Spark- The Best Technology For Data Processing

Apache spark for data processing

Spark is a non-proprietary and issued processing system applied for huge data workloads. It employs caches and optimized inquiry performance for fast questions against any size data. It has a faster approach for working with big data including MapReduce. The processing of Spark is faster as it runs on RAM. It has integrated libraries that execute algorithms of machine learning on jobs of MapReduce which also makes parallelizing easy across various computer resources. 


Spark can be used for various purposes such as data streams, graphs, machine learning algorithm execution, inserting data in the database, data pipelines creation, and managing distributed SQL. 


 Why is Spark used for data processing?


After the launch of Spark, many data processors and users were drawn to its ability to execute ETL work and data engineering on very big as well as unstructured datasets. When used in data processing, it offers various advantages such as:


1) Speed- Specially designed for performance, Spark is 100 times quicker than Hadoop for large-scale data processing by exploiting computing memory and numerous optimization. When data is saved on disk, Spark is fast. Currently, it has marked its place in the world record for large-scale on-disk sorting. 


2) Easy to Use- It hosts user-friendly APIs for working on a huge database. It is integrated with more than 100 operators collection for modifying data and similar data structure APIs for shaping semi-structured data. 


3) Allied Engine- Spark is integrated with high-level libraries such as graph processing, machine learning, streaming data, and SQL queries support. These conventional libraries enhance the productivity of the developer and can also be seamlessly linked to design complicated workflow


Features of Spark-


The features of Spark that makes it the most popular technology are mentioned below:


1) It supports various machine languages and enables the developer to compose applications in Phyton, R, Scala, and Java. 


2) Spark comprises a strong collection of SQL queries, complex analytics, and machine learning which enables the user to perform better analytics. 


3) It can process real-time data and give immediate results. 


4) The data in Spark is stored in the server's RAM. This enables quick access and also hastens the speed of analytics. 


5) One of the very important features of Spark that makes it an amazing choice is its speed. Its RDD, Resilient Distributed Dataset saves time in writing and reading operation which makes it 100 times faster than Hadoop to run. 




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