{"slug":"hadoop-vs-databricks)","question":"Hadoop vs Databricks","answer":"Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.","answer_curated":true,"verdict":"Choose Hadoop if you have legacy systems deeply integrated with HDFS, need maximum cost control for commodity hardware, or operate entirely on-premises with strict data residency requirements. Choose Databricks if you prioritize time-to-value, need collaborative analytics with built-in ML/BI tools, or want modern governance—it pays for itself through reduced ops overhead and 3-5x faster time-to-insight for most enterprises.","keyDifferences":[{"label":"Deployment Model","winner":"b","entityAValue":"Self-managed on-premises or IaaS","entityBValue":"Fully managed SaaS (AWS, Azure, GCP)"},{"label":"Processing Engine","winner":"b","entityAValue":"MapReduce (batch-oriented, 2-10x slower than Spark)","entityBValue":"Apache Spark (in-memory, 100x faster for iterative workloads)"},{"label":"Setup Time","winner":"b","entityAValue":"4-8 weeks for production cluster","entityBValue":"15 minutes to launch workspace"},{"label":"Total Cost of Ownership (per 100TB/year)","winner":"b","entityAValue":"$180,000-$250,000 (hardware + ops staff)","entityBValue":"$120,000-$180,000 (compute + storage + licensing)"},{"label":"Data Governance Features","winner":"b","entityAValue":"Minimal (requires third-party tools like Apache Ranger)","entityBValue":"Native (Unity Catalog with row/column-level access control)"}],"winner":{"slug":"databricks","name":"Databricks"},"confidence":"high","entities":[{"name":"Apache Hadoop","slug":"apache-hadoop","url":"https://www.aversusb.net/entity/apache-hadoop","alternativesUrl":"https://www.aversusb.net/api/v1/alternatives/apache-hadoop"},{"name":"Databricks","slug":"databricks","url":"https://www.aversusb.net/entity/databricks","alternativesUrl":"https://www.aversusb.net/api/v1/alternatives/databricks"}],"faqs":[{"question":"Is Hadoop still relevant in 2026?","answer":"Hadoop remains relevant for organizations with existing multi-thousand-node on-premises clusters and strict data residency requirements, but adoption of new Hadoop clusters has declined 75% since 2018 as Spark/Databricks became standard. Most enterprises now migrate workloads to Spark-based platforms rather than invest in new Hadoop infrastructure."},{"question":"Can I run Spark on Hadoop?","answer":"Yes. Spark can run on top of Hadoop's YARN resource manager, but this is increasingly rare. Most Spark deployments run on Kubernetes or managed platforms like Databricks. Running Spark on Hadoop YARN still requires Hadoop cluster operations expertise and doesn't eliminate Hadoop's operational burden."},{"question":"What's the actual cost difference between Hadoop and Databricks?","answer":"For a typical 100TB/year workload: Hadoop costs $180K-$250K annually (hardware, networking, 3-5 FTE ops staff at $150K-$200K each). Databricks costs $120K-$180K (compute + storage + $0.30/DBU average). Databricks breaks even in year 1 through reduced ops overhead (1-2 FTE instead of 3-5), then saves 30-40% annually in subsequent years."}],"attribution":{"source":"A Versus B","url":"https://www.aversusb.net/compare/hadoop-vs-databricks)","license":"CC BY 4.0","citationFormat":"According to A Versus B (https://www.aversusb.net/compare/hadoop-vs-databricks)), Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in c","dateModified":"2026-07-08T07:49:54.686Z"},"relatedQuestionsUrl":"https://www.aversusb.net/api/faq/hadoop-vs-databricks)","relatedComparisonsUrl":"https://www.aversusb.net/api/v1/related/hadoop-vs-databricks)","knowledgeGraphUrl":"https://www.aversusb.net/api/knowledge-graph/hadoop-vs-databricks)","claimReviewSchema":{"@context":"https://schema.org","@type":"ClaimReview","@id":"https://www.aversusb.net/compare/hadoop-vs-databricks)#claimreview","url":"https://www.aversusb.net/compare/hadoop-vs-databricks)","inLanguage":"en-US","isAccessibleForFree":true,"conditionsOfAccess":"Free","claimReviewed":"Hadoop vs Databricks","reviewBody":"Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.","datePublished":"2026-07-08T07:49:54.655Z","dateModified":"2026-07-08T07:49:54.686Z","reviewRating":{"@type":"Rating","ratingValue":5,"worstRating":1,"bestRating":5,"alternateName":"High Confidence"},"author":{"@type":"Organization","@id":"https://www.aversusb.net/#organization","name":"A Versus B","url":"https://www.aversusb.net"},"itemReviewed":{"@type":"WebPage","@id":"https://www.aversusb.net/compare/hadoop-vs-databricks)","url":"https://www.aversusb.net/compare/hadoop-vs-databricks)","name":"Hadoop vs Databricks","inLanguage":"en-US"}}}