Big Data Analytics with Spark Training Chennai - Learn from Experts!
Big Data Analytics with Spark Training in Chennai with Big Data
Big Data Analytics with Spark Training
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. Big Data Analytics with Spark Training Chennai .
Big Data Analytics with Spark
Hadoop as a big data processing technology has been around for 10 years and has proven to be the solution of choice for processing large data sets. MapReduce is a great solution for one-pass computations, but not very efficient for use cases that require multi-pass computations and algorithms. Each step in the data processing workflow has one Map phase and one Reduce phase and you’ll need to convert any use case into MapReduce pattern to leverage this solution.
The Job output data between each step has to be stored in the distributed file system before the next step can begin. Hence, this approach tends to be slow due to replication & disk storage. Also, Hadoop solutions typically include clusters that are hard to set up and manage. It also requires the integration of several tools for different big data use cases (like Mahout for Machine Learning and Storm for streaming data processing).
If you wanted to do something complicated, you would have to string together a series of MapReduce jobs and execute them in sequence. Each of those jobs was high-latency, and none could start until the previous job had finished completely.
Spark allows programmers to develop complex, multi-step data pipelines using directed acyclic graph (DAG) pattern. It also supports in-memory data sharing across DAGs, so that different jobs can work with the same data.
Spark runs on top of existing Hadoop Distributed File System (HDFS) infrastructure to provide enhanced and additional functionality. It provides support for deploying Spark applications in an existing Hadoop v1 cluster (with SIMR – Spark-Inside-MapReduce) or Hadoop v2 YARN cluster or even Apache Mesos.
We should look at Spark as an alternative to Hadoop MapReduce rather than a replacement to Hadoop. It’s not intended to replace Hadoop but to provide a comprehensive and unified solution to manage different big data use cases and requirements.