Apache Spark is quickly gaining steam both in the headlines and real-world adoption. UC Berkeley’s AMPLab developed Spark in 2009 and open sourced it in 2010. Since then, it has grown to become one of the largest open source communities in big data with over 200 contributors from more than 50 organizations. This open source analytics engine stands out for its ability to process large volumes of data significantly faster than MapReduce because data is persisted in-memory on Spark’s own processing framework.
When considering the various engines within the Hadoop ecosystem, it’s important to understand that each engine works best for certain use cases, and a business will likely need to use a combination of tools to meet every desired use case. That being said, here’s a review of some of the top use cases for Apache Spark.
1. Streaming Data
Apache Spark’s key use case is its ability to process streaming data. With so much data being processed on a daily basis, it has become essential for companies to be able to stream and analyze it all in real time. Apache Spark has the capability to handle this extra workload. Some experts even theorize that Spark could become the go-to platform for stream-computing applications, no matter the type. By supporting streaming analytics of multiple kinds, Apache Spark shows its versatility, making it a clear choice in most use cases. That versatility extends to other Spark streaming capabilities such as fraud detection and log processing.
2. Machine Learning
Another of the many Apache Spark use cases is its machine learning capabilities. Spark helps users run repeated queries on sets of data, which essentially amounts to processing machine learning algorithms. Spark’s machine learning library can work in areas such as clustering, classification, and dimensionality reduction, among many others. All this enables Spark to be used for some very common big data functions, like predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis. If a company has ever used a recommendation engine, it could be done much faster with Apache Spark.
3. Interactive Analysis
MapReduce was built to handle batch processing, and SQL-on-Hadoop engines such as Hive or Pig are frequently too slow for interactive analysis. Apache Spark, however, is fast enough to perform exploratory queries without sampling. Spark also interfaces with a number of development languages including SQL, R, and Python.
4. Fog Computing
While big data analytics may be getting a lot of attention, the concept that really sparks the tech community’s imagination is theInternet of Things (IoT). The IoT essentially embeds objects and devices with tiny embedded sensors that communicate with each other and the user, creating a fully interconnected world. This world collects massive amounts of data, processes it, and delivers revolutionary new features and applications for people to use in their everyday lives. All that processing, however, is tough to manage with the current analytics capabilities in the cloud.
That’s where fog computing and Apache Spark come in. Fog computing decentralizes the data processing and storage, instead performing those functions on the edge of the network. Analysing and processing this type of data can best be carried out by Apache Spark with its streaming analytics engine and interactive real time query tool.
When NOT to Use Spark
Even though it is versatile, that doesn’t necessarily mean Apache Spark’s in-memory capabilities are the best fit for all use cases. In particular Spark was not designed as a multi-user environment. Spark users are required to know whether the memory they have access to is sufficient for a dataset. Adding more users further complicates this since the users will have to coordinate memory usage to run projects concurrently. Due to this, users will want to consider an alternate engine, such as Apache Hive, for large, batch projects.
Over time, Apache Spark will continue to develop its own ecosystem, becoming even more versatile than before. In a world where big data has become the norm, organizations will need to find the best way to utilize it. As seen from these Apache Spark use cases, there will be many opportunities in the coming years to see how powerful Spark truly is.
Interested in learning more about Apache Spark, collaboration tools offered with QDS for Spark, or giving it a test drive? Click the button to learn more about Apache Spark-as-a-Service.
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