Monthly Archives: September 2011
Informatica 9.1 Supports Best Practices For Agile Data Integration
Informatica supports Agile Data Integration for Agile BI with best practices that encourage good data governance, facilitate business-IT collaboration, promote reuse & flexibility through data virtualization, and enable rapid prototyping and test-driven development. Organizations that want to successfully adopt Agile Data Integration should standardize on the following best practices and leverage Informatica 9.1 to streamline the data integration process, improve data governance, and provide a flexible data virtualization architecture.
1. The business and IT work efficiently and effectively to translate requirements and specifications into data services (more…)
Video: Informatica Cloud @ Dreamforce 2011 #df11
I discussed cloud integration and Informatica Cloud momentum and customer success with SIIA’s Rhianna Collier at Dreamforce 2011. Some of the questions:
- Are times changing when it comes to the importance of data integration to cloud computing adoption and success?
- With the growth of social and mobile, how is innovation changing your customer needs?
- Are you getting different questions from customers now with all of the industry changes?
- Is the social revolution the focus of next year or is it more than that?
- How do you monitor customer interactions and measure the ROI?
Data Longevity, Data Lifecycle Extension, Data Governance
There were two facets of data sharing and data release (as manifested by the concept of transparency, but can be expanded to any situation where there is data repurposing) that differ from the traditional view of data. (more…)
Zombies! Yes, They Are Real and Lurking Everywhere
Zombies are pretty big in Hollywood these days. The movie Zombieland was a huge box office success and taught us some valuable lessons for surviving when zombies take over the world. There were over 30 rules revealed. My two favorites were: 1) Good Cardio – to outrun zombies and 4) Doubletap – shoot a zombie twice to make sure they are done.
Surprisingly, the movie never talked about ZOMBIE WORKFLOWS!!!
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Action Plan for Hadoop Data Integration: Conclusion of Hadoop Blog Series
I had the opportunity to review and comment on the draft of a new Hadoop technical guide. It’s great to see the published paper: Technical Guide: Unleashing the Power of Hadoop with Informatica. This guide outlines the following five steps to get started with Hadoop from a data integration perspective.
(1) Select the Right Projects for Hadoop Implementation
Choose projects that fit Hadoop’s strengths and minimize its disadvantages. Enterprises use Hadoop in data-science applications for log analysis, data mining, machine learning and image processing involving unstructured or raw data. Hadoop’s lack of fixed-schema works particularly well for answering ad-hoc queries and exploratory “what if” scenarios. Hadoop Distributed File System (HDFS) and MapReduce address growth in enterprise data volumes from terabytes to petabytes and more; and the increasing variety of complex multi-dimensional data from disparate sources. (more…)
Ultra Messaging Efficiency For Better Agility and Scalability
Our first post in this series on Efficiency covered the high-level performance benefits of super-efficient messaging software, whether you measure for latency or throughput, since efficiency is the property of software that provides performance. “Ultra-low latency” is just another term for extremely fast, lean, efficient execution. For more, see the post: Ultra Messaging is Also High-Throughput, High-Availability, Lower-TCO Messaging.
Our next post covered 24×7 availability, reliability and lower TCO from this efficiency. Less hardware and fewer software processes to touch the data in transit between applications provides these benefits. For more, see the post Ultra Messaging: For 24×7 High Availability, Lower TCO, and Robust Reliability.
This post discusses how the same Ultra Messaging efficiency that provides performance, reliability, and lower TCO also provides great agility and near-linear scalability. And with today’s Big Data challenges, especially in the capital markets, efficiency is more prized than ever.
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Data Transparency and Data Governance
I have harped about one specific aspect of data sharing and uncontrolled data repurposing: when the data consumer is not part of the requirements analysis and design processes, that consumer’s perception of data semantics must be based on his/her own reinterpretation. For example, if I were to access a data set, say from data.gov, that had a list of failed banks, I am fed a csv file with the following columns headers:
| Bank Name | City | State | CERT # | Acquiring Institution | Closing Date | Updated Date |
Agile Data Integration Maximizes Business Value In The Era Of Big Data
Adopting Agile may require a cultural shift and in the beginning can be disruptive to an organization. However, as I mentioned in Part 1 of this blog series, Agile Data Integration holds the promise to increase chances of success, deliver projects faster, and reduce defects. Applying Lean principles within your organization can help ease the transition to Agile Data Integration. Lean is a set of principles first explored in the context of data integration by John Schmidt and David Lyle in their book on Lean Integration. First and foremost Lean recommends an organization focus on eliminating waste and optimizing the data integration process from the customers’ perspective. Agile Data Integration maximizes the business value of projects (e.g. Agile BI, Data Warehousing, Big Data Analytics, Data Migration, etc.) because you can get it right the first time by delivering exactly what the business needs when they need it. Break big projects into smaller more manageable deliverables so that you can incrementally deliver value to the business. Agile Data Integration also recommends the following: (more…)
Making Big Data A Little Smaller
Big Data. The term has certainly caught on and the phenomenon is real. Every nanosecond of every day, more and more data is being created at ever increasing speeds. And since storage has become so economical, both cost and foot print, there are fewer compelling reasons for the enterprise to manage down the size of its databases. In response, new and emerging technologies such as universal database connectivity, complex event processing, connectivity to social network feeds and in-memory processing have been developed to better manage Big Data’s scale. While this is great news for the enterprise, it comes with some challenges in respect to business analytics. (more…)
The Future of Applications (2): Lowering Costs and Complexity
This is my second blog in a series on the subject of “Informatica & Applications”. You can read my previous blog here:
It’s strange how IT has become so complex. When we started out so many years ago computing was the answer to everything. It was going to make us more productive and was going to allow us to spend so much more time with our loved ones. Do you remember that time when people said “we’ll have a paperless office, and we’ll be finished by 5pm.”?
I wish!
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