Apache Foundation released Spark 1.6 on January 4th, 2016. The latest version of Apache Spark offers better memory management and performance enrichments for faster processing of Parquet data format. Improved Parquet performance is likely to enhance the overall performance for streaming state management in Apache Spark. The latest version of Spark will speed up the overall performance of Apache Spark when working with existing Hadoop systems.
Mtell’s prescriptive and predictive analytics platform -Mtell PreviseTM has been reviewed to execute with the incorporation of all Apache Spark open source elements. Mtell is extending its machine learning platform to Apache Spark to achieve extreme performance and simplify applications for IIOT. Data analysts and data scientists with expertise skills in Scala, Python and R programming can enhance the Mtell Previse platform by deploying custom algorithms, custom business logic and calculations.
According to a recent survey on “Hadoop Perspective for 2016” by Syncsort, 70% of the respondents to the survey exhibited interest in deploying Apache Spark framework in 2016 over other compute frameworks because of its compute performance and the flair for interactive, streaming and other analytics capabilities. Apache Spark allows companies leverage novel big data platforms without having to replacing the existing big data tools or learn new skills.
With a more diverse and sophisticated set of Big Data to handle, businesses are struggling to keep up with timely and relevant data insights through Big Data solutions. With the Apache Spark 1.6 platform, ClearStory helps businesses with un-restricted data discovery and free-form data exploration.
Google recently made an announcement that it would be submitting its dataflow data processing technology to Apache Software Foundation. Google’s Dataflow data processing technology can handle both stream and batch processing of large datasets. The dataflow technology will come under the authority of Apache project along with Apache Flink and Spark runners.
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In 2010, Hadoop was the heart of the big data industry and Spark was just a research project at University of California at Berkeley. Today, Spark is an integral part of all the big data conversations happening across the globe. With many business banging straight on Spark and foregoing Hadoop- Is Spark going to replace Hadoop is the big “Big Data” question of 2016.
Spark 2015 Year In Review. Databricks.com, January 5, 2016
2015 saw an exponential growth in the enterprise adoption of Apache Spark with 1000 developers contributing code to the Spark Community when compared to 500 in 2014.2015 was a noteworthy year for Apache Spark in big data with major developments in platform API’s , Performance optimizations through Project Tungsten, Spark Streaming and Machine Learning API’s for Data Science.
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