Spark and Hadoop are big data frameworks, but they don’t serve the same features. Spark is a data processing tool that works on data collections and doesn’t do distributed storage. Hadoop, on the other hand, is a distributed infrastructure, supports the processing and storage of large data sets in a computing environment.
In order to have a glance on difference between Spark vs Hadoop, I think an article explaining the pros and cons of Spark and Hadoop might be useful.
Let’s jump in:
A fast engine for large data-scale processing, Spark is said to work faster than Hadoop in a few circumstances. It doesn’t have its own system to organize files in a distributed ways. Its big claim to fame is real time data processing compared to batch processing engine. It is basically a cluster-computing framework, which signifies that it completes more with MapReduce than the whole Hadoop ecosystem.
- Perfect for interactive processing, iterative processing and event steam processing
- Flexible and powerful
- Supports for sophisticated analytics
- Executes batch processing jobs faster than MapReduce
- Run on Hadoop alongside other tools in the Hadoop ecosystem
- Consumes a lot of memory
- Issues with small file
- Less number of algorithms
- Higher latency compared to Apache fling
Reasons to learn Spark
2017 is the time to learn spark and upgrade your skills. Developers earn highest average salary among other experts using the most popular development tools. Some of the other reasons are:
- Opens up various opportunities for big data exploration and making it easier for companies to solve various kinds of big data issues
- Organizations are on the verge of hiring huge number of spark developers
- Provides increased data processing speed compared to Hadoop
- Professionals who have experience with Apache spark can earn the highest average salaries
A framework that enables for distributed processing of large data sets using simple programming models, Hadoop has emerged as a new buzzword to fill a real need that arose in companies to analyze, process and collect data. It is resilient to system faults since data are written to disk after every operation. Hadoop is comprised of the various modules that work together to create the Hadoop framework. Some of the Hadoop framework modules are Hive, YARN, Cassandra and Oozie.
Also Read>> Top Hadoop Interview Questions & Answers
- Cost effective
- Processing operation is done at a faster speed
- Best to be applied when a company is having a data diversity to be processed
- Creates multiple copies
- Saves time and can derive data from any form of data
- Can’t perform in small data environments
- Built entirely on java
- Lack of preventive measures
- Potential stability issues
- Not fit for small data
Reasons to learn Hadoop
With the use of Hadoop, Companies can store all the data generated by their business at a reasonable price. Even, professional Hadoop training can help you meet the competitive advantage. Some reasons to learn Hadoop so that experts can exploit the lucrative career opportunities in the big data market.
- Brings in better career opportunities in 2017
- An exciting part of the big data world to meet the challenges of the fast growing big data market
- The job listings on sites like indeed.com show the increased demand for Hadoop professionals
- Hadoop is an essential piece of every organization’s business technology agenda
Many experts argue that spark is better than Hadoop or Hadoop is better than spark. In my opinion, both are not competitors. Spark is used to deal with data that fits in the memory, whereas Hadoop is designed to deal with data that doesn’t fit in the memory.