Benefits of using Apache Spark!

It is globally renowned fact that Apache Spark has become significant and familiar for it providing data engineers and data scientists, a powerful, unified engine which is fast (100 times faster than the Apache Hadoop that is for large-scale data processing) and easy to manage and use. Apache Spark allows data practitioners around the world to find solutions for their machine learning, graph computation, streaming, query conflicts, real-time interactive query processing problems interactively and at a very large scale.

Apache Spark is open-source which incredibly powerful enough to act as a processing engine built around speed, effectiveness, ease of use, performing sophisticated analytics, utilizing JAVA APIs, Scala, Python, R, and SQL. This powerful suite runs programs up to 100 times faster than that of Hadoop MapReduce in memory and 10 times faster on disk. It can be used to build data applications as a library, or to perform ad-hoc data analysis interactively. Spark powers a stack of libraries including SQL, DataFrames, and Datasets, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. You can combine these libraries seamlessly in the same application. As well, Spark runs on a laptop, Hadoop, Apache Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Apache Cassandra, Apache HBase, and S3. Below are some of the important use cases of Apache Spark,

Ability to process Streaming Data

An important learning of Apache Spark from its predecessors is that its power and capabilities to process streaming data. With so many data around the world being processed on a daily basis, it has become essential for companies to be able to stream and analyze it all in real time. And Spark Streaming has the capability to handle this extra workload. Some experts even theorize that Spark could become the go-to platform for stream computing applications, no matter the type. The reason for this claim is that Spark Streaming unifies disparate data processing capabilities, allowing developers to use a single framework to accommodate all their processing needs.

Machine Learning

Spark comes with an integrated framework for performing advanced analytics that helps users run repeated queries on sets of data—which essentially amounts to processing machine learning algorithms. Among the components found in this framework is Spark’s scalable Machine Learning Library (MLlib). The MLlib can work in areas such as clustering, classification, and dimensionality reduction, among many others. All this enables Spark to be used for some very common big data functions, like predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis. Companies that use a recommendation engine will find that Spark gets the job done fast.

Excellent Network Security

Network security is a good business case for Spark’s machine learning capabilities. Utilizing various components of the Spark stack, security providers can conduct real-time inspections of data packets for traces of malicious activity. At the front end, Spark Streaming allows security analysts to check against known threats prior to passing the packets on to the storage platform. Upon arrival in storage, the packets undergo further analysis via other stack components such as MLlib. Thus security providers can learn about new threats as they evolve—staying ahead of hackers while protecting their clients in real time.