Is Apache worth learning?

Yes, its worth learning Apache Hadoop in 2019 but you have to learn the Apache Spark along with it. Still companies are facing problems to hire Big Data professionals. If you are looking to change your career to ML, AI and Data Science it will help you on other side to understand the data processing.

Should I learn Spark for data science?

Learning Spark can Make Your Life Easy as a Data Scientist Machine learning is an iterative process that needs fast processing. Spark’s in-memory data processing makes that possible and along with below features creates a compelling platform for operational as well investigative analysis for data scientists.

Is Spark easy to use?

In many respects, Spark delivers on its promise of easy-to-use, high-performance analysis on large datasets. However, Spark is not without its quirks and idiosyncrasies that occasionally add complexity.

What are the prerequisites for taking up Apache Spark?

Now, to answer your question, There are no prerequisites for taking up Apache Spark. Though, basic knowledge of database, SQL and query language can help as a foundation to build your career in Spark. Since Spark has APIs in all the above 4 languages, it is not mandatory that you must Java only.

What are the prerequisites for Learning Spark?

Prerequisites for spark are. Spark program can be written in any one of the languages. , Programmer. Java, Scala,Python, Ruby, C, C++, No, Knowing Java is not a prerequisite for learning Spark. Spark provides API’s in Java, Python and Scala.

Why should I learn Apache Spark instead of Java?

The reason is, Apache spark allow most of the functional programming concepts and methods as well like map, flatmap etc.., Apache spark provides API in three languages ( Java, Scala, python). If you have any basic Java knowledge then it’s enough to learn spark if not learning Scala would be better choice than Java.

What is spark in Hadoop?

Apache Spark Tutorial. Apache Spark is a lightning-fast cluster computing designed for fast computation. It was built on top of Hadoop MapReduce and it extends the MapReduce model to efficiently use more types of computations which includes Interactive Queries and Stream Processing.