Solution Sheet

Product Sheet: Mainframe Data Access & Integration Solution

Mainframe Data Access & Integration

Many organizations have realized the benefits of bringing their mainframe data to Hadoop; however, they quickly face challenges when they attempt to do so – specifically around connectivity, data and file types, security, compliance and overall expertise. Precisely combines cutting-edge technology and decades of experience with both mainframe and Big Data platforms to offer the best solution for mainframe data access & integration with Hadoop. Precisely provides the experience so you don’t have to.

With Precisely Connect, you can:

• Get mainframe data into Spark and Hadoop – in a mainframe format – and work with it like any other data source!
• Preserve your data exactly as it was on the mainframe to meet governance & compliance mandates
• Enable non-mainframe developers to work with native mainframe data on the cluster
• Cleanse, blend & transform data on the cluster
• Directly access and understand VSAM files, mainframe fixed & variable files, and Db2 data
• Give your data meaning with COBOL Copybooks mapped directly to the mainframe data
• Stop wasting weeks of development time just to understand the data
• Keep your data lake in sync with changes made on the mainframe through seamless integration with Connect

Why Bring Mainframe Data to Big Data Frameworks

Bigger Insights & Better Return on Big Data Investments

Mainframes power many mission-critical applications throughout the enterprise, collecting, generating and processing some of the largest data volumes with exceptional performance and reliability. Many of the organizations that rely on the mainframe are, at the same time, building cloud data hubs and data lakes to house all their other important data for analytics with Big Data frameworks, like Spark and Hadoop. If the mainframe data isn’t part of these projects, a significant piece of the puzzle is missing. The result is that the mainframe data is underutilized and the value of the organization’s Big Data investments is diminished.

Therefore, it is critical that these organizations bring their mainframe data their enterprise data clouds and data lakes, where it can be analyzed along with other important data sources in a cost-effective and scalable manner, and curated for business analysts and data scientists to support strategic business use cases. By liberating this data from the mainframe, companies can make better and more informed decisions with information they might never have seen before – significantly impacting their growth and profitability.

Reliable and Economical Storage

Historically, mainframe Direct-Access Storage Devices (DASD) have been very expensive, so companies stored massive amounts of data on tape. However, data stored on tape isn’t easily accessible and tape deteriorates over time. By moving mainframe data from tape to Big Data repositories, companies have a cost effective way to reliably store that data – while making it available for analytics with other data sources.

Mainframe Access and Integration