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Think Economics -- Not Features -- When Evaluating Big Data Value

Posted: June 08, 2017   /   By: Mike Leone   /   Tags: Big Data, google, data warehouse, economic value validation, BigQuery

big_data_economics.jpg

Traditional enterprise data warehouse solutions helped to open the eyes of many organizations to the value of their data. Although these are significant systems, organizations quickly learned to monetize the actionable insight extracted from these systems, which led the rampant growth of the industry. Big data did not get big just from data growth. It got big because of its potential value, opportunities, and savings.

The more cost-efficiently you can capture a lot of data, plus the number of ways you can analyze it, equals the more worthwhile all that data could become. Value is results divided by costs. These (pseudo-)equations of big data value now extend not only to the disruptive power of transformative technologies like Hadoop, but also to increasingly popular cloud services for databases and data warehouses.

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Checking on Kudu

Posted: January 17, 2017   /   By: Nik Rouda   /   Tags: Data Management, data warehouse, big data and analytics, kudu, relational database

car.jpgCompromise is tough. I'd like a new car that gets over 50 miles per gallon, with 500 horsepower, comfortably seats 8, fits in a small parking spot, looks great, and costs less than $25,000. Not happening anytime soon.

Big data architects want it all. Efficient, fast, scalable, convenient, innovative, and inexpensive. They may have better options with Hadoop than I do with car buying. While HDFS and Hbase have proven themselves as sound choices for file system and NoSQL database approaches, Kudu is stepping up as a relational database that fits squarely between them.

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The Delta-V Awards: Engineered Systems for Analytics

Posted: January 08, 2016   /   By: Nik Rouda   /   Tags: Analytics, Big Data, Oracle, Teradata, data warehouse

engineered systemsESG's Delta-V awards recognize the top 20 companies that made an impact in big data and analytics in 2015. Here are the ones to applaud for their success in the category of Engineered Systems. You may be thinking, "wait, the whole point of big data is to go with commodity kit, not a big vendor appliance." You may also be wrong.

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A Striated Strategy at Strata

Posted: October 02, 2015   /   By: Nik Rouda   /   Tags: Analytics, Big Data, cloud, data warehouse, business intelligence

financial_analysisJust home from the latest Strata+Hadoop World in NYC, with over 6,700 participants and at least 150 vendors, and I wanted to share some reflections on the event and the big data market as a whole.

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Teradata Broadens the Base

Posted: June 08, 2015   /   By: Nik Rouda   /   Tags: Analytics, Big Data, Hadoop, Teradata, data warehouse

Del_Mar_BeachIn recent years, many were thinking that Teradata was losing its edge in the greater analytics marketplace. Long a favorite for large enterprise customers seeking to do world-class data warehouses, the company realized the market was rapidly shifting around it. Two major trends seem to have the biggest impact here: big data and cloud. While Teradata's core install base isn't at significant risk of going away, they may be looking at complementary alternatives. For the company to remain a dominant player, some new approaches would be required. The recent Teradata third party influencers event in Del Mar, California, showcased just how far their efforts at transformation have progressed.

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Leaders' Perspectives on Big Data: Technology Goals

Posted: April 17, 2015   /   By: Nik Rouda   /   Tags: Analytics, Big Data, Data Management & Analytics, database, data warehouse

Big-Data-ButtonI recently started a blog series based on research done by interviewing a number of leaders for big data and analytics initiatives. The first part in that blog series on leaders' perspectives on big data was specifically about business goals. This time we’re going to look a little more at the more technical objectives of big data, by which I mean technology goals.

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TDWI Gets Fresh

Posted: February 24, 2015   /   By: Nik Rouda   /   Tags: Analytics, Big Data, database, data warehouse, business intelligence

I'm a "big data" guy, in the broadest, most aggressively futuristic sense of the term. So when Tom Davenport opened the TDWI keynote saying he doesn't like the Kardashians and he doesn't like the term "big data," I was alarmed. Was this going to be another grandpa-style lecture on how business intelligence and data warehousing didn't need any of them new-fangled gizmos? You know the spiel, "Back in my day we did analytics on the way to school, for 10 miles, uphill both ways, in the snow, without shoes, and we were happy to do it!" When he started on his history of decision support analytics, I began to wonder if there there any earlier flights available out of Las Vegas that day....

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Teradata Takes the Turf for Big Data and Analytics

Posted: June 25, 2014   /   By: Nik Rouda   /   Tags: Analytics, Big Data, Data Management & Analytics, Enterprise Software, Teradata, data warehouse

It’s hard to picture the sport of big data and analytics without Teradata in the tournament. Depending on your loyalties, Teradata might be a favorite or you may be wishing for a new star to emerge. Either way, Teradata has long been a leader in traditional integrated data warehousing and data mart solutions, offering a wide range of platforms and tools for analytics and reporting.

Now the company is modernizing its portfolio with a much greater emphasis on big data and analytics and an inclusive approach to newer offerings such as Hadoop. The flexible range of multi-functional appliances will appeal to Teradata’s current enterprise customers who need to blend different data types for advanced analytics, although more must be done to show the broader market the value of appliances over commodity hardware and open source software.

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The Week That WaaS – BitYota and Redshift

Posted: November 29, 2012   /   By: ESG   /   Tags: Cloud Computing, Analytics, Data Management & Analytics, cloud, Private Cloud Infrastructure, Enterprise Software, Teradata, data warehouse, Amazon Web Services, BI, oracle cloud, Public Cloud Service

As the industry inevitably marches towards a widening set of cloud services, the layers of the IT onion separate and render themselves “as a Service.” Ironically perhaps, the industry started at the highest layer, applications or SaaS. But PaaS is gaining momentum, evidenced by nearly all the most well-known public clouds exhibiting a variety of PaaS and BI/analytics individual service slices, such as:

DBaaS = Database-as-a-Service
INTaaS = Integration-as-a-Service
DEVaaS = Development-as-a-Service
HaaS = Hadoop-as-a-Service, and its close cousin
BDaaS = Big Data-as-a-Service

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