The Waterline Data Blog

Feb 16, 2017

Big Data Smack Down: Round 1 – Best of Breed Stacks vs. All-in-One Solutions

Ladies and gentlemen, boys and girls of all ages, welcome to the World Data Federation’s giant cage match! In the corner to your left we have the straggle-toothed veterans—Informatica, IBM, and Oracle—dragging their legacy architectures into the big data age with so called “end-to-end solutions” and only “one throat to choke.” In the other corner, to your right, we have the young scrappy up and comers—Waterline, Trifacta, Arcadia, and Streamsets—building their platforms from scratch to run natively on Hadoop and Spark, with modern REST API architectures that allow for easy integration to create a custom “best-of-breed” big data stack!

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Topics: Big Data, Hadoop, Data Integration, Legacy Vendor Stack, Data management software

Feb 15, 2017

Introducing Smart Data Catalog 4.0 for Faster Use of Big Data

Today we’re very excited to announce early access availability of Smart Data Catalog 4.0, the latest version of the industry’s most trusted data catalog available.

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Topics: Big Data, Big Data governance, Compliance, Smart Data Catalog, tag based security

Feb 9, 2017

There will continue to be a natural conflict between big data and business self service

Two trends in big data have been tugging at each other particularly hard for the past several years and frankly, I find it amazing that more people aren’t talking about how these two trends are almost fundamentally opposed to one another. On one hand, there is the push for big data, and the general sentiment is, more, more, more – more data at a faster pace and with greater variety. This all falls under the train of thought that if I get more data, I can find new and bigger insights that will change my company or perhaps the world for better.

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Topics: Big Data, Big Data governance, Self service analytics, Hadoop, Data Redundancy, Smart Data Catalog

Feb 2, 2017

The Real Cost and Risk of Redundant Data

How big of a problem is data redundancy? If you are like most companies, it is much bigger than any one thing. 

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Topics: Big Data, Data Redundancy

Jan 31, 2017

2017 Prediction #3: The CDO Finally Moves into the BOARD ROOM

This particular prediction will be a little difficult to prove, but I will start off by stating that I am definitely not alone in my opinion about the rise of the CDO. Gartner recently wrote in its second CDO Survey (The State of the Office of the CDO), “Data- and analytics-related crises will continue to plague enterprises that do not implement the chief data officer (CDO) role and the office of the CDO.” This was heartening to see. While this topic has long been under discussion by data nerds like me, it had never really reached the mainstream. I think that’s about to change. Organizations do seem to be waking up (finally) to the strategic value of data.

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Topics: Big Data, Chief Data Officer

Jan 26, 2017

Waterline Data Welcomes Kaycee Lai and Todd Goldman to Executive Team

I’d personally like to welcome Kaycee Lai and Todd Goldman to our executive team.

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Topics: Waterline Data

Jan 24, 2017

2017 Prediction #2: Orgs Realize Hadoop Wasn’t the Panacea They Thought

As I continue these blog posts about my predictions for 2017, my mind wanders back to 2014 when Cloudera had just taken a massive investment from Intel, and Hortonworks had just gone public. Ah yes, I remember those good old days when Hadoop was going to eliminate the need for ETL, and data quality would no longer be needed, because the volume of data would make data quality issues operate at the noise level.


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Topics: Big Data, Hadoop

Jan 17, 2017

2017 Prediction #1: Big Data Will Continue to be Too Damn Hard for Orgs (Until They Admit It)

Do you know what DCE is or was? What about CORBA? These were distributed computing architectures designed to help create scalable applications. But most of you probably never heard of these technologies, because they never went anywhere. They more or less died on the vine. And do you know why? You guessed it: they were just too damn hard.


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Topics: Big Data, Hadoop

Jan 10, 2017

Pulling Data out of the ‘Fog’ (Part 3)

In my last post, I wrote how most organizations are relying on the street light method to find data. They’re searching for data only where they can see, not where the data might actually located. The result: they don’t know what data is even available. Tribal knowledge can help, but results are often spotty. People forget. People leave. People make mistakes.

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Topics: Big Data, Big Data governance

Jan 3, 2017

2017 Big Data Predictions from OUR NEW CMO Todd Goldman

Hi, and welcome to my first blog post at Waterline data.

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Topics: Big Data, Hadoop, Chief Data Officer