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.
- 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: