Apache Spark and Hadoop are two of the most popular frameworks used for big data analysis, computing, and processing. Both the software are quite popular and those who want to learn more about big data processing and analysis would definitely like to know which one to pursue and learn earlier.
Hadoop is the Fundamental of the Two
Hadoop is the more basic and fundamental of the two. It can teach you the basics and fundamentals of big data theory and practice. Hence you may prefer to learn Hadoop first as you will be able to start from the very beginning and will have your concepts clear towards big data processing.
Spark is the More Modern one
Spark on the other hand is the newer of the two. It is lightning fast and accords APIs of advanced level. Spark is as much as 100 times faster when compared to Hadoop, and can also access disk data at 10 times greater speed. Apache has its own in-memory and hence it can store intermediate data easily thereby making processes faster. Hadoop, on the other hand, depends on the disk for reading and writing and hence is slower. So if you want to learn the more modern software then Spark should be your choice.
In terms of popularity and usage, there are various trends to notice. Both Spark and Hadoop are flagship big data analysis products and have been created by the Apache Software Foundation. While Spark has around 10,000 installations so far, the number of Hadoop installations are more than 50,000. Hence Hadoop has been favorite for the last 5 years.
However, there is also another side of the picture. While Spark is the new entry, its demand has skyrocketed in the past 3 years. A survey that focused on the installation rates revealed that Spark installations increasing @ more than 45% in comparison to Hadoop's 14%.
Simplicity Towards Learning
Spark is a technologically advanced program and easy to learn. As there are a great number of RDD or Resilient Distributed Datasets equipped operators available, programming is easy at spark. Hadoop's MapReduce requires that the programmer and developer hand code all the operations, and hence it may be difficult towards working and learning. While you may want to learn the basics of Hadoop first, Spark may the future.
Ease of Management
Spark can be said to be a comprehensive framework for Data Analytics as it can be used for machine learning, streaming, and performing batches. Therefore when you use Spark you need not manage the various components towards each of the requirements. All the requirements can be easily handled by installing Spark on the cluster. On the other hand, Hadoop MapReduce only has the batch engine. Hence Hadoop users have to depend on the other engines including Impala and Storm etc for the different requirements. It can be very difficult for a user to manage the multiple components while using Hadoop.
While it is not mandatory to learn Hadoop before you start to learn Spark certification, basic knowledge of the HDFS framework and Hadoop will help you learn Spark better. Spark is a new and emerging technology and is getting increasingly popular. Spark professionals get a higher salary in comparison to those who have limited themselves to learning Hadoop. Hence a better and cautious approach will be to learn Hadoop first and then master Spark.
It is now easy and convenient for you to pursue The Spark and Hadoop certification in your free time. Leading and reputed online education services help you enroll for the certifications online and provide hands-on exposure, instructor-led lecture sessions and workshops, practice tests and other resources that help you excel in the exam.