Online Course Apache Spark and Scala Certification Training
The Apache Spark and Scala training course I recommend here for you provide details of fundamentals of real-time analytics and need of distributed computing platform, it will also explain Scala and it features further it will enhance your knowledge on the architecture of Apache Spark. The course will also explain the process of installation and running applications using Apache Spark further it will enhance your knowledge of performing SQL and streaming and batch processing, finally, it will explain machine learning and graph analytics on the Hadoop data.
Fundamental knowledge of any programming language is prerequisite for the course, it would be great to have a basic understanding of any database, SQL and query language for databases. Working knowledge of Linux or Unix based system is an advantage for this course although it not mandatory.
I will show a comparison between batch and real-time processing in case the enterprise use cases. In the case of batch processing, a large amount of data or transactions is procced in a single run over some time. "The associate jobs generally run entirely without any annual intervention additionally, the entire data is pre-selected and fed using command line parameters and scripts." In typical cases, it uses to execute multiple operations and handle the heavy data load, reporting, and offline data workflow, for example, generate daily or hourly reports for decision making.
On the other hand, real-time processing takes place upon data entry or command receipt instantaneously, it must execute on response time within stringent constraints the example is fraud detection.
Let's have a look at the Spark history
Spark has started in UC Berkely lap Matei Zaharia Before ten years ago in 2009, and 2010 it became open source under a BSD License. The project was donated to Apache Software company which changed its license to Apache 2.0 in 2013, in February 2014 Spark became an Apache top-level project then in November of the same year it used by the engineering team at DataBricks to set a world record in large scale sorting, now DataBricks provides commercial supports and provide certification for it.
In this time Spark exists as the next generation real-time and batch processing framework, that was the journey of Spark.
Limitations of MapReduce in Hadoop
MapReduce used in Hadoop is not suitable for many reasons such as not a good choice when it comes to real-time processing it is batch-oriented, because of which it's excited jobs that take time to process the data and provide results it takes minutes to complete a job which mainly depends on data amount and number of the nodes in the cluster. MapReduce is also not fitting for writing trivial operations like as "filter and joins" to write operations like this you might need to rewrite the jobs using MapReduce framework it becomes complex because of the key-value pattern, this pattern is required to be followed and reducer and map records, in addition, MapReduce doesn't work well wilt large data on a network it works on data locality principle and hence works well on a node with a data actually resigns, however, its not a good option when need to process a lot of data requiring shuffling over the network the reason is that it'll take a lot of time to copy the data.
An important point of the structure is that every application as its executor processes which run tasks in multiple threads and stay until the duration of the whole application. While it helps in scheduling and executor sides by separating applications it also means that without writing to an exterior storage system you can’t share data across applications of Spark.
Another feature is the agonistic behavior of Spark to the cluster manager underline, till Spark can obtain executor process and these can connect it's comparatively relaxed to run it even on a cluster manager supporting other application. The driver program demands to listen and accept the connections coming from the executors all the time, I mean the driver program have to be accessible to the network to be addressed by the working nodes. Tasks are scheduled by the driver on the cluster that's why it has to run to the worker nodes in proximity if it possible on the same local network to send remote requests to the cluster it should be opening RPC to the driver and let it submit operations from the neighborhood this better from running a driver far from through the worker nodes.
Watch the video here or skip to the online course