Applications of Apache Spark

Since the time of its inception in 2009 and its conversion to an open-source technology, Apache Spark has taken the Big Data world by storm. It has become one of the largest open-source communities that includes over 200 contributors. The prime reason behind its success is its ability to process heavy data faster than ever before.
Spark is a widely used technology adopted by most of the industries. Let’s look at some of the prominent Apache Spark applications:

  • Machine Learning: Apache Spark is equipped with a scalable Machine Learning Library called MLlib that can perform advanced analytics such as clustering, classification, dimensionality reduction, etc. Some of the prominent analytics jobs like predictive analysis, customer segmentation, sentiment analysis, etc., make Spark an intelligent technology.
  • Fog computing: With the influx of big data concepts, IoT has acquired a prominent space for the invention of more advanced technologies. Based on the theory of connecting digital devices with the help of small sensors, this technology deals with a humongous amount of data emanating from numerous sources. This requires parallel processing, which is certainly not possible on Cloud Computing. Therefore, Fog computing, which decentralizes the data and storage, uses Spark Streaming as a solution to this problem.
  • Event detection: The feature of Spark Streaming allows organizations to keep track of rare and unusual behaviors for protecting the systems. Institutions, such as financial, security, and health organizations, use triggers to detect potential risks.
  • Interactive analysis: Among the most notable features of Apache Spark is its ability to support interactive analysis. Unlike MapReduce that supports batch processing, Apache Spark processes data faster, because of which it can process exploratory queries without sampling.

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Along with Apache Spark applications, now, check out some of the most popular companies that are utilizing various applications of Apache Spark:

  • Uber: Uber uses Kafka, Spark Streaming, and HDFS for building a continuous ETL pipeline.
  • Pinterest: One of the successful web and mobile application companies, Pinterest uses Spark Streaming in order to gain deep insight into customer engagement details.
  • Conviva: The pinnacle video company, Conviva deploys Spark for optimizing the videos and handling live traffic.

I hope you liked this section of the Apache Spark tutorial on Apache Spark Applications. In the next section, you will learn how to download and install Spark. Happy learning!

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