Hadoop Spark Training Mumbai – Learn from Experts with Hands On!
Hadoop Spark Training in Mumbai with Big Data
Hadoop Spark Training
Hadoop is an ecosystem of open source components that fundamentally changes the way enterprises store, process, and analyze data. Unlike traditional systems, Hadoop enables multiple types of analytic workloads to run on the same data, at the same time.
Apache Spark is a cluster computing platform designed to be fast and general-purpose.
On the speed side, Spark extends the popular MapReduce model to efficiently support more types of computations, including interactive queries and stream processing. Speed is important in processing large datasets, as it means the difference between exploring data interactively and waiting minutes or hours. One of the main features Spark offers for speed is the ability to run computations in memory, but the system is also more efficient than MapReduce for complex applications running on disk. Hadoop Spark Training in Mumbai .
Is Apache Spark going to replace Hadoop?
Hadoop is parallel data processing framework that has traditionally been used to run map/reduce jobs. These are long running jobs that take minutes or hours to complete. Spark has designed to run on top of Hadoop and it is an alternative to the traditional batch map/reduce model that can be used for real-time stream data processing and fast interactive queries that finish within seconds. So, Hadoop supports both traditional map/reduce and Spark.
We should look at Hadoop as a general purpose Framework that supports multiple models and We should look at Spark as an alternative to Hadoop MapReduce rather than a replacement to Hadoop.
Difference between Hadoop Mapreduce and Apache Spark:
Spark stores data in-memory whereas Hadoop stores data on disk. Hadoop uses replication to achieve fault tolerance whereas Spark uses different data storage model, resilient distributed datasets (RDD), uses a clever way of guaranteeing fault tolerance that minimizes network I/O.For details see the UC Berkeley’s link Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing.
From the Spark academic paper: “RDDs achieve fault tolerance through a notion of lineage: if a partition of an RDD is lost, the RDD has enough information to rebuild just that partition.” This removes the need for replication to achieve fault tolerance.