Operating as an open source cluster computing framework is the Apache Spark that is aimed at providing an interface for programming whole clusters with absolute fault tolerance and data parallelism. Designed to offer lightning fast cluster computing, the Apache Spark is a reliable, fast and universal mechanism for processing large volumes of data.
Features Of Apache Spark
1. User Friendly
The Apache Spark offers more than 80 high level operators to ensure easy building of apps simultaneously. It can be conveniently used from Java, Python, Scala and R shells.
Hosting an advanced DAG execution engine supporting cyclic flow of data and in-memory computing, the Apache Spark is hundread times faster than Hadoop MapReduce in memory and ten times faster on disk.
While working on one application, Spark allows simultaneous use of MLlib for machine learning, SQL and DataFrames, Spark Streaming and GraphX.
4. Runs On Various Platforms
Hadoop Yarn, Mesos, standalone cluster mode or via Cloud, the Apache Spark is designed to run on all these platforms. It can aid in accessing data sources like the HBase, Cassandra, Tachyon, Hive, HDFS or any Hadoop data source.
Apache Spark As A Transformation Tier
In a bid to match up with the flowing data, applications must be able to stream data with the help of a real time infrastructure that further aids in capturing, processing, analysing and serving this data to humongous number of users. Combining three distributed systems – messaging system, a transformation tier and an operational database create a well structured and real time data pipeline along with an operational analytics.
While the Apache Kafka is a perfect example of messaging system, it is the Apache Spark, which is known to be one of the most sought after transformation tiers. The transformation tier basically, permits manipulation, development and analysis of data before being ready to be used by an application.
What makes Spark a perfect partner for the messaging system, Kafka is its function as a distributed and memory optimised system. Spark consists of a rich set of library and programming interfaces for easing up the processing and transformation of data-
1. Spark SQL
The Spark SQL is a unique module that allows seamless working with structured data by integrating mix SQL queries inside Spark program. This module can be effectively used in Scala, Python, R shells and Java. The Spark SQL and DataFrames give easy access to a range of data sources such as JSON, JDBC, Hive, Avro and Parquet, while even allowing collaboration of these sources. By being highly compatible with Hive front end and metastore, running unmodified Hive queries on existing data becomes possible. Spark SQL adopts the standard connectivity of JDBC and ODBC for making use of business intelligence tools.
2. Spark Streaming
Developing scalable fault tolerant streaming applications is now easier with Spark Streaming that supports Java, Python and Scala. With language integrated API, stream processing and writing streaming jobs is no more difficult. Spark Streaming allows recovery of lost work and operator state without having to alter the codes and it also makes reuse of same code for batch processing, running ad hoc queries on stream state and joining streams against historical data possible.
3. Spark MLlib
This scalable machine learning library from Apache Spark effectively works with Spark API and blends well with NumPy in Python and R libraries, while integrating with Hadoop data source that can assist in easy operation with Hadoop workflows. The Spark MLlib includes high quality algorithms that generate super fast results in comparison to MapReduce. Spark MLlib is also easy to deploy in existing Hadoop clusters or data source.
4. Spark GraphX
For graphs and graph parallel computation, GraphX acts as Apache Spark’s API. Within a single system, the Spark GraphX amalgamates comprehensible graph computation, examining analysis and ETL for easy working with both collections and graphs. With Spark’s basic features like ease of use, flexibility and fault tolerance, the GraphX delivers top notch performance in line with the fastest graph systems popularly used. Moreover, the GraphX comes with a wide variety of choices of growing library of graph algorithms.
These libraries aid in ingesting data from Kafka, filter them to smaller sets of data and then runs the data development operations to generate and deliver a completely filtered data set to a designated data store. In order to facilitate data transformation, Apache Spark hosts a distinct range of operators to bring in greater functionality within a single system. Thus, it is apt as a transformation tier in working with real time pipelines. Since the Spark doesn’t come with a storage mechanism, pairing it with the operational database becomes pertinent.
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