Apache Spark Redefining Big Data Processing
The above video is the recorded session of the webinar on the topic “Big Data Processing with Apache Spark and Scala”, which was conducted on 21st August’14.
Managing Big Data is one of the most challenging tasks. There are several cluster computing platforms that have come up in the recent past to confront the rising big data challenges. On such in the league, Apache Spark is an open-source cluster computing framework for Hadoop community clusters. It is the most preferred framework for real-time data processing. Initiated by AMP Lab at UC Berkeley, and consummated by Apache Software Foundation, Apache Spark has been written in Scala, and has leveraged in-memory, as well as batch processing capacities in a rising fashion.
It qualifies to be one of the best data analytics and processing engines for large-scale data with its unmatchable speed, ease of use, and sophisticated analytics. Following are the advantages and features that make Apache Spark a crossover hit for operational as well as investigative analytics:
- The programs developed over Spark run 100 times faster than those developed in Hadoop MapReduce.
- Spark compiles 80 high-level operators.
- Spark Streaming enables real-time data processing.
- GraphX is a library for graphical computations.
- MLib is the machine learning library for Spark.
- Primarily written in Scala, Spark can be embedded in any JVM-based operational system, at the same time can also be used in REPL (Read, Evaluate, Process and Load) way.
- It has powerful caching and disk persistence capabilities.
- Spark SQL allows it proficiently handle SQL queries
- Apache Spark can be deployed through Apache Mesos, Yarn in HDFS, HBase, Cassandra, or Spark Cluster Manager (Spark’s own cluster manager).
- Spark simulates Scala’s functional style and collections API, which is a great advantage to Scala and Java developers.
Need for Apache Spark:
Spark is rendering immense benefits to the industry in terms of speed, variety of tasks it can perform, flexibility, quality data analysis, cost-effectiveness, etc., which are the needs of the day. It delivers high-end, real-time big data analytics solutions to the IT industry, meeting the rising customer demand. Real-time analytics leverages business capabilities to heaps. Its compatibility with Hadoop makes it very easy for companies to quickly adopt it. There is a steep need for Spark-learned experts and developers, as this is a relatively new technology, which is being increasingly adopted.
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