Big Data Training With Spark And Hadoop
Big Data has made its way onto the market so does various processing engines that run various analytics on this data to generate quicker and wiser insights for decision making. Apache Spark is one such processing engine that is open source and is built to serve analytics in quick and easy to use format. Spark has become the choicest of processing engines due to its in-memory data processing, and it excels where engines like MapReduce flunk when high-speed processing is required. Latency in processing data may result in superannuation of derived insights as Big Data grows rapidly it requires speedy analytics to match its pace. Apache Spark comes with a built-in high-level library for data streaming, machine learning, SQL queries and graph analysis. It can process a constant stream of low latency data and has thus become an obvious choice for organisations. There are no prerequisites to Spark deployment as it is compatible with Hadoop and can work on top of the Hadoop Distributed File System (HDFS). It can be deployed on either a Standalone server or a distributed framework like YARN or Mesos and provides fault tolerance and data parallelism along with programming interface in the data cluster itself. Given the advantage of speed, cost and compatibility over other engines Spark have been predicted to dominate the Big Data landscape by 2022 according to Wikibon. As organisations prefer Apache Spark engine over other engines, this has amplified the demand of Spark developers in the market. According to Indeed.com Average pay of a Spark, the developer is around 108,366 USD per annum. Unfortunately, there is an insufficient number of professionals that can fill these roles. IT professionals have a good chance of leveraging this gap by being an early bird in acquiring Apache Spark certification and training that is structured and ordinated as per business requirements and best practices. Students or professionals interested in Spark training can opt for Hadoop- Spark Certification or Apache Spark certification training from any reputed institute. With proper learning tracks. To go for Hadoop-Spark certification course, individuals need to have basic knowledge of SQL and databases like filters, aggregates, joins, rank, etc. to comprehend the course. Spark already has inbuilt high-level libraries so creating workflows is easier and requires less coding, so you don’t need to be an expert in programming to get through the Spark training. Also, since Spark is compatible with Java, Scala, Python, R, and many more programming languages that are easy to learn, individuals are not restricted to just one language and get to choose the language they prefer. Most course providers, however, do touch upon basic programming concepts in python and scale, and as long as you understand loops in general programming, you will get through the course smoothly. Working knowledge of Linux- or Unix-based systems is good to have, additional certification training in Big Data Hadoop as a developer is highly recommended by employers for those who are going for Spark training.