Apache Spark – the next big thing for Big Data

The world today is more digital than it’s ever been before, and it’s only going to get even more digital with time. There’s a humongous amount of data being generated and handled on a day-to-day basis, and since the amount of data being churned is increasing, the processing power and architecture need to keep up.

Enter the Apache Spark; the offering from the old-time market leader. If you buy into the ideology that the Apache Spark consulting company, Active Wizards follows, it is the one tried-and-tested ace in your deck when there’s so much data to be worked upon and there are so many promising technologies being released on a daily basis. So, let’s dive into what Apache Spark brings to the table for you.

What is Apache Spark?

If the mention of a cloud computing solution doesn’t excite you anymore, wait till we tell you that it’s based on a cluster arrangement. Yes, that’s one of most powerful pegs of this idea. Apache Spark is the next level of computing that overtook the success story, Hadoop, especially since it’s based on a more powerful workflow than Hadoop’s backbone (MapReduce). As a result, it does more calculations in less time. This is achieved by a workflow that goes like this:

1. Fetch data from cluster
2. Perform analysis tasks in one go
3. Feed the data back to the cluster
4. Let the nodes take control thereon

But wait, that’s not the best part. Spark brings to life several interesting concepts that you might not have heard of before:

1. REPL (it’s shell system), which enables the user to test the results of a single line of code without having to first lay down the entire job; this makes isolated computing a breeze.
2. RDD, short for Resilient Distributed Dataset, brings the ability to compute sets of objects in parallel; thus, guaranteeing speed when you need it the most. Everything is done when it’s really needed.
3. The driving heart and soul of Spark architecture is named Spark Care. It brings together multiple features that cloud computing has been known for all along, such as the ability to mitigate faults in data computing, and batch scheduling of jobs to be handled by the cluster; making it easier to get more done in less time, and with a robust system that handles the operations with storage solutions.Spark core essentially brings multiple libraries that can perform different functions under one umbrella. Let’s talk on a first-name basis:
   1. Spark SQL – can handle both SQL and Hive operations.
   2. Spark streamlining – batches of data are streamed, processed, and fed out for being published/used.
   3. MLlib – provides freedom to integrate different algorithms to get cluster-based computing benefits on machine learning-based applications.

Apache Spark has opened up a new frontier in the market; creating opportunities for extensive data processing in real-time. There are integration challenges and many questions about how to move from older and existing systems to it, but the efficiency it guarantees and the money that it’s going to save is going to be a good reason to invest in it.