Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconBig Databreadcumb forward arrow iconTop 3 Apache Spark Applications / Use Cases & Why It Matters

Top 3 Apache Spark Applications / Use Cases & Why It Matters

Last updated:
22nd Jan, 2020
Views
Read Time
7 Mins
share image icon
In this article
Chevron in toc
View All
Top 3 Apache Spark Applications /  Use Cases & Why It Matters

Apache Spark is one of the most loved Big Data frameworks of developers and Big Data professionals all over the world. In 2009, a team at Berkeley developed Spark under the Apache Software Foundation license, and since then, Spark’s popularity has spread like wildfire.

Today, top companies like Alibaba, Yahoo, Apple, Google, Facebook, and Netflix, use Spark. According to the latest stats, the Apache Spark global market is predicted to grow with a CAGR of 33.9% between 2018 to 2025.

Spark is an open-source, cluster computing framework with in-memory processing ability. It was developed in the Scala programming language. While it is similar to MapReduce, Spark packs in a lot more features and capabilities that make it an efficient Big Data tool. Speed is the core attraction of Spark. It offers many interactive APIs in multiple languages, including Scala, Java, Python, and R. Read more about the comparison of MapReduce & Spark.

Reasons Why Spark is so Popular

  • Spark is the favourite of Developers as it allows them to write applications in Java, Scala, Python, and even R. 
  • Spark is backed by an active developer community, and it is also supported by a dedicated company – Databricks.
  • Although a majority of Spark applications use HDFS as the underlying data file storage layer, it is also compatible with other data sources like Cassandra, MySQL, and AWS S3.
  • Spark was developed on top of the Hadoop ecosystem that allows for easy and fast deployment of Spark.
  • From being a niche technology, Spark has now become a mainstream tech, thanks to the ever-increasing pile of data generated by the fast-growing numbers of IoT and other connected devices.

Read: Role of Apache Spark in Big Data & What Makes it Different

Ads of upGrad blog

Applications of Apache Spark

As the adoption of Spark across industries continues to rise steadily, it is giving birth to unique and varied Spark applications. These Spark applications are being successfully implemented and executed in real-world scenarios. Let’s take a look at some of the most exciting Spark applications of our time!

1. Processing Streaming Data

The most wonderful aspect of Apache Spark is its ability to process streaming data. Every second, an unprecedented amount of data is generated globally. This pushes companies and businesses to process data in large bulks and analyze it in real-time. The Spark Streaming feature can efficiently handle this function. By unifying disparate data processing capabilities, Spark Streaming allows developers to use a single framework to accommodate all their processing requirements. Some of the best features of Spark Streaming are:

Streaming ETL – Spark’s Streaming ETL continually cleans and aggregates the data before pushing it into data repositories, unlike the complicated process of conventional ETL (extract, transform, load) tools used for batch processing in data warehouse environments – they first read the data, then convert it to a database compatible format, and finally, write it to the target database. 

Data enrichment – This feature helps to enrich the quality of data by combining it with static data, thus, promoting real-time data analysis. Online marketers use data enrichment capabilities to combine historical customer data with live customer behaviour data for delivering personalized and targeted ads to customers in real-time.

Trigger event detection – The trigger event detection feature allows you to promptly detect and respond to unusual behaviours or “trigger events” that could compromise the system or create a serious problem within it.

While financial institutions leverage this capability to detect fraudulent transactions, healthcare providers use it to identify potentially dangerous health changes in the vital signs of a patient and automatically send alerts to the caregivers so that they can take the appropriate actions.

Complex session analysis – Spark Streaming allows you to group live sessions and events ( for example, user activity after logging into a website/application) together and also analyze them. Moreover, this information can be used to update ML models continually. Netflix uses this feature to obtain real-time customer behaviour insights on the platform and to create more targeted show recommendations for the users.

Explore our Popular Software Engineering Courses

2. Machine Learning

Spark has commendable Machine Learning abilities. It is equipped with an integrated framework for performing advanced analytics that allows you to run repeated queries on datasets. This, in essence, is the processing of Machine learning algorithms. Machine Learning Library (MLlib) is one of Spark’s most potent ML components.

This library can perform clustering, classification, dimensionality reduction, and much more. With MLlib, Spark can be used for many Big Data functions such as sentiment analysis, predictive intelligence, customer segmentation, and recommendation engines, among other things.

Another mention-worthy application of Spark is network security. By leveraging the diverse components of the Spark stack, security providers/companies can inspect data packets real-time inspections for detecting any traces of malicious activity. Spark Streaming enables them to check any known threats before passing the packets on to the repository.

When the packets arrive in the repository, they are further analyzed by other Spark components (for instance, MLlib). In this way, Spark helps security providers to identify and detect threats as they emerge, thereby enabling them to solidify client security.

In-Demand Software Development Skills

3. Fog Computing

To grasp the concept of Fog Computing is deeply entwined with the Internet of Things. IoT thrives on the idea of embedding objects and devices with sensors that can communicate amongst each other and with the user as well, thus, creating an interconnected web of devices and users. As more and more users adopt IoT platforms and more users join in the web of interconnected devices, the amount of data generated is beyond comprehension.

As IoT continues to expand, there arises a need for a scalable distributed parallel processing system for processing vast amounts of data. Unfortunately, the present processing and analytics capabilities of the cloud aren’t enough for such massive amounts of data. 

Explore Our Software Development Free Courses

What’s the solution then? Spark’s Fog Computing ability.

Fog Computing decentralizes data processing and storage. However, certain complexities accompany Fog Computing – it requires low latency, massively parallel processing of ML, and incredibly complex graph analytics algorithms. Thanks to vital stack components like Spark Streaming, MLlib, and GraphX (a graph analysis engine), Spark performs excellently as a capable Fog Computing solution. 

Concluding Thoughts

Ads of upGrad blog

These are the three significant applications of Spark that are helping companies and organizations to create significant breakthroughs in the domains of Big Data, Data Science, and IoT. 

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

Profile

Utkarsh Singh

Blog Author
Get Free Consultation

Select Coursecaret down icon
Selectcaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Big Data Course

Frequently Asked Questions (FAQs)

1 Does Apache Spark offer any benefits?

Apache Spark is a hugely popular unified analytics engine designed for machine learning and big data. Since its launch, Apache Spark has seen rapid adoption by organizations across various industries. There are several advantages of employing this platform, which account for its tremendous popularity. First is the lightning-fast speed of large-scale data processing offered by Apache Spark; it is up to 100 times faster than that provided by Hadoop. Then Spark comes as a whole unified package with high-level libraries, graph processing, SQL query support and data streaming capabilities, which contribute to the productivity of developers. And, of course, Apache Spark is highly user-friendly too.

2What is exactly meant by data analytics?

Data analytics is the process by which meaningful insights are extracted from raw data with the help of specialized software applications. These applications help transform, streamline and arrange the data in specific models such that it can help in drawing conclusions and identifying patterns. With the infinite power that data holds today, data analytics has evolved into a complex practice that is used to extract meaning from massive volumes of data and often high-velocity data that bring about various challenges. This is why expert data analytics professionals, also known as data scientists, are required for the successful handling and modelling of data.

3Is Big Data and Hadoop the same thing?

Big Data and Hadoop are not really the same; although they are very closely interconnected, i.e. without the existence of one, there would be no meaning or existence of the other. You can consider Big Data as an asset of extreme value to businesses, but to realize and make use of the value that is contained in this asset, you need some method or tool. Hadoop is a tool or platform developed to extract the maximum value from this asset, i.e. Big Data. Big Data refers to complex, massive datasets that are processed, analyzed and stored with the help of a sophisticated framework known as Apache Hadoop.

Explore Free Courses

Suggested Blogs

Characteristics of Big Data: Types & 5V’s
5363
Introduction The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and lik
Read More

by Rohit Sharma

04 Mar 2024

50 Must Know Big Data Interview Questions and Answers 2024: For Freshers & Experienced
7036
Introduction The demand for potential candidates is increasing rapidly in the big data technologies field. There are plenty of opportunities in this
Read More

by Mohit Soni

What is Big Data – Characteristics, Types, Benefits & Examples
185203
Lately the term ‘Big Data’ has been under the limelight, but not many people know what is big data. Businesses, governmental institutions, HCPs (Healt
Read More

by Abhinav Rai

18 Feb 2024

Cassandra vs MongoDB: Difference Between Cassandra & MongoDB [2023]
5460
Introduction Cassandra and MongoDB are among the most famous NoSQL databases used by large to small enterprises and can be relied upon for scalabilit
Read More

by Rohit Sharma

31 Jan 2024

13 Ultimate Big Data Project Ideas & Topics for Beginners [2024]
99675
Big Data Project Ideas Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill
Read More

by upGrad

16 Jan 2024

Be A Big Data Analyst – Skills, Salary & Job Description
899646
In an era dominated by Big Data, one cannot imagine that the skill set and expertise of traditional Data Analysts are enough to handle the complexitie
Read More

by upGrad

16 Dec 2023

12 Exciting Hadoop Project Ideas & Topics For Beginners [2024]
20657
Hadoop Project Ideas & Topics Today, big data technologies power diverse sectors, from banking and finance, IT and telecommunication, to manufact
Read More

by Rohit Sharma

29 Nov 2023

Top 10 Exciting Data Engineering Projects & Ideas For Beginners [2024]
39933
Data engineering is an exciting and rapidly growing field that focuses on building, maintaining, and improving the systems that collect, store, proces
Read More

by Rohit Sharma

21 Sep 2023

Big Data Architects Salary in India: For Freshers & Experienced [2024]
899182
Big Data – the name indicates voluminous data, which can be both structured and unstructured. Many companies collect, curate, and store data, but how
Read More

by Rohit Sharma

04 Sep 2023

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon