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Uber Goes Unconventional: Using Driver Phones As A Backup Datacenter

A revolutionary step in the Datacenter Backup/Replication and Storage Technology – different from the traditional database replication approach wherein the the master data center is considered the Golden Source and Reservoir of Data and in case of any fail-over, the Master Data store overwrites the client side data (which in turn may affect the end user experience by wiping/overwriting any recent data) :

Happy Learning!! 🙂

Image Source


Infographic: How to Explain #BigData to your Grandmother

Big Data Infographic


Source Link

Data Footprints by Generations [Infographic]

Interesting Infographic!!

Data Footprints by Generations

Via: Wikibon Infographics

Big Data – A Visual History

Big Data – A Visual History (Timeline)

Source Site :

Big Data Will Change Our Lives

PDC-2013 – Day-2 Summary (Data Mgmt/Int Track)

ImageThe Day-2 of the PDC-2013 was full of breakout sessions and were split into 4 different tracks:

  • Optimizing System Operations
  • Data Management / Integration
  • Business Visionary
  • User Experience

The attendees were allowed to choose any one track and stick to it for the entire day. There were restrictions in swapping or attending sessions from multiple tracks. From a personal front, I did not like this idea as it was more forced to attend all the sessions pertaining to one track. Attendees should have been given session selection, along with the tracks.

The deep-dive sessions were taken by a group of enthusiast developers and specialists from the data management and integration group of Pegasystems. The speakers were the people who coded the features we see in Pega7. I opted for the “Data Management / Integration”

As a part of these sessions, I felt like a school and college going student taking running notes, through-out the session.

Will be sharing the notes that I took as a part of these sessions :

Notes from the Data Management / Integration Track :

What are the challenges we face with respect to data management :

  • huge scale
  • reusability
  • procedural load
  • tight coupling

The Data Management initiative for the Pega7 development had the following Vision items :

  • Reusability
  • On-Demand Access
  • Usage and source decoupled
  • Caching & Performance
  • Improved Tooling

Changes and new features from a Reusability front :

  • parametarized data pages
  • multiple data sources

Changes and new features from a On-demand access front :

  • autopopulate property
  • direct data-page access

Changes and new features from a usage and source decoupling  front :

  • data source virtualization
  • data source simulation

Changes and new features from a caching and performance front :

  • asynchronous loading of data pages (on-demand or procedural)
  • enhanced caching mechanism
  • new addition of data page alerts
  • refresh strategies (time-based, conditional or reload)

(Pega shared that Pega 7 is 5000% faster response than previous versions. And on a personal front am very curious and eager to know, what gave a nitro booster performance to the product)

Changes and new features from an improved tooling front :

  • wizards for making life easy with the click of buttons and configurations
  • tools to identify and notify threshold limits
  • tools for pruning based on least recent usage items
  • integration tools

One of the concepts which I liked was:

Data Source Virtualization : 

For this features any new data source can be added, for eg: data exposed by BestBuy, Google or my custom database.

  • Once the new data source is added, a data transform can be placed to absorb or use a bunch of properties/parameters exposed by the services (eg:BestBuy/Google)
  • Based on the transformation, necessary application properties can be mapped (based on naming conventions)
  • This actually makes the Application independent of the Data Source – having a loose coupling
  • It provides a flexibility to add a Data Source of any class at any point in time – which was not the case earlier
  • The Data Pages can be defined as D_Location, D_Weather, D_ProductList or D_Offers

PN: Collected the notes for the Executive Panel from a fellow attendee

The last session was the Pega Executive Panel,a Q&A session. Key highlights:

  • No lightweight version planned, as it sees a lot of opportunities in marketplace to resolve complex business challenges
  • Pega wants to move to a partner led delivery model, with Pega in a advisory role and focusing more on license

Please do share your thoughts and inputs on the session – if you attended and i missed out to mention it in my blog 🙂

Learning Avenues :

If you are looking for Pega7 Personal Edition and eagerly waiting for it like me 🙂  – have a hawk eye on this link

Happy Reading!! 🙂

Who’s Big in Big Data? (Infographic)

Taming BigData

Taming Big Data | A Big Data Infographic
Via: Wikibon Big Data

How Big is BigData ?

How Big is BigData ? – Nice and Crisp Video

Happy Learning 🙂

Making Sense from BigData

The buzzword “bigdata” is very much in the air and is the most talked about topic these days. So the elements driving it also becomes very important.

As Albert Einstein has very rightly quoted

“Any Fool can know…..but the point is to understand”

The same quote also holds good for the concept driving Big Data which deals with variety and volume of data, that is unstructured and flows at different velocities. But now the question is “How do we make sense from this huge data dump“.

“Data without a sound approach would be as good as a Noise”

Here is an approach to make sense from the data dump and use it as on opportunity to serve business better :

  • Look : i.e. Searching,
    • finding for some interesting stuff in the big data stack
  • Listen : i.eMachine Learning,
    • figuring out what is important and what is not or classify & cluster the data
  • Learn :  i.e. Information Extraction,
    • getting necessary, useful and important facts from the data
  • Connect : i.e. Reasoning,
    • putting different facts together and find out a conclusion
  • Predict : i.e. Data Mining,
    • mining rules from the data to get some pattern
  • Correct : i.e. Optimization,
    • figuring out what is the right thing to do

Happy Learning!! 🙂


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