Databricks vs Apache Spark: Key Differences and When to Use Each

Within the big data and analytics space there are two names at the forefront of conversation: Apache Spark and Databricks. While they’re closely related, they serve very different purposes in the data ecosystem. Understanding their core differences is critical for architects, developers, and data engineers looking to build scalable, high-performance data solutions in the cloud. This article takes a look at what each are and a comparison of when to use each.

What is Apache Spark?

Apache Spark is an open-source distributed computing engine designed for big data processing. Developed originally at UC Berkeley’s AMPLab, it is now maintained by the Apache Software Foundation. Spark is known for its speed, scalability, and support for a wide range of workloads including batch processing, streaming, machine learning, and graph analytics.

Key Features of Apache Spark:

• Multi-language support (Python, Scala, Java, R)
• In-memory computing for fast performance
• Extensive libraries: Spark SQL, MLlib, GraphX, and Spark Streaming
• Open-source and can run on any infrastructure (on-prem, cloud, or hybrid)

Spark is a powerful, flexible engine, but deploying and managing it can require substantial operational effort, especially at scale.

What is Databricks?

Databricks is a unified data analytics platform built by the original creators of Apache Spark. It’s a fully managed cloud service that provides an optimized runtime for Apache Spark along with an integrated workspace for data science, machine learning, and data engineering.

Key Features of Databricks:

• Managed Spark clusters with auto-scaling and auto-termination
• Collaborative notebooks with support for multiple languages
• Delta Lake for ACID transactions and scalable metadata handling
• Built-in integration with cloud storage (Azure Data Lake, Amazon S3, Google Cloud Storage)
• Advanced tools like MLflow, Unity Catalog, and job orchestration

Databricks abstracts away much of the complexity of managing Spark infrastructure, allowing teams to focus on building and delivering insights faster.

Side-by-Side Comparison

To better understand how Apache Spark and Databricks differ, it’s helpful to look at a direct, feature-by-feature comparison. Below is a breakdown of the most important aspects—ranging from deployment and usability to collaboration and cost—so you can evaluate which solution aligns best with your organization’s needs.

Feature	Apache Spark	Databricks
Type	Open-source engine	Commercial platform built on Spark
Setup	Manual cluster management	Fully managed, auto-scaling clusters
Deployment	Self-hosted or cloud VMs	Cloud-native (Azure, AWS, GCP)
Ease of Use	Requires engineering expertise	User-friendly UI and notebooks
Cost	Free (infra separate)	Paid platform (includes compute & features)
Advanced Features	Basic Spark libraries	Delta Lake, MLflow, Unity Catalog
Collaboration Tools	Limited	Built-in notebooks with version control
Security & Governance	DIY	Enterprise-grade IAM, RBAC, and auditing

This side-by-side comparison highlights a key theme: Apache Spark provides a powerful foundation, but it requires manual setup and tuning. Databricks builds on that foundation with a polished, cloud-native experience designed to boost productivity and simplify data workflows. Your choice ultimately depends on how much control you want to maintain versus how much convenience you’re seeking.

When Should You Use Apache Spark?

While Databricks offers a managed experience, some organizations may prefer the flexibility and control of working directly with Apache Spark. Whether due to infrastructure preferences, budget constraints, or specific deployment needs, there are scenarios where using raw Spark makes more sense. Below are key situations where Apache Spark stands out as the better fit.

Apache Spark is a great fit for:

• Organizations with in-house DevOps expertise
• Teams needing full control over infrastructure
• On-premise or hybrid cloud environments
• Cost-sensitive projects that can handle infrastructure management

It offers flexibility and control but comes with higher overhead in terms of setup and maintenance.

When Should You Use Databricks?

Databricks is designed to simplify and accelerate data workflows, making it an attractive choice for teams looking to move fast without managing infrastructure. With its fully managed environment, built-in collaboration tools, and advanced features like Delta Lake and MLflow, Databricks is well-suited for modern, cloud-first data projects. Here are some common scenarios where Databricks shines.

Databricks is ideal for:

• Teams that want to accelerate development cycles
• Enterprises working in cloud-native environments
• Projects that require streamlined collaboration between data engineers, analysts, and data scientists
• Use cases that benefit from Delta Lake and MLflow for scalable, reliable machine learning workflows

It’s especially well-suited for companies building modern data lakehouses or adopting Lakehouse architecture.

Conclusion

Apache Spark and Databricks both play vital roles in the modern data landscape, but they cater to different needs and levels of maturity within an organization. Apache Spark is the engine. Databricks is the race car built on top of it. Choosing between them depends on your team’s skills, your infrastructure preferences, and your need for speed and scalability.

If you need the raw power of Spark with full control over deployment, Apache Spark may be the right choice. But if you’re looking for a seamless, collaborative, and production-ready platform, Databricks is the way forward.

Whether you’re building a Spark cluster from scratch or exploring the Databricks ecosystem, make sure your architecture aligns with your long-term data strategy. The right choice today can save you countless hours tomorrow.

Original Article Source: Databricks vs Apache Spark: Key Differences and When to Use Each written by Chris Pietschmann (If you're reading this somewhere other than Build5Nines.com, it was republished without permission.)