Evolution of Apache Spark

Before Spark, first, there was MapReduce which was used as a processing framework. Then, Spark got initiated as one of the research projects in 2009 at UC Berkeley AMPLab. It was later open-sourced in 2010. The main purpose of this project was to create a cluster management framework that supports various computing systems based on clusters. After its release to the market, Spark grew and moved to Apache Software Foundation in 2013. Nowadays a lot of organizations across the world have incorporated Apache Spark for empowering their big data applications.

Why do we need Apache Spark?

Most of the technology-based companies across the globe have moved toward Apache Spark. They were quick enough to understand the real value possessed by Spark such as Machine Learning and interactive querying. Industry leaders such as Amazon, Huawei, and IBM have already adopted Apache Spark. The firms that were initially based on Hadoop, such as Hortonworks, Cloudera, and MapR, have also moved to Apache Spark.

Big Data Hadoop professionals surely need to learn Apache Spark since it is the next most important technology in Hadoop data processing. Therefore, ETL professionals, SQL professionals, and Project Managers can gain immensely if they master Apache Spark. Finally, Data Scientists also need to gain in-depth knowledge of Spark to excel in their careers.
Spark can be extensively deployed in Machine Learning scenarios. Data Scientists are expected to work in the Machine Learning domain, and hence they are the right candidates for Apache Spark training.
Key Features of Spark

Developed in AMPLab of the University of California, Berkeley, Apache Spark was developed for high-speed, easy-to-use, and more in-depth analysis. Though it was built to be installed on top of the Hadoop cluster, its ability to parallel processing allows it to run independently as well.

Let’s take a closer look at the features of Apache Spark:

 

Fast transformation and processing:
The most important characteristic of Apache Spark that has made the big data world choosing this technology over others is its speed. Big data is characterized by its volume, variety, velocity, value, and veracity due to which it needs to be processed at a higher speed. Spark contains Resilient Distributed Datasets (RDDs) that save the time taken in reading and writing operations, and hence it runs almost 10–100 times faster than Hadoop.

Flexibility: Apache Spark supports multiple languages and allows developers to write applications in Java, Scala, R, or Python. Equipped with over 80 high-level operators, this tool is quite rich from this aspect.

In-memory computing: Spark stores data in the RAM of servers, which allows it to access data quickly and in-turn, accelerates the speed of analytics.

Real-time ongoing process: Spark is able to process real-time streaming data. Different from MapReduce, which processes the stored data, Spark is able to process the real-time data and hence is able to produce instant outcomes.

Better analytics: Contrasting to MapReduce that includes Map and Reduce functions, Spark has much more in store. Apache Spark comprises a rich set of SQL queries, Machine Learning algorithms, complex analytics, etc. With all these Spark functionalities, Big Data Analytics can be performed in a better fashion.

Compatibility with Hadoop: Spark is not only able to work independently; it can work on top of Hadoop as well. Not just this, it is certainly compatible with both versions of the Hadoop ecosystem.

Domain Scenarios of Apache Spark

Today, there is widespread deployment of big data tools. As days are passing, the requirements of enterprises increase, and therefore there is a need for a faster and more efficient form of data processing. Most streaming data is in an unstructured format, coming in thick and fast continuously. Here in this Apache Spark blog, we look at how Spark is used successfully in different industries.

Banking
Spark is being more and more adopted by the banking sector. It is mainly used here for financial fraud detection with the help of Spark ML. Banks use Spark to handle credit risk assessment, customer segmentation, and advertising. Apache Spark is also used to analyze social media profiles, forum discussions, customer support chat, and emails. This way of analyzing data helps organizations make better business decisions.

E-commerce
Spark is widely used in the e-commerce industry. Spark Machine Learning, along with streaming, can be used for real-time data clustering. Businesses can share their findings with other data sources to provide better recommendations to their customers. Recommendation systems are mostly used in the e-commerce industry to show new trends.

Healthcare
Apache Spark is a powerful computation engine to perform advanced analytics on patient records. It helps keep track of patients’ health records easily. The healthcare industry uses Spark to deploy services to get insights such as patient feedbacks, hospital services, and to keep track of medical data.

Media
Gaming companies are using Apache Spark for finding patterns from their real-time in-game events. With this, they can derive further business opportunities by customizing such as adjusting the complexity-level of the game automatically according to players’ performance, etc. Some media companies, like Yahoo, use Apache Spark for targeted marketing, customizing news pages based on readers’ interests, and so on. They use tools such as Machine Learning algorithms for identifying the readers’ interests’ category. Eventually, they categorize such news stories in various sections and keep the reader updated on a timely basis.

Travel
Many people land up with travel planners to make their vacation a perfect one, and these travel companies depend on Apache Spark for offering various travel packages. TripAdvisor is one such example of a company that uses this programming language to compare different travel packages from different providers. This Programming language helps to scan through hundreds of websites to find the best and reasonable hotel price, trip package, etc.

Conclusion

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