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.
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.
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.
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.
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.
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.
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