Data Aggregation – Informatica Perspectives

Category Archives: Data Aggregation

Big Data Have You Flummoxed? Join Our ‘Hadoop Tuesdays’ Webinar Series, Starting This Month

by Joe McKendrick on September 12, 2011 – 9:42 am

This fall, I have the fantastic privilege of moderating a series of informative Webcasts, called “Hadoop Tuesdays,” co-sponsored by Informatica and Cloudera, on the phenomenon sweeping the data management space known as Hadoop.

Big Data may be the problem, but Hadoop is the answer. Hadoop is an open-source software framework that enables applications to run across large arrays of nodes, accessing petabytes’ worth of data. It was originally created by Doug Cutting to support the open-source Nutch search engine project, which is now part of the Apache Lucene text-search library. ‘Hadoop’ was actually named after Cutting’s son’s toy elephant – a fitting analogy for the Big Data challenges that lie ahead.

The series kicks off on September 22nd with a “TweetJam” over the Twitter network – simply check in at Noon Eastern Time that day with hashtags #Hadoop or #infatj. Read More »

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Why MDM and Data Quality is Such a Big Deal for Big Data

by Ravi Shankar on May 26, 2011 – 11:11 pm

Big Data is the confluence of three major technology trends hitting the industry right now: Big Transaction Data (describing the enormous growing volumes of transactional data within the enterprise), Big Interaction Data (describing new types of data such as Social Media data that are impacting the enterprise), and Big Data Processing (describing new ways of processing data such as Hadoop). If you can imagine companies having problems with business-critical master data such as customers, products, accounts, and locations at current data volumes, now that problem is compounded many-fold with the growth into Big Data. That’s where MDM and Data Quality come in as the fundamental solutions. So, why is MDM and Data Quality such a big deal for Big Data? Read More »

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Harnessing Social Media With Informatica

by Ravi Shankar on April 6, 2011 – 12:02 am

Improving sales and service through customer centricity requires listening to and understanding your customers. And where are customers speaking these days?

You guessed it—social media. Just think about it. Each day, customers tweet 50 million times on Twitter and update their Facebook status 60 million times. Add in LinkedIn and user reviews and YouTube and blog commentary and more and you’ve got a customer data gold mine and a new frontier for marketing.

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More Selling Points Of Data Services and Data Abstraction

by David Linthicum on July 29, 2010 – 10:14 am

As we discussed in my last blog post, when building a SOA, data abstraction is the single most important approach and enabling technology when it comes to managing data within a SOA.

“Data abstraction is the key. It allows you to fix issues with the existing physical databases within the data service itself. Moreover, you can combine many different databases, and even unstructured information, into a single unified view of the data that is more representative of the business.”

Let’s walk further down this road. When using the approach of data abstraction, and data abstraction using data services, you’re able to emulate the desired data and data structure without having to recreate and restructure the physical databases, nor having to statically bind the services to the physical data. The core value of this is agility, but there are many other advantages as well, including: Read More »

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If You Have A Data Integration Round Hole, Don’t Buy Square Pegs

by David Linthicum on May 18, 2010 – 5:50 am

I’m often taken aback by the focus on technology as “the solution,” and not as an approach to the solution. One of the reasons I blog for Informatica is because they do focus on the solution and not the tool. Believe me, vendors will sell plenty of tools if they provide those tools in the context of the solution.

So, how do you find the right data integration tool? It’s really a matter of understanding your own data integration requirements, and creating baseline models for what the existing “as is” state is. This means you must understand your data at the structure and model levels, or, more simply put, you must understand what you have, where you have it, and what it’s doing. Read More »

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