Fast Big Data Processing with Spark Training Bangalore – Learn from Experts!
Fast Big Data Processing with Spark Training in Bangalore with Big Data Analytics
Fast Big Data Processing with Spark Training
what is Apache Spark
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. Fast Big Data Processing with Spark Training Bangalore .
Fast Big Data Processing with Spark
Spark started out of our research group’s discussions with Hadoop users at and outside UC Berkeley. We saw that as organizations began loading more data into Hadoop, they quickly wanted to run rich applications that the single-pass, batch processing model of MapReduce does not support efficiently. In particular, users wanted to run: u More complex, multi-pass algorithms, such as the iterative algorithms that are common in machine learning and graph processing u More interactive ad hoc queries to explore the data Although these applications may at first appear quite different, the core problem is that both multi-pass and interactive applications need to share data across multiple MapReduce steps (e.g., multiple queries from the user, or multiple steps of an iterative computation). Unfortunately, the only way to share data between parallel operations in MapReduce is to write it to a distributed filesystem, which adds substantial overhead due to data replication and disk I/O. Indeed, we found that this overhead could take up more than 90% of the running time of common machine learning algorithms implemented on Hadoop. Spark overcomes this problem by providing a new storage primitive called resilient distributed datasets (RDDs). RDDs let users store data in memory across queries, and provide fault tolerance without requiring replication, by tracking how to recompute lost data starting from base data on disk. This lets RDDs be read and written up to 40× faster than typical distributed filesystems, which translates directly into faster applications.
Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to get/present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.