How to Assure Your Mainframe Data is Secure in Hadoop
Data security is one of the topmost concerns for businesses and IT departments today. Last year businesses experienced the second highest number of verified and tracked data breaches since these statistics first began to be tracked in 2005. The Identity Theft Center tracked some 781 breaches in 2015, which does not include an unknown number that were either never detected or never reported.
Don’t Give Up Mainframe Security to Take Advantage of Hadoop Analytics
Data security on the mainframe is famously good. That’s one of the reasons the mainframe is still carrying the lion’s share of the world’s most sensitive transactions, such as credit card payments and storing consumer data. On the other hand, Hadoop is all but essential for getting the kind of business and operational intelligence today’s organizations need to survive and remain competitive.
Hadoop’s Security Has Come a Long Way in a Short Time
In the early days, Hadoop wasn’t exactly known for its high level of security. But over time, developers (with lots of help from Syncsort) have built enterprise-class security features and measures into the system. Now it’s as potent for securing your data as it is for processing it and delivering valuable business insight and intelligence.
When accessing data on the mainframe, the process needs to be secured from the point of access through the offloading process and in the Hadoop cluster, as well. Now that Hadoop has security support from the likes of Kerberos and LDAP, plus the Hadoop-specific solutions that are now available, such as Apache Sentry, organizations can have total confidence that their data is secure from beginning to ending. This helps businesses stay within compliance, as well as providing protection against a legal and PR quagmire.
Solid Security is Essential for Regulatory Compliance
Syncsort’s DMX-h takes care of your security and compliance worries with support for FTPS and Connect:Direct data transfers, and also features native support for both Kerberos and LDAP. It also integrates seamlessly with all of the popular security systems, like Apache Sentry, as it handles the processing within the Hadoop cluster.
Many businesses operate within industries such as finance that require that data be copied in its original format. DMX-h is able to make this happen, plus it is the easiest way to access and integrate mainframe data into Hadoop because DMX-h data integration tasks are able to work directly with mainframe data without having to convert the data into a different format for storage or processing in Hadoop.
DMX-h is the ideal solution for heavily regulated industries like banking, insurance, and healthcare, which have struggled in the past to leverage Hadoop and Spark cost-effectively. These industries must deal with massive mainframe data sets while keeping the original EBCDIC format, which is not able to be processed within Hadoop. DMX-h is the only software that is able to make this happen.
Better still, it doesn’t take specialized mainframe or Hadoop skills to use DMX-h for offloading data from the mainframe to Hadoop securely. It assures the data lineage for governance purposes, while delivering the lowest possible levels of latency. You can populate your Hadoop Enterprise Data Hub in just a few easy clicks.
Learn more about Syncsort’s DMX-h and Big Data Integration.