Enterprise to Cloud Data Integration

The best analytics require detailed data. Leading enterprise organizations mine their transaction data for insights 79% of the time, and at least 60% of enterprise transactions touch their mainframe server. It’s safe to say that the best predictive analytics require detailed enterprise data from the mainframe server.

There is actionable information to be gleaned from new sources like website click-streams, but when combined with the contextual enterprise data of customer and transaction data, the richness is multiplied.

What’s the easiest way to copy data from the mainframe to your enterprise analytics platform? A look at integration costs recently showed that on a big data project, 80% of the development effort goes into data integration and only 20% is spent on analytics—probably the inverse of how it should be.

In sectors like financial services, banking and insurance, these customer insights help detect fraud, reduce loan risk, and find new opportunities in cross-selling services.

Data Integration Challenges into Cloud Platforms

Traditional solutions to enterprise data integration were not purpose-built for big data and cloud platforms, and this has made implementation expensive and complex.

Now, Veristorm’s vStorm Enterprise solves the challenges of enterprise data integration into Hadoop and other big data solutions on cloud platforms.

• It provides a secure gateway to cloud-based analytics solutions

• Nothing to install: vStorm Enterprise runs as a cloud service

• Reduce mainframe costs: Data is extracted in native form

• Data conversion is automated for DB2, VSAM, QSAM, Datacom/DB and others

• Eliminate data conversion costs: Data is converted on the fly on the target platform

• Preserve analytic agility; metadata is also extracted and converted

• Low-latency, because data is copied and converted without staging

• Filter data before export

With the introduction of vStorm Enterprise 3.0, Veristorm supports all of the major cloud vendors and solutions, including database as a service, Hadoop and Spark in the cloud, and cloud-based file storage.

IBM

SoftLayer: Copy enterprise data into SoftLayer as-is for your own projects, or insert enterprise data (like VSAM) directly into NoSQL solutions like Cloudant and DashDB. Hadoop and Spark are also supported, including Hive.

Bluemix: Now when you use Bluemix to rapidly deploy an application, you can move equally rapidly to populate it with production data. Cloudant, DashDB, MongoDB are all supported as NoSQL platforms.

Oracle

Oracle Big Data Cloud Service is supported for low-latency data import from mainframe sources like DB2. Because metadata is supported, the target application does not have to be tightly coupled to the incoming data.

Google

The vStorm Enterprise software can import mainframe sources like DB2 or VSAM directly into Google’s BigTable and CloudSQL.

Amazon

Now you can copy mainframe enterprise data into to Amazon’s Elastic MapReduce through direct streaming to S3 for less expensive archive.

Microsoft

Microsoft’s SQL Server (RDBMS as service), DocumentDB (NoSQL DB as service) and HDInsights applications are built upon the Microsoft Azure clould layer. All of these are available as low-latency targets for data movement.