7 Powerful Features of Apache Spark for Web Development

Today, we are living in the age of information technology (I.T.) and business intelligence (B.I.). The concepts that drive modern businesses are governed by the analysis of huge chunks of data. E-commerce businesses, government organizations, health care industries, media companies and more thrive on one important aspect, which is information. Yahoo’s open-source big data technology, Hadoop, which allows processing large chunks of data made it possible for several sectors to leverage the power of data processing.

Today, data processing and data storage makes it possible for large-scale businesses to conduct risk and trend analysis. It helps to study and understand the growing demands and requirements of the general masses. This in turn helps to produce, promote and deliver products and services which are utilitarian in nature. It is for this reason that the collection, storage and analysis of relevant data are the crucial aspects of conducting modern businesses. But, what we are talking about right now is terabytes and petabytes of information often referred to as big data. This is where Hadoop comes in to picture. Technologies like Hadoop are used by large-scale businesses for processing big data and conducting trend analysis that defines and ideates modern-day business concepts based on statistical data. For instance, share markets or government policies. It is useful for making improved business decisions.

What is Hadoop?

Hadoop is a big data processing technology that offers two important capabilities, namely data storage through Hadoop Distributed File System (HDFS) and data processing (MapReduce computing paradigm). It is an open-source software platform that is used to scale across thousands of devices or servers present in a Hadoop cluster to store and process information.

Today, there are a lot of tools that are designed to work with Hadoop to improve its efficiency. The Hadoop ecosystem consists of various technologies to specifically deal with particular use case scenarios.

What is Apache Spark?

Apache Spark is an open-source cluster computing framework which works as a standalone technology or a Hadoop-compatible technology. It was developed at AMPLabs in UC Berkeley as part of Berkeley Data Analytics Stack (BDAS).

Usually, there are three different types of use cases in big data processing, namely batch (ad-hoc queries), real-time streaming and interactive (querying historical data).

As mentioned earlier, there are different tools to deal with these specific use cases within a Hadoop ecosystem. Apache Spark solves this issue as it provides a common framework for working with all types of data sets in any use case scenario.

Apache Spark Certification

Apache Spark is also gaining prominence as a robust career stream. A huge rise is witnessed in the availability of several tutorials to learn Apache Spark.

There are some big organizations and educational institutes offering Apache Spark tutorials for data scientists and software developers. Moreover, there are also some online courses for learning Apache Spark for beginners.

Moreover, it is very simple for professional developers to learn spark cluster setup. There is a no dearth of online resources and documentation that teaches about spark cluster installation to get you started. You may find helpful guides on Databricks and Cloudera websites.

Now, let us discuss Apache Spark features in detail:

1. Supports Multiple Use Case Scenarios :

In the field of data science, there are two important streams, namely investigative and operational analytics. Data scientists conducting investigative analytics take advantage of modern statistical environments such as R. On the other hand, others who work on operational analytics build software products offering improved ways to query machine-learning models that work in real-time environments. The Hadoop ecosystem requires a different technology stack to deal with a specific use case. Often, there are professionals from different language backgrounds, such as Python and Java working on different scenarios. Apache Spark helps solve this issue. Apache Spark is written in Scala, a new language to work suitably with Apache Spark. But, being Scala-based, it integrates with any Java virtual machine (JVM) environment. Hence, it supports Scala, Java, SQL, Python and R (in progress) programming languages.

2. Speed:

Unlike Hadoop MapReduce which stores all data on disc and performs data processing, Apache Spark, on the other hand, loads all data in distributed memory (RAM) across a cluster of machines. Hence, it becomes possible to iteratively transform data and cache when required. It has been observed that Apache Spark processes data 100 times faster than Hadoop MapReduce when all data is stored in-memory and 10 times faster in case of insufficient memory.

3.Compatibility

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Apache Spark is compatible with both the versions of the Hadoop ecosystem, namely SIMR (Spark in MApReduce) and YARN (Yet Another Resource Negotiator). Hence, it becomes possible for large-scale companies to adopt and implement Apache Spark with their existing infrastructure.

4. In-memory computing:

Hadoop MapReduce keeps shuffling things in and out of disk (I/O) and usually, for SQL engines, such as Hive, a chain of MapReduce operations is required which involves a lot of I/O activity. Hence, MapReduce is rendered unsuitable for faster data sharing as well as for performing multiple jobs on a same dataset. Moreover, MapReduce jobs uses data which has been replicated and stored on-disc within a cluster. On the other hand, Apache Spark performs in-memory computing without I/O, which is faster than MapReduce for working on the same data.

5. Machine Learning Library:

Apache Spark comes with MLib (Machine Learning Library) which includes learning algorithms and utilities, such as classification, regression, clustering, collaborative filtering, lower-level optimization primitives and higher-level pipeline APIs. Apache Spark adds various libraries making it easier for developers to deal with any use cases. It offers Spark Streaming, Spark SQL and GraphX to allow conducting real-time analysis of anything, such as trading data and web clicks.

6. Easy-to-Code:

Apache Spark also offers a Map and a Reduce function but offers more functions like Filter, Join and Group-By which makes it easier for developing applications for Apache Spark. Moreover, compared to developing applications for Hadoop MapReduce which requires writing almost hundred lines of code, the same can be achieved by writing simply four lines of code for Apache Spark.

7. Lazy Evaluation:

Lazy Evaluation is another prominent feature of Apache Spark. The feature allows it to wait for instructions before providing a final answer and does not necessarily waste time evaluating entire data which is irrelevant before any clear instructions are given.

Conclusion:

Today, Apache Spark is used for dealing with various use cases scenarios, such as high-performance batch computation, real-time stream processing, business intelligence and more. Apache Spark has been already deployed on the production clusters of various large-scale companies, such as Yahoo, VideoAmp, Taboola and more.

Spark cluster computing framework is a strong alternative to Hadoop. Apache Spark is also gaining prominence as a robust career stream. A huge rise is witnessed in the availability of several tutorials to learn Apache Spark. There are a plethora of options to receive Apache Spark training.

In this post, we summarized some important points that outlined the importance and benefits of Apache Spark as an execution engine in big data processing. If you want to share any feedback, then you can write your comments in the comments section below. Thank you.

I am a technical blog writer and content developer at Eduonix Learning Solutions, beside content writing I also love to discuss on topics related to web designing, SEO and other stuff which are trending in today's web development world.