders. Therefore, selection often boils down to platform preference and suitability for your organization’s data strategy:

• Databricks: Best for real-time data processing and machine learning capabilities.
• AWS Redshift: Best for large-scale data warehousing and easy integration with other AWS services.

Databricks vs. Redshift: Comparison Chart

Databricks Overview

Databricks is a unified analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data and machine learning projects. It is built on top of Apache Spark, an open-source data processing engine, and offers several tools and services to simplify and accelerate the development of data-driven applications.

Databricks is well-suited to streaming, machine learning, artificial intelligence, and data science workloads — courtesy of its Spark engine, which enables use of multiple languages. It isn’t a data warehouse: Its data platform is wider in scope with better capabilities than Redshift for ELT, data science, and machine learning. Users store data in managed object storage of their choice and don’t get involved in its pricing. The platform focuses on data lake features and data processing. It is squarely aimed at data scientists and highly capable analysts.

Databricks Key Features

Databricks lives in the cloud and is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework, which makes management of infrastructure easier. Some of Databricks’ defining features include:

Auto-Scaling and Auto-Termination

Databricks automatically scales clusters up or down based on workload demands, optimizing resource usage and cost efficiency. It can also terminate clusters when they are no longer needed, reducing idle costs. This feature is particularly beneficial for companies with fluctuating workloads or those looking to optimize cloud costs.

MLflow

Databricks MLflow simplifies the machine learning lifecycle by providing tools to manage the end-to-end ML process—from experimentation to production deployment and monitoring. Data science teams in various industries benefit from MLflow for reproducibility, collaboration, and operationalizing machine learning models.

Delta Lake

Databricks Delta Lake provides reliable data lakes with ACID transactions and scalable metadata handling. It allows for more efficient data management and streamlines data engineering workflows. Companies dealing with large-scale data processing and analytics, especially those with real-time data needs, find Delta Lake valuable. It’s often used in industries like finance, healthcare, and retail.

Databricks Pros and Cons

Databricks offers some great strengths, including its ability to handle huge volumes of raw data, and and its multicloud approach – the platform interoperates with the leading cloud providers. However, a challenge for some: the platform is geared for advanced users; many use cases require real expertise.

Pros

• Databricks uses a batch in-stream data processing engine for distribution across multiple nodes.
• As a data lake, Databricks’ emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.
• The platform can be used for raw unprocessed data in large volumes.
• Databricks is delivered as software as a service (SaaS) and can run on AWS, Azure, and Google Cloud.
• There is a data plane as well as a control plane for back-end services that delivers instant compute.
• Databricks’ query engine is said to offer high performance via a caching layer.
• Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

Cons

• Some users, though, report that it can appear complex and not user-friendly, as it is aimed at a technical market and needs more manual input for resizing clusters or configuration updates.
• There may be a steep learning curve for some.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It allows users to analyze large amounts of data using SQL queries and BI tools to gain insights. Major AWS users would be best on Redshift due to better integration with the entire Amazon ecosystem.

AWS Redshift Key Features

Redshift positions itself as a petabyte-scale data warehouse service that can be used by BI tools for analysis. Some of its best features include:

Columnar Storage and Massively Parallel Processing

Amazon Redshift uses columnar storage and MPP architecture to deliver high performance for complex queries on large datasets. It’s optimized for analytics workloads. Redshift is designed for scalability and performance, making it suitable for enterprises processing terabytes to petabytes of data.

Integration with AWS Ecosystem

Redshift seamlessly integrates with other AWS services like S3, Glue, and IAM, simplifying data ingestion, transformation, and security management within the AWS cloud. Companies heavily invested in the AWS ecosystem and those looking for a fully managed data warehousing solution often choose Redshift.

Concurrency Scaling

Redshift’s concurrency scaling functionality automatically adds and removes query processing power in response to the workload, ensuring consistently fast query performance even during peak usage. This capability is essential for businesses with unpredictable query patterns or those needing consistent performance under heavy loads, such as during business intelligence reporting.

AWS Redshift Pros and Cons

Redshift certainly benefits from being a product of the powerful AWS platform – it offers enormous scalability, and provides a long list of services. However, in some instances it can be expensive, and it doesn’t support all types of semi-structured data.

Pros

• Redshift scales up and down easily.
• Amazon offers independent clusters for load balancing to enhance performance.
• Redshift offers good query performance — courtesy of high-bandwidth connections, proximity to users due to the many Amazon data centers around the world, and tailored communication protocols.
• Amazon provides many services that enable easy access to reliable backups for Redshift datasets.

Cons

• Some users noted that Redshift can sometimes be complex to set up and use at times and ties up more IT time on maintenance due to lack of automation.
• A lack of flexibility in areas, such as resizing, can lead to extra expense and long hours of maintenance.
• It lacks support for some semi-structured data types.

Databricks vs. Redshift: Support and Ease of Implementation

Databricks offers an array of support of advanced use cases, while Redshift tends to be more user friendly.

Databricks

Databricks offers a variety of support options that can be used for technical and developer use cases:

• Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms.
• It comes with its own user interface as well as ways to connect to endpoints, such as Java database connectivity (JDBC) connectors.

Redshift

Amazon Redshift is said to be user-friendly and demands little administration for everyday use:

• Setup, integration, and query running are easy for those already storing data on Amazon S3.
• Redshift supports multiple data output formats, including JSON.
• Those with a background in SQL will find it easy to harness PostgreSQL to work with data.

Support and Implementation Winner: Redshift

This category is close, although Redshift is the narrow winner. The platform benefits from its support by AWS. The platform offers relatively accessible ease of implementation.

Databricks vs. Redshift: Integration

Databricks in some cases calls for third party solutions to integrate certain tools, while Redshift is of course a top choice for existing AWS customers.

Databricks

Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. Databricks supports any format of data, including unstructured data. But it lacks the vendor partnership depth and breadth that Amazon can muster.

Redshift

Obviously, those already committed to the AWS platforms will find integration seamless on Redshift with services like Athena, DMS, DynamoDB, and CloudWatch. The level of integration within AWS is excellent.

Integration Winner: It Depends

Redshift wins in this category, if a company is an AWS client. Obviously, the fact that Redshift is an integral part of the AWS platform helps in this category. In contrast, Databricks integrates with all the major cloud providers (including AWS, of course) and is used by multicloud clients – it clearly is not AWS-dependent.

Databricks vs. Redshift: Pricing

Pricing can vary considerably based on use case: Databricks can be pricey for users who require consultant help, and Redshift charges by the second if daily allotment is exceeded. This category is practically a toss-up.

Databricks

Databricks takes a different approach to packaging its services. Compute pricing for Databricks is tiered and charged per unit of processing, with its lowest paid tier starting at $99 per month. However, there is a free version for those who want to test it out before upgrading to a paid plan.

Databricks may work out cheaper for some users, depending on the way the storage is used and the frequency of use. For example, consultant fees for those needing help are said to be expensive.

Redshift

Redshift provides a dedicated amount of daily concurrency scaling. But you get charged by the second if it is exceeded. Customers can be charged an hourly rate by type and cluster nodes or by amount of byte scanning. That said, Redshift’s long-term contracts come with big discounts.

Roughly speaking, Redshift has a low cost per hour. But the rate of usage will vary tremendously depending on the workload. Some users say Redshift is less expensive for on-demand pricing and that large datasets cost more.

Pricing Winner: Redshift

This is a close one, as it varies from use case to use case, but Amazon Redshift gets the nod.

The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements before making a purchasing decision.

Databricks vs. Redshift: Security

Like pricing, this category is a close call. Both platform are focused on security.

Databricks

Databricks provides role-based access control (RBAC), automatic encryption, and plenty of other advanced security features. These features include network controls, governance, auditing and customer-managed keys. The company’s serverless compute deployments are protected by multiple layers of security.

Redshift

Redshift does a solid job with security and compliance. These features are enforced comprehensively for all users.

Additionally, tools are available for access management, cluster encryption, security groups for clusters, data encryption in transit and at rest, SSL connection security, and sign-in credential security. These tools enable security teams to monitor network access and traffic for any irregularities that might indicate a breach.

Access rights are granular and can be localized. Thus, Redshift makes it easy to restrict inbound or outbound access to clusters. The network can also be isolated within a virtual private cloud (VPC) and linked to the IT infrastructure via a virtual private network (VPN).

Security Winner: Tie

Both platforms do a good job of security, with strong compliance and monitoring tools, so there is no clear winner in this category.

Who Shouldn’t Use Databricks or AWS Redshift?

Who Shouldn’t Use Databricks 

• Small businesses with minimal data needs: For small businesses with relatively simple data processing and analysis requirements, Databricks may be overly complex and expensive.
• Companies not leveraging cloud platforms: Databricks is tightly integrated with major cloud platforms like AWS, Azure, and GCP. If an organization prefers on-premises solutions or has strict data residency requirements that limit cloud adoption, Databricks may not be the best fit.
• Limited use cases: If the primary focus is on traditional data warehousing and analytics without extensive machine learning or data engineering needs, simpler tools like traditional SQL-based data warehouses might be more suitable.

Who Shouldn’t Use Redshift

• Non-AWS cloud users: Although Redshift is tightly integrated with AWS services, organizations using other cloud providers like Azure or Google Cloud Platform might face challenges in terms of interoperability and data transfer costs when considering Redshift.
• Small-scale or start-up companies: Redshift, being a powerful data warehousing solution, may not be cost-effective for smaller businesses with limited data volumes and budget constraints.

2 Top Alternatives to Databricks & AWS Redshift

Google Cloud Dataproc

Google Cloud Dataproc is a managed Apache Spark and Hadoop service offered by Google Cloud Platform. Similar to Databricks, it provides a fully managed environment for running Spark and Hadoop jobs. However, unlike Databricks, Google Cloud Dataproc supports a broader range of open-source big data tools beyond Spark, such as Hadoop, Hive, and Pig.

Snowflake

Snowflake is a cloud-based data warehouse solution that offers similar capabilities to Redshift. It is known for its simplicity, scalability, and separation of storage and compute. Snowflake automatically handles infrastructure management, scaling, and performance optimization, making it easier to use compared to Redshift.

How We Evaluated Databricks vs. AWS Redshift

To write this review, we evaluated each tool’s key capabilities across various data points. We compared their features, ease of implementation, support, pricing, and integrations to help you determine which platform is the best option for your business.

Our analysis found that Databricks and Redshift tie for features and security, the integration category is a toss-up, and Redshift tops for ease of implementation and pricing – though pricing can vary of course based on utilization.

Bottom Line: Databricks and AWS Redshift Use Different Approaches 

In summary, Databricks wins for a technical audience, and Amazon wins for a less technically savvy user base. Databricks provides pretty much all of the data management functionality offered by AWS Redshift. But it isn’t as easy to use, has a steep learning curve, and requires plenty of maintenance. Yet it can address a wider set of data workloads and languages. And those familiar with Apache Spark will tend to gravitate towards Databricks.

AWS Redshift is best for users on the AWS platform that just want to deploy a good data warehouse rapidly without bogging down in configurations, data science minutia, or manual setup. It isn’t nearly as high-end as Databricks, which is aimed more at complex data engineering, ETL (extract, transform, and load), data science, and streaming workloads. But Redshift also integrates with various data loading and ETL tools and BI reporting, data mining, and analytics tools. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and more will certainly make it attractive to developers in those camps.