Data infrastructure optimization, availability & security software
Data integration & quality software
The Next Wave of technology & innovation

Offloading Big Data and Workloads to Hadoop: What are You Waiting for?

The mainframe is a bit like Mr. Mark Twain: reports of its death have been greatly exaggerated. Through the advent of the cloud and mobile and big data and all of the other unbelievable changes of the past several years, the mainframe keeps hanging in there. In fact, the mainframe still carries the bulk of the workload in most organizations, so when it comes time to delve into big data, that’s where the goodies are hiding. On the mainframe, it takes countless hours of ETL to get data offloaded and into a big data analytical platform like Hadoop. That is, until now.

Offloading Data is No Longer Time-Consuming and Difficult

data transfer
DMX-h even eliminates the need for writing additional code or installing more software.

Hadoop itself does not include an easy ETL solution. The good news is, big data integration tools like Syncsort DMX-h are available to help you offload data and batch processing, not only from mainframe, but also from Windows, Unix and Linux into Hadoop quickly, easily and for significant cost savings. In fact, DMX-h is a single Hadoop ETL tool that allows for seamless data offload to Hadoop. DMX-h allows you to translate and sample data during the transfer process without even writing any code. DMX-h also gives you the power to develop MapReduce ETL jobs without the trouble of coding.

Offloading Data Can Reduce Data Warehousing Costs

data transfer
What used to be a long, expensive process is now fast and easy with the right tools. Offloading data and workloads to Hadoop also saves costs associated with managing a data warehouse.

Most of the time, the point of offloading mainframe data and workloads to Hadoop is about getting the benefits of big data analytics. While that does offer a powerful punch in terms of ROI, there is another side benefit to offloading. Moving the resource intense data and processes off of the Enterprise Data Warehouse and into Hadoop can save significant costs. Data warehouses typically exhibit the 20-80 rule: 20 percent of the data transformation takes up 80 percent of the available resources. Offloading into Hadoop can free up these resources, freeing up tremendous power and resources within the IT infrastructure.

So, what are you waiting for? Start offloading enterprise-wide data and begin realizing the potential of Hadoop and big data analysis today.

Related Posts