• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Hadoop, Mainframe & Syncsort: Simply the Best: Part 3

March 24, 2016   Big Data

In case you missed Part I and Part II of our blog series, “Simply the Best,” I’m Paige Roberts, the new Product Manager for big data. Our last two posts looked at why customers are increasingly moving data from the mainframe to Hadoop, along with the many benefits of using DMX-h to do so.

Today, Arnie Farrelly, the VP of Global Support and Services, will share insights into real customer use cases.

So, can you tell me about any specific customer who is using this capability?

It’s brand new, just introduced in the latest version, but we have several customers who have really been asking for it.

What’s a good example of an in-production DMX-h mainframe to Hadoop customer?

Well, there are quite a few. One large insurance industry customer needed to populate their data lake very rapidly. Their challenge was that they had hundreds of tables in DB2 on the mainframe, hundreds of Oracle tables, and mainframe VSAM files as well. They needed a way to get all that data into the data lake quickly. Even with a point and click interface, designing 600 jobs that simply took one table, moved it onto Hadoop and wrote it out would take too long. They’d looked at Informatica and other solutions, but they weren’t able to find anything that would actually work. They moved their VSAM files into Hadoop with DMX-h, no problem. For the databases, we just recently developed a utility called Data Funnel that can ingest a lot of tables all at once. You can take an entire database schema, hundreds of tables, and load them into HDFS very quickly.  They were able to load, I think, 1.4 terabytes of Oracle data, and over 600 DB2 tables. That was huge for them. They thought that was the greatest thing since sliced bread.

Data Funnel works on any kind of database, not just mainframe DB2?

Any source or target that our DMX-h engine supports, Data Funnel supports or can support, and that covers a lot of ground.

It just grabs hundreds of tables at a time?

Exactly. It runs in parallel across all the different data nodes so you can ingest data into the cluster in parallel.

That’s impressive. I could have used one of those a few years back. Do you have another example mainframe to Hadoop customer?

Well, a large US bank that we can’t name right now, was one of our early DMX-h adopters. They were using something called JRecord, an open source utility that lets you get mainframe data into Hadoop. It requires a fair amount of programming – a programmer’s kind of tool. The problem for the bank was they had incredibly complex copybooks, like 83-page copybooks, with lots of redefines in them. They used us to take that complex data with all the redefines, pull it in, transform it and load it into HDFS – and did so very easily. The POC was completed in a day. We went right in and showed them we could parse that complex copybook, which was no small feat.

Just right out of the box, you could parse that with DMX-h?

Within a day.

JRecord couldn’t do that?

Well, maybe they could, but it would have taken a fair amount of work and expensive developer time. That’s the thing we’re hearing a lot. A lot of the challenges people are facing with Hadoop are doable. They’re solvable. They’re just really complicated and difficult, and require a whole lot of time-consuming work to get them done. Companies don’t want to have to go out and hire more people, train them on Hadoop and wait forever. Our product fits in nicely, saving money, time and aggravation by simplifying the process.

Learn why mainframe data is an essential part of your data hub and how DMX-h can help break through the common barriers for data access.

So, about this theme I’ve been hearing around mainframe to Hadoop, “Simply the Best.” Why do you feel Syncsort is simply the best for this use case? What does it bring to the table that’s so special?

A few things:

First, Syncsort was processing big data before it was a buzz word. We know big data. Back when sorting two gigs of data was a huge, insurmountable task, we invented our high performance sort product that could process way more than two gigs of data very efficiently. Our product blew away the market with the performance and efficiency. So, we get the big data problem of trying to process more data than standard software is designed to handle. We know big data.

Second, we know mainframes. Our history on mainframes is decades long, and half our business is still building and selling mainframe software. We have IronStream, a Kafka to Splunk product for exporting streaming mainframe logs that sells like hotcakes. Our understanding of mainframes is unmatched in the market.

And finally, we know Hadoop. We’ve got a team of Hadoop experts. They know the best practices. We are in the top 10 contributors to core Hadoop, and have contributed to other projects like Spark, Sqoop and Parquet. We have been partnered tightly with Cloudera for years, and we are partnered, certified and have customers deployed on all the major distributions; Hortonworks, MapR, IBM, and Pivotal. We are committed to and immersed in Hadoop.

Put those three things together and we have a perfect storm. There really isn’t anyone else out there who can touch us for any project with both a mainframe and Hadoop involved. If your organization has a mainframe running key systems, and is moving to a Hadoop implementation, you would really be silly NOT to use Syncsort.

It’s a triple-threat!

(laughing) Yeah, we’re a triple-threat. We’re also very agile as a company, and the skills and knowledge on this team are incredible. We know Hadoop, we know mainframes, we know our products, and we know the space. In a lot of cases, if you need a feature we don’t have, we can extend the product within a week or two. That’s definitely not true of some other vendors in the market. They’ll tell you, “Oh, that will be on the roadmap. Look for it in a year or two, maybe.” We’re able to move on customer needs, and really deliver a solution that works. I know it may almost sound ridiculous, but a solution that works is sometimes a rare thing in this area. We hear this a lot from customers: “I tried other solutions, and they’re just not working.” We go in, show them a demo, and they’re already convinced. Because our product is straightforward to use, you can see how it works and what it does immediately.

At the end of the day, customers are not looking for a complex solution. We make complex problems very simple. That’s one thing we do exceptionally well as a company.

That’s why we’re Simply the Best.

Check out our video here to see DMX-h in action, accessing and loading Mainframe data into Hadoop.

Let’s block ads! (Why?)

Syncsort blog

Best, Hadoop, Mainframe, Part, Simply, Syncsort
  • Recent Posts

    • ANOTHER SIMPLE EXAMPLE OF FASCIST NAZI LEFTISTS AT WORK
    • Nvidia and Harvard develop AI tool that speeds up genome analysis
    • Export with large E instead of small e
    • You’ll be back
    • Building AI for the Global South
  • Categories

  • Archives

    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited