APACHE SPARK VS. HADOOP: WHICH BIG DATA FRAMEWORK IS THE BEST FIT?

Organizations can use the right tools with Hadoop for their Big Data Strategy

A common question that organizations looking to adopt a big data strategy struggle with is – which solution might be a better fit, Hadoop vs. Spark, or both? To help answer that question, here’s a comparative look at these two big data frameworks.

You can learn more about Hadoop and Spark in the blog.

What is Hadoop?

• It is an open-source software
• S distributed file system
• A MapReduce execution engine
• It stores manages and processes very large data sets in parallel across distributed clusters of commodity servers.

Hadoop Features

• Flexibility – Handles multiple Data Formats
• Scalability – Accommodate small and large workloads
• Affordability – a Real Steal
• However, MapReduce and Batch jobs are slow. Changing Industry requirements make it obsolete.

What is Apache Spark?

• Spark is a scalable open-source Hadoop execution engine designed for fast and flexible analysis of large multiple-format data sets.
• Spark can manipulate data in real time, allowing for fast, interactive queries that finish within seconds.

Spark on Hadoop supports:

• SQL Queries
• Streaming Data
• Machine Learning
• Graph Algorithms
• and combines seamlessly into a single workflow

Big Data Strategy

Hadoop has evolved into a universal framework that supports multiple models like:

• Spark
• Pig
• Avros
• Cassandra
• Zookeeper
• HBASE
• MAHOUT