When it comes to cluster computation, Apache Hadoop has been at the top of the charts for companies all over the world. Since the data had a swift turn towards being called big data, Apache Spark has come through to the rescue. Apache Spark development services have experienced a surge in the market with regular stacking of data.
So, what is the role of Apache Spark in handling Big Data? Moreover, what exactly is Apache Spark and how does it work? In this blog, we will go beneath the skin of Spark and dig out all the answers. So, let’s begin!
What Is Apache Spark?
Apache Spark is a database management system used for lightning-fast computing with the help of cluster computation. Spark’s ability to involve cluster computations accelerates the processes involved in computations. Additionally, Spark is capable of implementing additional processes as compared to its predecessor, Hadoop.
The best part about Spark technology is that it allows you to amalgamate with Hadoop and work hand-in-hand. This results in effective solutions presented on your table.
What Are The Issues Resolved By Apache Spark?
If you consider the issues resolved by the Apache Spark, Hadoop’s name will automatically arrive in the picture. So, here are the issues you will no longer face in Spark which you did in Hadoop.
- The major issue that organizations faced during their time with Apache Hadoop was the time it took for each computation process. The good news is that Spark has resolved that issue and even gone beyond that. Due to in-memory cluster computations, Spark will provide 100 times faster speed. Moreover, when you run the processes on disk, Spark will still manage to provide 10 times faster speed.
- With Java, Scala, R, and Python on-board, Spark allows you to implement multiple languages. Now, by resolving this issue, Spark has targeted a broader audience and a larger pool of developers.
- When you look back at Hadoop, it uses MapReduce as its programming model. Undoubtedly, it’s a simple model but has some limitations attached to it. Therefore, Spark has integrated SQL queries, Machine Learning, Streaming Data, and Graph Algorithms alongside Map and Reduce. What it does is provide advanced analytics to the organizations based on their big data.
Use Cases Of Spark
If you still carry a doubt about whether to adopt Apache Spark consulting services in your business model, these use cases will definitely help you achieve a decisive point.
- The most efficient use of Spark has to be streaming big data. With loads and loads of data entering the fray, it becomes difficult to manage and stream the data at the same time. However, with Apache Spark by your end, steaming big data would be effortless. Well, you don’t believe us? Ask, Amazon! They are successfully using Spark to manage and stream their data at the same time.
- Spark can help you set up your marketing campaign, as well. With Machine Learning capabilities, Spark can help you segregate the customers based on their preferences. As we discussed earlier, Spark is well-equipped to process large sets of data, which allows the framework to analyze and generate advanced reports.
- A progressive step towards IoT(Internet of Things), Spark allows Fog Computing in an effortless environment. With a real-time query, complex graph analytics, and machine learning, Spark rises as a solution-driven framework for Fog Computing.
- Companies like Netflix, Uber, and Conviva are already enhancing their content with the help of Apache Spark consulting services. They can quickly analyze the data of their customers and provide solutions based on them. The process results in enhanced customer satisfaction, and of course, improved sales.
How Does Apache Spark Works?
After having an in-depth understanding of the use cases of Apache Spark, it’s intriguing to understand the process behind it. In order to understand the working of Spark, we need to jump into the architecture of the framework. So, here are the components that comprise the architecture of Apache Spark.
1. Apache Spark Core
Spark Core is a base engine that provides support to all other components present in the Spark framework. The component is responsible for in-memory computing, which makes it a crucial component for attaining lightning-fast speed. Additionally, Apache Spark Core also references datasets from internal to external storage memories. This function allows you to easily retrieve the processed data.
2. Spark SQL
Spark SQL invites data abstracts, preferably known as Schema RDD. The new abstraction allows Spark to work on the semi-structured and structured data. It serves as an instruction to implement the action suggested by the user.
3. Spark Streaming
Spark Streaming teams up with Spark Core to produce streaming analytics. Since Spark Core generates the required speed, Spark Streaming ingests data in small batches. These batches go through the RDD transformations to produce a streaming experience for the users.
Machine Learning Library is an altogether separate framework placed just above Apache Spark, due to Spark’s architecture which is memory-based. It helps to generate a lightning-fast processing speed when the computations are performed on the disk. We already discussed that Spark is 10 times faster than Hadoop on the disk, MLib is solely responsible for that!
GraphX is a framework for processing graphs placed above the Spark Core. The component is responsible for producing graphical representations of the computation run by Spark. It uses Pregel Abstraction API in order to model the graphs which are defined by the users.
When all these components are combined together to shape a framework, popularly known as Apache Spark, you tend to enjoy all the use cases discussed in this blog. Spark has covered a huge market with its large presence, and it targets to grow even further. If you are looking for an Apache Spark Development Company to integrate Spark into your business, Ksolves will end your search! Feel free to contact us for more information regarding our services!
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