Big Data and the Telecom World

The complicated world of telecommunications analytics continues to be a primary driver behind complex data analytics solutions and I find it mentioned time and time again in Big Data use cases and scenarios.

Those of us who have lived in this world for years will probably agree with me that we’ve been pioneers in “Big Data techniques” ever since we were asked to build CDR (call detail record) data warehouses. My first CDR solution was for customer service and marketing at AT&T in the 1990s. We used Oracle for the DW and hired PhD statisticians to build models for predictive analytics on top of that data.

The marketing department was able to utilize that data to better understand customer patterns of usage and make data-driven decisions about how to package subscriptions and products. The call center team used the analytics from our cubes for market basket and association algorithms that provided reps with the ability to cross-sell to customers, which was also used by sales for up-sell opportunities to corporate accounts.

Then there is also the mass amounts of streaming data coming from network equipment which was used by engineering and the NOC for troubleshooting, predicting outages and tuning the network. Rules for correlation, thresholds and root-cause were needed to make sense of the 1000s of events/sec and not overwhelm systems and dashboards.

Does that sound familiar to today’s “Big Data use cases”? It should. We used to call these techniques CEP (complex event processing) and VLDB (very large databases). Really, at the end of the day, what this meant was that our DBAs, architects and developers needed to think about scale and distributed architectures to account for the scale that we were dealing with.

Today, it is a nice evolution of these techniques to see Hadoop, sharded databases, NoSQL and in-database analytics providing packaged, easier ways to process and manage systems of TB & PB scale.

Essentially, what this means is that these techniques now become available to all IT departments and examples like the churn & customer analytics (the holy grail of telcos is churn management) solutions become better, faster with improved data sampling because of new, emerging Big Data technologies.

I found this story on the Internet by Harish Vadada from Telecom Cloud here. It talks about T-Mobile with databases like Oracle & SQL Server using Big Data technologies such as Hadoop, to improve the delivery of customer & churn analytics to drive both the bottom-line and top-line of their business. Very impressive and spot-on to what I am saying here in this post.

Cheers! Mark


PSSUG November 2012 Presentation: Big Data with SQL Server

Thank you all for coming out on a rainy, snowy, cold evening to join us for this month’s PSSUG meeting!

Here is a link to the slides that I used tonight during my presentation of Big Data with SQL Server and Hadoop demos:

Br, Mark

Hortonworks on Windows – Microsoft HDInsight & SQL Server – Part 1

I’m going to start a series here on using Microsoft’s Windows distribution of the Hadoop stack, which Microsoft has released in community preview here together with Hortonworks:

Currently, I am using Cloudera on Ubutnu and Amazon’s Elastic MapReduce for Hadoop & Hive jobs. I’ve been using Sqoop to import & export data between databases (SQL Server, HBase and Aster Data) and ETL jobs for data warehousing the aggregated data (SSIS) while leaving the detail data in persistent HDFS nodes. Our data scientists are analyzing data from all 3 of those sources: SQL Server, Aster Data and Hadoop through cubes, Excel, SQL interfaces and Hive. We are also using analytical tools: PowerPivot, SAS and Tableau.

That being said, and having spent 5 years previously @ Microsoft, I was very much anticipating getting the Windows distribution of Hadoop. I’ve only had 1 week to play around with it so far and I’ve decided to begin documenting my journey here in my blog. I’ll also talk about it so far, along with Aster, Tableau and Hadoop on Linux Nov 7 @ 6 PM in Microsoft’s Malvern office, my old stomping grounds:

As the group’s director, one of the reasons that I like having a Windows distribution of Hadoop is so that we are not locked into an OS and can leverage the broad skill sets that we have on staff & off shore and so that we don’t tie ourselves to hiring on specific backgrounds when we analyze potential employee experience.

When I began experimenting with the Microsoft Windows Hadoop distribution, I downloaded the preview file and then installed it from the Web Installer, which then created a series of Apache Hadoop services, including the most popular in the Hadoop stack that drives the entire framework: jobtracker, tasktracker, namenode and datanode. There are a number of others that you can read about from any good Hadoop tutorial.

The installer created a user “hadoop” and an IIS app pool and site for the Microsoft dashboard for Hadoop. Compared to what you see from Hortonworks and Cloudera, it is quite sparse at this point. But I don’t really make much use of the management consoles from Hadoop vendors at this point. As we expand our use of Hadoop, I’m sure we’ll use them more. Just as I am sure that Microsoft will expand their dashboards, management, etc. and maybe even integrate with System Center.

You’ll get the usual Hadoop namenode and MapReduce web pages to view system activity and a command-line interface to issue jobs, manage the HDFS file system, etc. I’ve been using the dashboard to issue jobs, run Hive queries and download the Excel Hive drive, which I LOVE. I’ll blog about Hive next, in part 2. In the meantime, enjoy the screenshots of the portal access into Hadoop from the dashboard below:

This is how you submit a MapReduce JAR file (Java) job:

Here is the Hive interface for submitting SQL-like (HiveQL) queries against Hadoop using Hive’s data warehouse metadata schemas: