What Is A Data Hub?
The following is a transcript of the video interview above with Jeff Carr, CEO and Co-Founder of SlamData.
Well, I think the idea a data lake has been around for a few years. It really sort of came out of a lot of what was happening in the Hadoop market. And it was this idea that you could take all of your different data sources inside of a company, and combine them in a single source of truth.
And, generally, it’s a good idea and there’s value to it. But I think for a lot of companies, getting the data in was one thing. But, actually getting it out, and getting analytic value out of it, was a different thing. In many cases these projects failed because they weren’t able to do that efficiently.And, what we've done at SlamData: We've really taken that idea and made it more agile, and more useful. And we refer to it as a Data Hub.Click To Tweet
…where rather than saying I’m going to park every single possible source of data I can in one place… I’m going to take the different data sources that I have and combine them in a Data Hub, or data ponds if you will, that are relevant to a particular business focus — whether that’s my finance department, or my marketing department, or whatever — and then use SlamData to be able to essentially create what amounts to an agile data warehouse.
So instead of some 6 or 12 month project to build a data warehouse, you can stand up a Data Hub in a couple of days — even an afternoon — pulling in data from 6 or 8 different sources. Immediately users can get analytic value from it. And, iterate, iterate, iterate really fast — getting value from data sources that previously you either couldn’t or wouldn’t since it would take months to do.
You know everyone wants to talk about agility. Whether it’s agile development or agility of one kind or another. And, what we’re really talking about is the ability to create agile data warehouses, or Data Hubs, that you can stand up in a few days, get value from. And, iterate quickly or in some cases just tear it down and move on if you’ve gotten value you need.
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The Characteristics of NoSQL Analytics Systems
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns
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