How Apache Spark Has Matured In Cluster Computing and Continues To Get Better
Velocity, Variety, Volume, and Veracity are the four things that define data in humongous sizes that are collectively known by the term Big Data. The tact of handling this data, processing it, and analyzing it to draw insights has come to be the most powerful tool for businesses today. The inherent power in Big Data is helping organizations learn more about their target market, their customers’ interests, and predict and forecast the crucial market trends and happenings that could impact their business directly or indirectly. This is the power of Big Data that almost all companies are trying to utilize today. Expectations from Big Data companies have escalated in the recent times because of the increasing demands, and these demands are what led to the birth of Apache Spark.
Apache Spark is a tool that performs various actions like querying and processing large amounts of data and generating analytics that yield results often in the form of statistics and predictions about anything and everything a company wants to know.
Over the years, Apache Spark has matured into the most favorable Big Data analytics platform for its flexibility and scalability. Many other reasons have led developers and enthusiasts in the Big Data space to believe that Apache Spark has the potential to replace Hadoop MapReduce completely.
First, is the exceptional speed that Apache Spark exhibits? If you are a lover of the analytics tool, you know of this- Spark stores data in RAM and not in the disks which substantially helps it reduce the processing time of computations and analytics. Ever since its inception, this is how Apache Spark has matured and become better than all others in its competition-
- Community growth: This is a seemingly trivial reason for Apache Spark’s popularity, but a community plays a vital role in keeping developers and enthusiasts in touch with the technology solution. Apache Spark’s community is a rapidly growing community with about 400 developers from a 100 companies. These developers now manage the open-source community project and bring in the new releases with updates and fixes.
- IBM bet on Apache Spark: Back in 2015, IBM had placed an enormous investment bet ($300 million along with contributions from 3000 researchers at IBM) in the Apache Spark project calling it the most significant open-source project for the next decade. As the project grew and gained importance, only a year later IBM doubled down its investments in the project. The project continued towards maturity, and today we know the Apache Spark as a near real-time, high-performance analytics tool.
- Added and Enhanced SQL functionalities: As Spark grew in popularity and demand, a number of API updates, including adding support for Hive tables were included in the latest release of Apache 2.2.
- Improved algorithms for MLlib and GraphX: Some new algorithms became a part of the two popular Spark components, such as locality sensitive hashing, personalized PageRank, and Multiclass logistic regression. The support for some distributed algorithms was also added in the latest Apache Spark release, such as ALS, Random forest, LDA, Gaussian mixture model, etc.
For Apache spark implementation solution, it is fair to say the platform is maturing every day and any developer wanting to work with Big Data cannot ignore Apache Spark!