Compare Hadoop vs. Spark vs. Kafka for your big data strategy
Big data became popular about a decade ago. The falling cost of storage led many enterprises to retain much of the data they ingested or generated so they could mine it for key business insights.
Analyzing all that data has driven the development of a variety of big data frameworks capable of sifting through masses of data, starting with Hadoop. Big data frameworks were initially used for data at rest in a data warehouse or data lake, but a more recent trend is to process data in real time as it streams in from multiple sources.
A big data framework is a collection of software components that can be used to build a distributed system for the processing of large data sets, comprising structured, semistructured or unstructured data. These data sets can be from multiple sources and range in size from terabytes to petabytes to exabytes. Such frameworks often play a part in high-performance computing (HPC), a technology that can address difficult problems in fields as diverse as materials science, engineering or financial modeling. Finding answers to these problems often lies in sifting through as much relevant data as possible. The most well-known big data framework is Apache Hadoop. Other big data frameworks include Spark, Kafka, Storm and Flink, which are all — along with Hadoop — open source projects developed by the Apache Software Foundation. Apache Hive, originally developed by Facebook, is also a big data framework.
What are the advantages of Spark over Hadoop? The chief components of Apache Hadoop are the Hadoop Distributed File System (HDFS) and a data processing engine that implements the MapReduce program to filter and sort data. Also included is YARN, a resource manager for the Hadoop cluster. Apache Spark can also run on HDFS or an alternative distributed file system. It was developed to perform faster than MapReduce by processing and retaining data in memory for subsequent steps, rather than writing results straight back to storage. This can make Spark up to 100 times faster than Hadoop for smaller workloads. However, Hadoop MapReduce can work with much larger data sets than Spark, especially those where the size of the entire data set exceeds available memory. If an organization has a very large volume of data and processing is not time-sensitive, Hadoop may be the better choice. Spark is better for applications where an organization needs answers quickly, such as those involving iterative or graph processing. Also known as network analysis, this technology analyzes relations among entities such as customers and products.
What is the difference between Hadoop and Kafka? Apache Kafka is a distributed event streaming platform designed to process real-time data feeds. This means data is processed as it passes through the system.
