With the rise of Big Data, a data-driven approach to business is transforming the enterprise. Companies today are thinking about and using data in myriad new ways to drive business value, from reducing risk and fraud in the financial sector to bringing new pharmaceuticals to market more quickly at a higher level of efficacy.
Control-M vs. Oozie and Hadoop Open Source Tools
Explore open source and enterprise Hadoop batch management tools side-by-side to make the right decision for your organization.
Combine The Best Of Both Worlds With Agile And Distributed BI Platforms
Minimize effort, cost, and risk with the Control-M platform
Start your free trial of Control-M for Hadoop.
Experience for yourself how easy it can be to build, test, and run Big Data workflows and accelerate time-to-value for Big Data applications.
Automate Big Data batch processes with an enterprise alternative to Oozie
Big Data developers know that Oozie just hasn’t kept up with the rapid evolution of the Hadoop ecosystem or the needs of today’s enterprises. Now there’s a better way to manage Big Data workflows.
Read the blog to learn how you can:
•Develop Hadoop workflows 40% faster through automation
•Schedule and manage Big Data batch processes alongside other enterprise workloads
•Ensure Big Data workflows execute without disrupting business activity
Analyzing big data poses multiple challenges. Highly parallel distributed data architecture is
one solution, but until recently it has been mostly limited to databases, not business intelligence (BI) application servers. In this report, application development and delivery (AD&D) pros working on BI initiatives will learn about the capabilities of distributed BI platforms mostly based on Hadoop.
Use a pla;orm approach to keep your evolving Big Data environment flexible and efficient.
Apache Spark can drama0cally speed Big Data workloads by overcoming Hadoop limita0ons and suppor0ng in-memory processing.
This white paper examines how a plaForm approach can enable you to introduce Spark into your environment without disrup0ng exis0ng opera0ons, crea0ng silos, or limi0ng future flexibility.
Experience how easy it can be to build, test, and run Big Data workflows and accelerate time-to-value for Big Data applications using Control-M.