Key features of Spark
Developed in AMPLab of University of California, Berkeley, Apache Spark was developed for higher speed, ease of use and more in-depth analysis. Though it was built to be installed on top of Hadoop cluster, however its ability to parallel processing allows it run independently as well.
Let’s take a closer look at the features of Apache Spark –
- Fast processing – The most important feature of Apache Spark that has made the big data world choosing this technology over others is its speed. Big data is characterized by volume, variety, velocity and veracity which needs to be processed at a higher speed. Spark contains Resilient Distributed Dataset (RDD) which saves time taken in reading and writing operations and hence it runs almost ten to hundred times faster than Hadoop.
Check out this insightful video on PySpark Tutorial for Beginners:
- Flexibility – Apache Spark supports multiple languages and allows the developers to write applications on Java, Scala, R, or Python. Equipped with over 80 high-level operators this tool is quite rich from this aspect.
- In-memory computing – Spark stores the data in the RAM of servers which allows him to access it quickly and in turn accelerating the speed of analytics.
- Real-time processing – Spark is able to process real-time streaming data. Unlike MapReduce which processes the stored data, Spark is able to process the real-time data and hence is able to produce instant outcomes.
- Better analytics – Contrasting to MapReduce that includes Map and Reduce functions, Spark includes much more than that. Apache Spark consists of a rich set of SQL queries, machine learning algorithms, complex analytics, etc. With all these functionalities, analytics can be performed in a better fashion with the help of Spark.