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Databricks vs Apache Spark: What’s the Difference?
Confused between Databricks and Apache Spark? This presentation breaks down the key differences, similarities, and real-world use cases of both powerful big data tools. Learn how Spark forms the engine, while Databricks simplifies, manages, and scales Spark in the cloud. Ideal for data engineers, analysts, and tech professionals exploring the right platform for modern data processing, machine learning, and analytics. ✅ Covers: What is Apache Spark? What is Databricks? Feature-by-feature comparison Use cases Deployment & cost insights When to use which
Databricks vs Apache Spark: What’s the Difference?
- 1. Databricks vs ApacheSpark Understanding the Tools PoweringBig Data Analytics PAGE 1
- 2. Introduction Big Dataanalytics is evolving rapidly. Spark and Databricks are two major technologies in this space. Let’s explore their relationship, differences, and use cases. PAGE 2 +91-96400 01789 contact@accentfuture.com
- 3. What is ApacheSpark? PAGE 3 •Open-source distributed data processing engine. •Designed for big data analytics and machine learning. Key Features: •In-memory computation •High performance •Supports Java, Scala, Python, R •Components: Spark Core, Spark SQL, MLlib, GraphX, Spark Streaming +91-96400 01789 contact@accentfuture.com
- 4. What is Databricks? PAGE4 •Unified data analytics platform built by Spark creators. •Cloud-based, commercial product. Integrates: •ApacheSpark •Delta Lake •MLflow •Collaborative notebooks •Available on Azure, AWS, and GCP. +91-96400 01789 contact@accentfuture.com
- 5. Spark vs Databricks-Quick Summary PAGE 5 Feature Apache Spark Databricks Type Open-source engine Cloud-based platform Developer Apache Software Foundation Original Spark creators Ease of Use Requires setup and management Fully managed & optimized UI/UX CLI-based or third-party tools Rich notebooks & visual interface Integration Manual (Hadoop, Hive, etc.) Pre-built (Delta, MLflow, etc.) +91-96400 01789 contact@accentfuture.com
- 6. Key Differences Deployment: • Spark:Self-hosted on Hadoop, Kubernetes, etc. • Databricks: Managed cloud service. Performance: • Databricks optimizes Spark with Photon engine, caching, and auto-scaling. User Experience: • Databricks provides collaborative notebooks, version control, job scheduling. PAGE 6 +91-96400 01789 contact@accentfuture.com
- 7. Apache Spark Use Cases PAGE7 •Batch processing in Hadoop ecosystems •ETL pipelines •Data lake processing •ML pipelines (with MLlib or custom libraries) +91-96400 01789 contact@accentfuture.com
- 8. Databricks Use Cases PAGE 8 •Real-timeanalytics & dashboards •Unified data lakes with Delta Lake •Advanced ML with built-in MLflow •Team collaboration in notebooks •Enterprise-level governance and security +91-96400 01789 contact@accentfuture.com
- 9. Cost & Licensing PAGE9 •Apache Spark: •Free to use, open-source •Costs tied to infrastructure and maintenance •Databricks: •Subscription-based •Pay-as-you-go or reserved instances on cloud platforms +91-96400 01789 contact@accentfuture.com
- 10. When to UseWhat PAGE 10 Scenario Best Choice Custom, on-prem big data infrastructure Apache Spark Cloud-first, collaborative data teams Databricks Real-time ML + Governance Databricks DIY, fine-tuned control Apache Spark +91-96400 01789 contact@accentfuture.com
- 11. Coexistence PAGE 11 •Databricks runson Apache Spark. •They are not competitors but complementary. •Many teams start with Spark and migrate to Databricks for scalability. +91-96400 01789 contact@accentfuture.com
- 12. Summary PAGE 12 •Apache Spark:Engine powering big data. •Databricks: Cloud-native platform to simplify and scale Spark. •Choose based on: •Deployment preference •Skillset •Team collaboration •Budget +91-96400 01789 contact@accentfuture.com
- 13. Thank You PAGE 13 📧contact@accentfuture.com 🌐 AccentFuture 📞 +91-96400 01789 DATABRICKS ONLINE TRAINING +91-96400 01789 contact@accentfuture.com