Apache Spark has been making waves in the Big Data world and is quickly gaining speed in real-world adoption. Since its advent in 2009, Spark has grown to become one of the largest open-source communities in Big Data. This open-source analytics engine is known for its ability to process large volumes of data significantly faster than MapReduce, making it a much sought-after tool by many organizations.
Spark brings interactive performance, streaming analytics, and machine learning capabilities to a wide audience and offers a more developer-friendly and integrated platform. Let’s look at some of the use cases of Spark that makes it a preferred Big
Apache Spark Use Cases:
Here are some of the top use cases for Apache Spark:
Streaming Data and Analytics
Apache Spark’s key feature is its ability to process streaming data. With petabytes of data being processed every day, it has become essential for businesses to stream and analyze data in real-time. Apache Spark has the capability to do this. Spark could become the default platform real-time analytics. Apache Spark shows its versatility, making it a clear choice when it comes to streaming analytics of multiple kinds.
Spark isn’t the first Big Data tool for handling streaming ingest, but it is the first one to integrate it with the rest of the analytic environment. You can use the same code for streaming analytic operations as you can for batch, and use Spark to compute over both the stream and historical data.
The comparatively long execution times of a Hadoop MapReduce job make it problematic for hands-on exploration of data, by data scientists. This is where Spark’s speedy in-memory capabilities comes to play. Now, it can happen completely within Spark, without need for Java engineering or sampling of the data.
Spark can be used for model building and deployment, making the process much more efficient, and providing data scientists the hands-on insight into model performance.
Spark allows users to run repeated queries on datasets, which is nothing but processing machine learning algorithms. Spark’s machine learning library can work in areas such as clustering, classification, and dimensionality reduction, among many others, enabling them to be used for some Big Data functions like Predictive Intelligence, customer segmentation, and Sentiment Analysis.
MapReduce handles batch processing and SQL on Hadoop engines such as Hive or Pig, but are too slow for Interactive Analysis. Apache Spark overcomes this shortcoming as it fast enough to perform exploratory queries without sampling. Spark also interfaces with a number of development languages like SQL, R, and Python.
With GraphX, Spark brings all the benefits of using its environment to graph computation and allows use cases such as social network analysis, fraud detection, and recommendations. Spark’s integration of the platform brings flexibility and resilience to graph computing, as well as the ability to work with graph and non-graph sources.
Fog computing distributes the data processing and storage, instead of performing those functions on the edge of the network. Analyzing and processing this type of data is best be carried out by Apache Spark with its streaming analytics engine and interactive real-time query tool.
Applications Using Apache Spark:
Credit Card Fraud Detection
Apache Spark Streaming, running on Hadoop, makes it potential for banks to process transactions and detect fraud in real-time against previously identified fraud tracks. In Spark, the in-coming transaction feeds are checked against a known database and if there is a match, a real-time trigger can be set up to alert the call center personnel who can then validate the transaction instantly with the credit card owner. If not, the data is stored on Hadoop, where it can be used to continuously update the models in the background through deeper Machine Learning.
Different components of the Spark stack are used to examine data packets for traces of malicious activity in real-time. At the front end, it uses Spark Streaming to check against known threats before passing the packets on to the storage platform where the data will be further processed using other packages such as GraphX and MLLib.
NextGen Genomic companies are using the power of distributed storage and comput through Spark on Hadoop to radically reduce the time it needs to process genome data.Earlier, it used to take several weeks to align chemical compounds with genes, but now, it only takes a couple of hours. The drastic reduction in the time to process genomic data is a major benefit for the researchers.
Real-Time Ad Processing
Advertising is very time-sensitive, so advertisers have to move fast if they want to make an impact. Spark on Hadoop is one way to help them achieve that. One advertising firm uses Spark, based on MapR-DB database, to build a real-time ad-targeting platform.The system matches user behavior with historical patterns and decides which ads to show users on the internet.
There is a diverse range of real-time uses for the Spark stack as it helps to simplify the challenging and compute-intensive task of processing high volumes of real-time or archived data and integrating relevant complex capabilities such as Machine Learning and graph algorithms. With Spark’ developer-friendly nature, interactive performance and fault tolerance, organizations are reaping benefits in productivity, maintainability, and operational expense.