Big Data Journal

Apache Spark is an open source, in-memory data processing engine. It was originally developed in 2009, at University of California, AMP lab, which was later donated to Apache Software Foundation.  Apache Spark is quickly gaining momentum not only in the headlines but also in real world adoption. Today, it has grown to the extent that, customers from all the industries are using it to improve their businesses with the HDP. Clients are detecting patterns and proving insight which is changing some facets of life and driving organizational change.  Apache Spark has grown to become the largest open source community for large data with its own in-memory processing framework. There are various engines within Hadoop system. Every engines works best for certain cases. Here are some of the uses for Apache Spark: Fog Computing and Apache Spark: Internet of Things- This concept sparks the tech community’s imagination. The Internet of Things embeds device with sensors and c

First of all, it is important to know that the old and the conventional MapReduce model is still being used for certain batch computing activities. Nevertheless, there are some specific tasks that cannot be carried out with the help of MapReduce and it is only because of this reason that Google has recently stopped using MapReduce and has started with the use of Cloud Dataflow. For example, Google web search index updating activity is dealing with a huge amount of data but it requires incremental updated on a constant basis. As per reports, Google has come up with an incremental computing mechanism known as Percolator for carrying out this huge data activity. It is important to note that Google has come up with a streaming computing mechanism known as Milwheel for carrying out low-latency computing jobs. Different services such as Google Map views now depend on Milwheel. One thing that is worth noting is that the applications of Google are not created from, no-where.