Why Hadoop with Apache Spark Matters!
Hadoop — the popular data processing framework — becomes all the more useful when better performing components get connected to the same. Certain shortcomings of the Hadoop platform, for instance, the MapReduce component that’s reputed to be slow for effective real-time data analysis, can be handled well by the integration of Apache Spark with Hadoop. This powerful Hadoop-based data processing tool works well with streaming and batch workloads alike. It is outfitted with striking features to help Hadoop handle all the tasks that it is being pushed into in the current and competitive scenario.
Here, it deserves mention that Spark runs atop existing Hadoop clusters for providing additional and enhanced functionality. Take a look.
Key Advantages of Apache Spark
Apache Spark adds the features of unprecedented speed; yes, it enables applications linked with Hadoop clusters to run up to 10 times faster on disks, and up to 100 times faster in memory.
Also, its ease-of-use allows for quick writing of applications in SCALA, Java, or Python. The sophisticated analytics of Apache Spark are well-designed for streaming data, supporting SQL queries, and dealing with complex data structures (also consider checking out this perfect parcel of information for data science degree).
The other impressive features of Apache Spark include:
Hadoop Integration – Spark works effectively with files stored in HDFS.
Spark’s Interactive Shell –Written in SCALA, Spark boasts of its very own version of SCALA interpreter.
Spark’s Analytic Suite –It has many tools for large-scale graph processing, interactive query analysis, and real-time analysis.
Resilient Distributed Datasets (RDD’s) – These are distributed objects that are equipped with the features of cached in-memory located across a cluster of diverse compute nodes. These are the main data objects used by Spark.
Distributed Operators – Along with MapReduce, many other operators may be used with RDDs.
Why Apache Spark with Hadoop Matters?
Built on Top of HDFS
Apache Spark fits well with the many features and modules of Hadoop’s open-source community. As it is built on top of HDFS-- Hadoop Distributed File System, it offers up to 100 times faster performance metrics than Hadoop’s certain applications. More so, it is not tied to the two-stage paradigm of MapReduce.
Compatible with Machine Learning Algorithms
Apache Spark offers primitives for in-memory, fast and reliable cluster computing. It allows user programs to query and load data into the cluster’s memory repeatedly (Here's the perfect parcel of information to learn data science).
Alternative for MapReduce
Spark executes jobs in small bursts of micro-batches, which are usually five seconds (or less) apart. Offering far more stability than stream-oriented, real-time Hadoop frameworks like Twitter Storm, this software is capable of being used for diverse jobs like ongoing analysis of real-time data and execution of other computationally in-depth tasks that involves higher levels of graph processing and machine learning.
Spark allows developers to write data-analysis codes in Scala, Java or Python; all with the help of over 80 high-level operators.
Runs Much Faster than Hadoop’s Data-processing Platform
Oft referred to as the “Hadoop Swiss Army knife,” Spark offers the abilities of creating data-analysis processes that are designed to operate about 100 times faster than others running on standard MapReduce and other Apache Hadoop applications. In the recent past, MapReduce has been serving as a bottleneck in Hadoop clusters because it executes jobs in the batch mode. This effectively means that it does not permit real-time data analysis in any way. Apache Spark comes to Hadoop’s rescue in this case too.
The libraries linked with Spark are especially designed for complementing the kinds of processing jobs that are now being aggressively explored with innovative, new, commercially supported Hadoop deployments. Spark Streaming is enabled for high-speed data processing that’s ingested from various sources. In addition, the features of GraphX allow for computations on graph-centric data (also consider checking out this career guide for data science jobs).
Apache Spark’s version 1.0 brings to the front a stable and reliable API (application programming interface).It allows developers to interact with Spark via their own applications. For instance, with this feature in place, Storm can be integrated with in Hadoop based deployments, easily.
SPARK SQL Components
Spark SQL components for accessing structured data related to Hadoop and other compatible platforms allows data to be queried alongside unstructured data, especially in case of analysis work. In addition, Spark SQL also allows for SQL-like queries that are capable of being run on data stored in the Apache Hive. The extraction of data from Hadoop, with the aid of SQL queries, is yet another variant of smart real-time querying that has sprung around Hadoop.
Apache Spark and Hadoop [HBASE, HDFS and YARN]
Apache Spark is totally compatible with HDFS (Hadoop’s Distributed File System) and other components like YARN (Yet Another Resource Negotiator) as well as HBase distributed databases.
Industry Adopters of Apache Spark
Cloudera, Intel, MapR Pivotal, IBM and many other IT companies of high repute have encompassed Spark into their Hadoop programs and stacks. Databricks, an organization founded by certain developers of Spark, offers commercial support for this software. Today, NASA and Yahoo!, among others, are using Spark for their daily data operations.
Everything that Spark has on its cards is proving to be a big draw for commercial vendors and users of Hadoop. Companies looking towards implementing Hadoop, along with those who are already in the fray, are manning analytics systems centered on Hadoop for their real-time processing systems. Overall, Spark offers a variety of functionality for supporting or building proprietary items around Hadoop. The implementation of Spark by top companies lays credence to the fact that it is a highly successful alternative, particularly as far as real-time processing is concerned.
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