Everywhere you go, people are talking about Spark, the fast and flexible open source engine for large-scale data processing. But what is it, and what does it mean for big data analytics?
Apache Spark is an open source engine built specifically for data science. It helps simplify algorithm development and accelerate analytics results. With Spark, you can better extract value from big data, conducting deeper analyses and delivering results faster, all while reducing the time and effort required for coding.
There’s good reason for all of the interest. Spark accelerates analytics on Hadoop, delivering unheard of speed – up to 100x faster than Hadoop MapReduce in memory – to data scientists and developers working with big data at scale.
After all of this, you may still be wondering why Spark should matter to you. Highly versatile in many environments, Spark is known for its ease of use in creating algorithms that harness insight from big data. In fact, Spark is being used right now by a range of organizations for innovative, real-world analytics use cases, and the list is growing.
Stay tuned for updates on how Spark and IBM SPSS software can help you do more and deeper analytics with less coding and faster response times than using Hadoop alone. Meanwhile, learn more about IBM’s commitment to Spark here – http://www.ibm.com/analytics/us/en/technology/spark/