What Is Apache Spark? How Has Spark Helped Big Data Take a Step Forward in the Future?

Apache Spark is a data processing engine. It is most commonly used for large data sets. Apache Spark often called just ‘Spark’, is an open-source data processing engine created for Big data requirements. It is designed to deliver scalability, speed, and programmability for handling big data for machine learning, artificial intelligence, streaming data, and graph data applications.

Apache Spark has gained much popularity in a short span. Its analytic’s engine can process data almost 100 times faster than its competitors. Its lightning-fast in-memory cluster computing feature boosts the processing speed of your application.

Let’s read on to learn more about Apache Spark. In this article, we will dive deep into the intricacies of Apache Spark- its evolution, features, components, and working.

Spark was first developed at UC Berkley in 2009. Now, it is handled & owned by Apache Software Foundation. Apache Spark has the largest open source community in big data. It has more than 1000 contributors. Now, its also being included as one of the core components in several commercial extensive data offerings.

The Hadoop framework is based on an easy-to-understand programming model - MapReduce. Map Reduce is best known for its scalability, flexibility, cost-effectiveness, and fault-toleration. Before Apache Spark, industries were using Hadoop extensively for analyzing their data.

The biggest concern with this model was speed. Hadoop could not handle big data, and the wait time between queries and run-time was immense. Apache spark resolves this issue by running big data at a lightning-fast speed.

Apache Spark is based on Hadoop MapReduce. It ensures better productivity by extending the efficiency model of MapReduce. Against the common belief - Apache Spark is not a modified or updated version of Hadoop. It is not dependent on Hadoop as it has its cluster management system.

Apache spark follows hierarchical master/slave architecture. The Spark driver is the master node in charge of the cluster manager. It is further responsible for managing the worker nodes (slave). Together they deliver data results to the application.

The answer received from the application code is handled by Spark Driver, generating SparkContext. The SparkContext works with a cluster manager to monitor and distribute execution across all nodes. In the process, Resilient Distributed Datasets are also created. These Datasets are the reason for Spark’s excellent processing speed.

RDDs are a fundamental part of Apache Spark. As mentioned above, they are why Sopar’s data processing speed is so remarkable. RDDs are a fault-tolerant group of elements that are distributed between different nodes of a cluster. The operations are run on these clusters parallelly, and the load is distributed.

Apache Spark has gained much popularity in the Big data community. It also boasts of a strong community with more than 100 members. Here is a list of features that set Apache Spark aside from all the other Big data processors.

Spark is super fast, and that is no secret. Spark facilitates applications to run in the Hadoop cluster up to 100x faster in memory and 10x quicker while running on a disk. Spark makes this possible by decreasing the number of reads and write operations on the disk. Spark stores the intermediate steps in memory.

Much popularity of Spark can be attributed to its versatility also. Along with Map & Reduce, Spark supports Machine Learning, Artificial Intelligence, SQL queries, and Graph Algorithms.

Contrary to common belief, Spark provides built-in APIs in many languages, including Python, Java, and Scala. So, you can write your application in any language, and Spark will help you out with speedy data processing.

Now that you understand Spark’s evolution and its features, let's look at how it can be built on Hadoop Components.

With the standalone deployment, you can statistically allocate resources to Spark and Hadoop Distributed File System (HDFS) explicitly. In this case, Spark and MapReduce run parallelly to handle all Spark jobs efficiently.

In Hadoop Yarn deployment, Spark runs on Yarn without pre-installing any application or setup. This helps Spark function atop Hadoop Ecosystem.

Spark in Map Reduce is used to launch spark job and standalone deployment. In SIMR deployment, you don't need any administrative access.

These are the three ways Spark can be deployed as a top Hadoop distributed file system. Now let us understand what the different components of Spark are.

As the name suggests, Spark core is the engine upon which all the operations run. It is the core on top of which all the functionalities are built. The core facilitates all in-memory computing and dataset referencing by using external storage systems.

Spark SQL - as shown in the image above, is a component that works on top of Spark Core. It is responsible for introducing new data abstraction or schemaRDD. This, in turn, provides support for semi-structured and structured data sets.

Streaming needs to be on the go and fast. Spark Streaming does exactly that by using Spark core's fast scheduling capabilities. It performs streaming analytics by inserting small batches of data and applying RDD to it.

Spark architecture has a distributed memory that helps in machine learning frameworks and functionalities. Spark MLlib is 9x faster than the Hadoop disk-based version of Apache Mahout.

GraphX also works on top of Spark Core, a distributed graph processing framework. It helps by providing an API for showing graph computation. It also provides an optimized and highly efficient runtime for abstraction.

Apache Spark has shown tremendous promise with its fast speed with big data processing. It boasts of lightning-fast cluster computing and a large community of over 1000 developers. Spark supports many functions like Machine learning, Artificial intelligence, SQL queries, etc. It also has a wide range of libraries to help you handle your data handling needs.