What is Spark?
All You Need to Know about Spark in 2021
Apache spark is one of the best next-generation technologies. It is a unified platform to solve many big data problems.
Unlike other traditional technologies, Hadoop and spark framework is Cluster computing processing, as a result, a spark can process a big amount of data as well.
Hadoop runs on top of Java Virtual Machine So it’s platform-independent and runs on top of Windows, Mac, and Linux.
Nowadays it’s a hot cake and huge demand in the IT industry. Apache Spark training is highly recommended for experienced and freshers to get a job easily and quickly.
Prerequisites to learn spark
Core Java and minimum SQL knowledge required to learn Apache Spark.
Apache Spark opensource so it minimizes that cost and optimizes the performance.
If you have Java, Scala, Python, or SQL experience, you can easily implement an Apache spark project.
Is Hadoop mandatory to learn Spark?
In programming languages first C language came into the picture. After a couple of years, the Java language came into the picture. Compare with C, Java has many extra features like OOPS, JVM and other libraries added in Java.
Similarly in big data, first Hadoop came into the picture. After a couple of years, Apache Spark came into the picture. Spark can process streaming data, batch data, machine learning and graphical data. Spark very fast and optimize performance as well.
If you know C language, you can easily understand Java as well. Similarly, if you have Hadoop knowledge, you can easily implement any big data project but Hadoop is not mandatory to implement Spark projects.
Apache Spark is a next-generation technology, for the next ten years allcompanies using spark to solve many bigdata problems. You can implement spark projects using either Java, Scala, or Python languages. If you familiarize those languages, you can easily implement one end-to-end project. Spark by default using Scala language, but if you use python to implement Spark project, it is called Py-Spark.
Training intention, familiarize the production environment. So, to familiarize the production environment, any Cloud (AWS or Azure) knowledge is also highly recommendable. The main reason nowadays, many projects additionally use AWS or Azure.
Hadoop ecosystem vs Spark ecosystems
Few technologies are associated with big data technologies. For example.
Hadoop, Hive, Sqoop, Flume, Oozie, Zookeeper, HBase, and Cassandra are all these technologies associated with Hadoop called the Hadoop ecosystem.
Similarly, Spark-SQL, Spark-streaming Kafka, Nifi, Airflow, and Flink these technologies associated with Spark called Spark ecosystem.
All these technologies are highly recommended for implementing end-to-end big data projects. Cloud, DevOps, and Java, Python experience is extremely useful to implement one end-to-end project.
Spark is not a replacement for any technology. Spark just extension or next generation of existing technologies. Your previous Java, SQL knowledge is useful to implement one big data project.
It means what are you doing using Hadoop MapReduce, Hive, Sqoop, and other ETL (Extract Transform and Load) technologies, everything you can do it using spark. Additionally, all big data technologies are open-source, so it minimizes cost, that’swhy most of the companies looking for Spark training to implement their big data projects.
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