What is Spark Framework?

(Newswire.net — May 7, 2020) — “Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway”      –    Geoffrey Moore, American Management Consultant and Author.

We live in a world of Big Data today. Big Data analytics is all about a complex process of examining large and varied data sets, or Big Data, in order to uncover information from it, such as market trends and customer preferences, unknown correlations, hidden patterns or any other useful insights, so that it can help organizations in making informed business decisions.

Big Data Analytics is driven by specialized analytics systems and software and high powered computing systems. Big Data Analytics is important for businesses because it helps data analysts and data scientists in analyzing growing volumes of structured transaction data and other forms of data. 

Application of Big Data Analytics significantly reduces costs as it reduces the need for storing huge amounts of data and helps in finding out new or improving business functionalities. With the speed of Big Data Analytics tools, businesses are able to analyze information quickly, eventually making better and quick decisions.

Big Data Analytics helps in determining the customer’s requirements and satisfaction, resulting in better production and meeting their needs.

There are many tools available in the market for Big Data Analytics. Apache Spark is one such tool that we will discuss in this article. Further, you’ll also understand why taking a Spark online course would be beneficial for your career.

What is Spark?

The website of Apache Spark defines it as a unified analytics engine for large-scale data processing.

Its tagline says “Lightning-fast unified analytics engine.”

Apache Spark is a data processing framework, that can perform data processing tasks on huge data sets and that too, very quickly. In addition to this, it distributes data processing tasks across multiple systems, either solitarily or in assistance with other distributed computing tools.

Apache Spark is also defined as an open-source cluster computing framework, that is used for real-time processing. It is one of the most successful projects of the Apache Software Foundation. 

Today, there are many established companies that have adopted Spark, such as, Amazon, Yahoo, eBay, and many more. All the sectors like banking, telecommunications, healthcare, government agencies, gaming companies, and others use Spark for real-time analysis. Tech-giants like Microsoft, IBM, Apple, and Facebook also use Spark. 

So, today Big Data processing is seeing Spark as its market leader. You can deploy Spark in a variety of ways, providing native bindings for Scala, Python, Java, and R programming languages. It also supports machine learning, streaming data, SQL, and graph processing.

Now let us have a look at the features of Spark that make it one of the hottest Big Data Analytics tools today.

• Speed – Apache Spark is best when it comes to large scale data processing, as it runs up to 100 times faster than Hadoop. Controlled partitioning lets Spark achieve this speed. This high performance is for batch and streaming data, by using an advanced DAG scheduler, a query optimizer, and a physical execution engine.
• Generality or Supporting multiple formats – Spark supports multiple data formats like JSON, Hive, Parquet, and more. It powers a stack of libraries that includes SQL and DataFrames, SparkStreaming, and more, that can be combined seamlessly within the same application.
• Real-Time Computation – the computation of Spark is real-time and has low-latency that is because of in-memory computation. There are times when real-time computation becomes necessary, and Spark is designed for massive scalability due to this reason.
• Delayed Evaluation – the evaluation by Apache Spark can be delayed until the time it becomes necessary. This makes Spark work faster. Spark adds the transformations to DAG or Directed Acyclic Graph of computation and is executed only when it is required.
• Machine Learning – The machine learning component of Spark is MLib, and helps in the big data processing. It can help in using multiple tools for processing as well as machine learning.
• Runs Everywhere – the most important feature of Spark is that you can make it run using its standalone cluster mode, on Kubernetes, on Hadoop YARN, on Mesos. You can access data in many data sources too.

Spark Deployment

Source

There are three ways in which Spark can be deployed with Hadoop components, as explained below.

• HDFS: in this mode, Spark standalone deployment happens, which implies that Spark runs on top of HDFS (Hadoop Distributed File System) in order to utilize the distributed replicated storage. Here, Spark and MapReduce run parallelly to cover all jobs in the cluster.
• Hadoop Yarn: without the need for pre-installation or root-access, Spark can run on Hadoop Yarn. it lets other components to run on top of the stack by integrating Spark into the Hadoop ecosystem or Hadoop stack.
• SIMR or Spark in MapReduce: SIMR is used to launch Spark jobs along with standalone deployment. That means Spark can be used with Hadoop MapReduce as well as separately as a processing framework.

Components of Spark

The components of Spark are responsible for making it fast and reliable. Most of them were built to resolve the issues that arose while using Hadoop MapReduce. The Spark components are as follows:

Source

1. Spark core.

2. Spark Streaming.

3. Spark SQL.

4. MLib (Machine Learning).

5. GraphX (Graph).

Spark core forms the base engine for large scale parallel and distributed data processing. It is the fundamental engine that forms the platform for all other functionalities. It provides In-Memory computing and references to datasets in external storage systems.

On top of the Spark core lies Spark SQL that introduces a new data abstraction method which is known as SchemaRDD. This provides support for data that may be structures as well as semi-structured.

Spark core’s fast scheduling capability is utilized by Spark Streaming, to carry out streaming analytics. The data is put into mini-batches and RDD or Resilient Distributed Datasets transformations are performed over those mini-batches of data.

There is a distributed machine learning framework or MLib above Spark because of the distributed memory-based Spark architecture. Spark MLib is 9 times as fast as the Hadoop version of Apache Mahout which is disc-based.

On top of the Spark lies a distributed graph processing framework called GraphX. It provides an API for demonstrating graph computation so that it can model user-defined graphs by utilizing Pregel abstraction API. for this abstraction, it also provides you with optimized runtime.

Bottom Line

So, you have read a brief introduction to Spark Framework. One thing you need to remember is that Spark is not a modified version of Hadoop. Also, it is not dependent on Hadoop, because it has its own cluster management. Hadoop is just one of the ways of implementing Spark.

Since Spark is one of the hottest tools for real-time data analytics, and the requirement of real-time data analytics can be seen in almost every sector, you can find it really rewarding if you get Spark certified. For getting Spark certified, all you need is to find out a good online training provider. The rest is not your part, they will take care of all you are required to learn and ensure that you get certified.