Top Use Cases for Apache Spark

Apache Spark is an open-source project developed by AMPLab in 2009 and currently has over 50 organizations and 200 contributors using it. It uses its own processing framework to persist data in-memory and that means it can process large volumes of data compared to MapReduce.

Below are some of the use cases for Apache Spark:

Streaming data

Many companies are using it to meet their daily data streaming demands, making it one of the most common Apache Spark use cases. Spark can stream and analyze data in real time. Developers can also use a single framework for all their processing need.

Streaming ETL reads data, cleans, aggregates, converts it into formats compatible with specified databases and writes it to the target database. It can combine live with static data to help organizations perform detailed and real-time analyses, making it one of the most demanded application for modern big data use cases. For instance, organizations can come up with more personalized marketing and ad campaigns by analyzing both combined live customer behavior data and historical data using the Spark Streaming service.

Financial organizations are also using Spark Streaming trigger event identification functionality to detect fraudulent behavior. The institution receives timely relay signals for action. Hospitals also use the functionality to identify health changes in patient’s vital health. The caregiver can receive the message immediately for immediate actions.

Netflix and other companies are using Spark Streaming to analyze live session events such as a login into their website or applications, in order to analyze customer behavior and provide real-time and relevant movie and service recommendations.

Uber uses Spark Streaming among other services to collect data that can be used in more complex analytics. Pinterest also uses ETL pipeline to gain timely insight on how users engage on its network and it can then use the insights to recommend products and services. Conviva, the second-largest online video company after YouTube, uses Spark to optimize video streams and manage live video traffic and thus reduce customer churn.

Machine learning

This is also one of the most modern and important Apache Spark use cases that is in high demand among business organizations. Spark has the MLlib that can assist in clustering, classification and dimension reduction. Companies can take advantage of this to perform predictive intelligence and customer segmentation.

Using the machine learning capabilities, security companies scrutinize data packets in real time for malicious attacks. Analysts check against renowned attacks on the front end and MLlib perform a further analysis.

Interactive analysis

Spark performs interactive analysis faster than Hive, Pig and MapReduce. It can be used together with visualization tools to visualize complex data tools.

Through Structured Streaming, a more improved Spark version, users can run interactive queries against existing sessions in web analytics, meaning it adds ability to carry out an interactive query on live data.

Fog computing

Spark has gained grounds in this among other big data use cases because it can perform better than existing platforms. Through the Internet of Things (IoT), tiny sensors are being used to communicate with each other and the user. The data is then processed and delivered into various applications and features. Since processing and analyzing requires huge capability, machines and parallel processing, decentralized processing is used. However, Spark Streaming acts as the better fog computing solution that can eliminate complexities involved in processing of this data. It has interactive real-time query tool (Shark), a machine learning library (MLib), and a graph analysis engine (GraphX).