Hadoop vs Spark vs Kafka: How They Differ, How They Fit Together, and Where Databricks and Hive Come In

The short answer

Hadoop and Spark are not straight alternatives. Hadoop is a platform: HDFS stores the data, YARN schedules work across the cluster, and MapReduce is its original batch engine. Spark is a faster, more general processing engine that commonly runs on YARN and reads from HDFS, or runs on Kubernetes against object storage. Kafka is something else again: a platform for moving and storing streams of events, which Spark can read from and write to. The real question in most estates is whether to keep MapReduce jobs, move processing to Spark, and keep or retire HDFS and YARN underneath.

Hadoop vs Spark vs Kafka at a glance

What Hadoop is

The Apache Hadoop project has four modules. Hadoop Common holds shared utilities. HDFS is a distributed file system for high-throughput access to application data. YARN is a framework for job scheduling and cluster resource management. MapReduce is a YARN-based system for parallel processing of large data sets: map tasks run in parallel over chunks of input, the framework sorts their output, and reduce tasks combine it, with input and output kept in the file system.

In practice, HDFS and YARN often carry other engines, such as Hive, HBase and Spark, alongside or instead of hand-written MapReduce; CDH and HDP clusters ship several of them together. Many of those clusters are on lines the community no longer releases: 3.2 was declared end of life in December 2023, 3.3 has had no release since June 2023, and Cloudera's CDH and HDP distributions left vendor support between 2020 and 2022. Our Hadoop end-of-life guide lists every line.

What Spark is

Apache Spark describes itself as a multi-language engine for data engineering, data science and machine learning on single machines or clusters. It handles batch and streaming in one API and runs SQL, Python, Scala, Java and R. It has no storage of its own: it reads from HDFS or any Hadoop-supported file system, including S3. It needs a cluster manager, and supports its own standalone manager, Hadoop YARN and Kubernetes.

The design point that separates Spark from MapReduce is memory. Spark can persist a data set in memory and reuse it across operations, which helps iterative work such as machine learning and interactive queries. If a cached partition is lost, Spark recomputes it from the transformations that created it. MapReduce, by contrast, writes results to the file system between stages.

Spark maintains feature releases for six months and the last minor of each major line for eighteen months. The 3.5 line has an extended period to November 2027 with security fixes only, and 4.5.0 is planned as the 4.x long-term line.

What Kafka is

Apache Kafka is a distributed event streaming platform. Producers publish events to topics, Kafka stores them durably and replicates them across brokers, and consumers subscribe and read them. Events are not deleted when read; each topic keeps them for as long as you configure. It is the transport and buffer between systems, not an analytics engine. Spark's Structured Streaming reads data from and writes data to Kafka, so a common pipeline lands events in Kafka, processes them with Spark, and writes results to a data lake on HDFS or object storage.

Where Hive and Databricks fit

Apache Hive is a SQL data warehouse system over distributed storage, with HiveServer2 for JDBC and ODBC clients and the Hive Metastore as the catalogue of tables and partitions. Spark SQL can read and write Hive tables and talk to Hive metastores from version 2.0.0 to 4.1.0, so Spark often shares the metastore with Hive rather than replacing it. Our Hive vs Trino comparison covers the SQL engines that sit on that metastore.

Databricks was founded in 2013 by the original creators of Apache Spark, Delta Lake and MLflow. It sells a managed data and AI platform built around Spark and a lakehouse design. It is a commercial service, not a project, so the comparison with Hadoop is really one between running an open source platform yourself and buying a managed one.

How to choose

• Keep Hadoop when HDFS holds data you cannot move cheaply, YARN already schedules your workloads, and Hive or HBase depend on it. Patch the platform and modernise the engines on top.
• Move processing to Spark for new batch and streaming work, machine learning and anything iterative. Spark on YARN runs on the cluster you have; Spark on Kubernetes with object storage is one path off HDFS.
• Add Kafka when systems need to exchange events in real time, or when you need a durable buffer between producers and processing.
• Choose Databricks when you would rather pay for a managed Spark platform than run clusters yourself, and the lock-in and cost suit you.

For stream processing specifically, our Flink vs Spark comparison covers the other main engine.

Where OSSeva fits

OSSeva supports Hadoop, Hive and Kafka. It does not support Spark or Databricks. OSSeva for Apache Hadoop ships patched, signed builds for Hadoop 2.8 to 3.3 and for CDH 5 and 6 and HDP 2.6 and 3.1, and patches the ZooKeeper that NameNode and ResourceManager failover depend on, so Spark jobs on YARN keep running on a patched platform. OSSeva for Apache Hive patches Hive 1.2, 2.3 and 3.1, and OSSeva for Kafka covers the Kafka version you run. Each is priced per cluster, under one contract. See Hadoop extended support and CDH and HDP end of life, or book a discovery call for a quote.

Frequently asked questions

Hadoop vs Spark vs Kafka: what is the difference?

Hadoop stores data in HDFS, manages cluster resources with YARN and runs batch jobs with MapReduce. Spark processes data in batch and streaming and can keep it in memory, running on YARN, Kubernetes or its own cluster manager. Kafka moves and stores streams of events between systems. A typical stack uses all three.

Is Spark replacing Hadoop?

Spark covers the batch work MapReduce was built for, adds streaming, SQL and machine learning, and runs on YARN, so MapReduce jobs can move to Spark without leaving the cluster. Spark does not replace HDFS or YARN. Where a Hadoop cluster is retired, object storage and Kubernetes can take over those roles.

Spark vs Databricks: what is the difference?

Spark is an open source Apache project you can run anywhere. Databricks is a commercial managed platform from the company founded by Spark's original creators, with Spark at its core and its own services around it.

Hadoop vs Hive: what is the difference?

Hadoop is the platform: storage, resource management and MapReduce. Hive is a SQL data warehouse that runs on that platform, with a metastore that catalogues tables and HiveServer2 for SQL clients. Hive needs storage and an execution engine; Hadoop provides them.

Spark vs Hive: which should I use?

Hive for SQL access to warehouse tables through HiveServer2 and BI tools. Spark for data engineering and machine learning in Python, Scala or SQL. They often share the Hive Metastore, so many clusters run both.

Does Spark need Hadoop?

No. Spark can run with its standalone manager or on Kubernetes and read from object storage. It uses Hadoop-compatible file system support to read HDFS and S3, and runs on YARN when a Hadoop cluster is already there.