Advanced Big Data Analytics using Apache Spark Ecosystem!

Apache Spark managed to provide several advantages over any other big data technologies such as Hadoop and MapReduce. It offers more functions and comes with optimized arbitrary operator graphs. There are many other advantages such as the following,

• Optimization overall data processing workflow
• Concise and reliable APIs in Scala, Java, and Python
• Interactive shell assigned for Scala and Python
• Additional capabilities in Big Data analytics and Machine Learning areas

In addition to the functionalities offered by core APIs of Apache Spark, it enables advanced big data analytics in its ecosystem with the help of various additional support to several other big data applications.

Spark Streaming

Being at the heart as a batch-mode processing framework, Apache Spark extends its ability to offer a streaming mode that constantly stores data in “micro-batches,” efficiently providing streaming support for applications that do not require low-latency responses. The Spark distribution ships with support for streaming data from Kafka, Flume, and Kinesis. This Spark Streaming mode can be utilized for processing the real-time streaming data. This is depending on the micro batch style of computing and processing. It basically makes use of the DStream which is basically a series of RDDs, to process the real-time data.

MLLib

MLLIb library is an addition to the core Spark APIs that brings various machine learning algorithm to be explored and implemented with Spark for off-the-shelf use by data scientists, including Naive and Multi- nominal Bayesian models, clustering, collaborative filtering, and dimension reduction. MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.

GraphX

GraphX is also a crucial APIs provided by the Apache Spark ecosystem which enables graph algorithm support for Apache Spark, including a parallelized version of PageRank, triangle counts, and the ability to discover connected components. GraphX is the new (alpha) Spark API for graphs and graph-parallel computation. At a high level, GraphX extends the Spark RDD by introducing the Resilient Distributed Property Graph: a directed multigraph with properties attached to each vertex and edge. To support graph computation, GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregateMessages) as well as an optimized variant of the Pregel API. In addition, GraphX includes a growing collection of graph algorithms and builders to simplify graph analytics tasks.

Spark SQL (formerly known as Shark)

Apache Spark SQL library offers most of the fundamental and uniform access to several different structured data sources such as Apache Hive, Avro, Parquet, ORC, JSON, JDBC/ODB, etc. It allows the data scientist to develop SQL queries that can be executed across the Apache Spark cluster, and to collaborate these data sources without the need for complicated ETL pipelines. Apache Spark SQL provides the exceptional capabilities to expose various Apache Spark datasets over JDBC API and allow running the SQL-like queries on Spark data using traditional BI and visualization tools. Apache Spark SQL allows the business to implement ETL functions on their Big Data from different formats it’s currently in (like JSON, Parquet, a Database), transform it, and expose it for ad-hoc querying.