Webinar: Goodbye, Bottlenecks: How Scale-Out Hadoop and In-Memory Solve ETL
August 3, 2015
The ETL (extract-transform-load) process was born out of necessity, and for decades, it has been the glue that holds together data sources and target applications. Anecdotally, it accounts for 70 percent of the effort in a data warehouse setup. But as data growth continues to soar, and as increased competition demands real-time data, the standard ETL process has pushed these systems to their break point, making the process brittle and often unmanageable. Scaling up resources can solve the problem, but it’s very costly and only a matter of time before the processes hit another bottleneck. Companies have tried ETL on Hadoop to alleviate Big Data bottlenecks with affordable scalability, but have found ETL on Hadoop to be unreliable; any hiccup can lead to the ETL job restarting, putting extra hours into the daily process.
If outmoded ETL is standing in the way of your real-time analytics, it might be time to consider a completely new approach. New solutions are being deployed that meld the scalability of Hadoop with the features of relational databases. These solutions give enterprises an entry point to Hadoop that provides tangible benefits by making data available to applications and analysts in minutes and seconds, instead of days and hours.
Join us for this episode of The Briefing Room to learn from veteran analyst Dr. Robin Bloor, as he explains how modern, data-driven architectures must adopt equally capable data integration strategy. Dr. Bloor is briefed by Rich Reimer, VP of Marketing & Product Management at Splice Machine, who discusses how his company solves ETL performance issues and enables real-time analytics and reports on Big Data. He demonstrates that by leveraging the immense scale-out power of Hadoop and the powerful in-memory speed of Spark, users can bring both analytical and operational systems together, eventually performing transactions only when needed.
View the recorded webinar now: