As traditional data processing tools are becoming more and more inadequate in dealing with the huge data sets so pervasive in business computing today, where can you find a platform that can help you stay competitive in the big data word? Open-source Apache Spark was developed for data processing in 2009, and it has since become one of the most popular big data processing platforms in the world.
Why should you consider Spark?
With its ease of use — even when processing big data and complex analytics — Spark provides several advantages over other big data processors. For example:
- Spark has a unified framework for processing a variety of diverse data sets.
- It processes big data 100 times faster than Hadoop.
- Spark includes support for Structured Query Language (SQL), streaming data, machine learning, and graph processing.
- It provides easy-to-use application programming interfaces (APIs) for processing big data sets and more than 100 operators for transforming data.
- Spark’s ability to store data in memory makes it possible to answer complex queries in a short time period.
Benefits for businesses
Apache Spark is gaining traction in the business world. Why? With Spark, you could benefit from its ability to handle billions of records at a time. You could even potentially reduce processing times from hours to minutes and, in other cases, days to hours. Spark’s powerful processing engines provide ample time for your data team members to validate information before releasing it to your clients. Without Spark, validation can require very high bandwidth, costing you more to complete. Overall, Spark could help you achieve timeliness in data processing and meet your clients’ expectations, leading to corresponding revenue accrual in many cases.
Collaboration through integration
Spark provides integration APIs for Scala, Java, and Python, which opens more doors to employees at multiple levels from data scientists to developers and testers, allowing them to look at and discuss the same data sets at their convenience and in less time. Its machine learning packages and seamless integration with R programming applications make it a favorite framework for data scientists. Spark’s standard libraries increase developer productivity, and you can seamlessly combine them to create complex workflows. Also, all data processing APIs seamlessly integrate with one platform, which removes many potential development and operational pitfalls.
Whether you choose to use Spark or another big data processing tool, incorporating it into your business operations requires planning and expertise. It’s important to work with an extended team of experts to minimize implementation problems and launch time so you can quickly start doing more with all that data.