Ben Werther announcing the general availability of the Platfora BI:

At Platfora, we made a bet that Hadoop’s destiny wasn’t simply to be a cheaper, slower cousin of the relational data warehouse. […] Hadoop is superb at two things — it provides a near-infinite data reservoir where data of all kinds can be landed without needing to figure out how it will be used ahead of time, and it is a slow lumbering freight-train of an engine for crunching and aggregating batches of millions or billions of rows.

They are neither the first, nor the last to understand and bet on Hadoop. But in some cases this bet originates only in the financial potential of the Hadoop market and less so on the technological potential.

Indeed it’s rarely the case that these two can leave alone. When they do, it leads to either a smaller market segment or to a shorter life time. Looking around at what’s happening in the Hadoop space, technologically and business wise, I assume many economists would recognize the signs of a long lived opportunity.

As a side note, I find it interesting that very few articles are looking at two other fundamental aspects of the Hadoop platform, which, in my opinion, were, are and will remain critical to the growth of this market: open source and extensibility. Without any of these two, what would we see would be tons of copy cats wasting resources in creating small indistinguishable clones, plus countless and endless negotiations to extend and integrate the platform. Hadoop is open source and the open source developers working on it have built it with extensibility in mind. The proof is out there and is clear: look at the breadth and depth of the tools around Hadoop.

That’s the power of open source. The way of the future.

Original title and link: Recognizing the Power of Hadoop: Platfora BI Is Better on Hadoop (NoSQL database©myNoSQL)

via: http://www.platfora.com/bi-is-better-on-hadoop/

Hadoop certainly plays a key role in the big data revolution, so all Business Intelligence (BI) vendors are jumping on the bandwagon and say that they integrate with Hadoop. But what does that really mean?

Boris Evelson suggests 7 questions to clarify the kind and level of integration with Hadoop each BI vendor is providing. While not my space, most often I hear about this integration it only means: “we can use Hadoop as an ETL tool”.

Original title and link: 7 Questions to Understand What Type of Hadoop Intergration BI Vendors Mean (NoSQL database©myNoSQL)

via: http://blogs.forrester.com/boris_evelson/12-09-25-what_do_bi_vendors_mean_when_they_say_they_integrate_with_hadoop

Pricing takes a bit of work to nail down; it pays to talk to the nice salespeople and let them help you sort it all out. All of these BI suites have free software downloads, and free community support. All of them offer multiple modules, custom support and engineering services, and training and documentation. So the answer to “how much will it cost” is always “it depends.”

Software licenses and custom engineering are especially “it depends.” Your total first-year costs can easily hit $10,000 for a small shop. Presumably you’ll spend less on support and training after your first year. Still, that’s considerably less than the traditional proprietary offerings from SAP, IBM Cognos, SAS and other old-time proprietary BI vendors.

The 5 solutions mentioned: Jaspersoft, Spargo, Pentago, Openl, Actuate.

Original title and link: 5 Business Intelligence Suites (NoSQL database©myNoSQL)

via: http://www.smallbusinesscomputing.com/print/biztools/5-best-linux-business-intelligence-suites.html

Philip Russom clarifies some myths about Hadoop and MapReduce circulating inside the BI community:

1. Hadoop consists of multiple products.
2. Hadoop is open source but available from vendors, too.
3. Hadoop is an ecosystem, not a single product.
4. HDFS is a file system, not a database management system (DBMS).
5. Hive resembles SQL but is not standard SQL.
6. Hadoop and MapReduce are related but don’t require each other.
7. MapReduce provides control for analytics, not analytics per se.
8. Hadoop is about data diversity, not just data volume.
9. Hadoop complements a DW; it’s rarely a replacement.
10. Hadoop enables many types of analytics, not just Web analytics.

I do hope this lack of information and misconceptions are not real as otherwise some BI careers would really be endangered.

Original title and link: Busting 10 Myths About Hadoop (NoSQL database©myNoSQL)

via: http://tdwi.org/Articles/2012/03/20/Busting-10-Hadoop-Myths.aspx?Page=2&p=1

Harish Kotadia:

Predictive Analytics has been billed as the next big thing for almost fifteen years, but hasn’t gained mass acceptance so far the way ERP and CRM solutions have. One of the main reason for this is the high upfront investment required in Software, Hardware and Talent for implementing a Predictive Analytics solution.

Well, this is about to change – […] Using R, HBase and Hadoop, it is possible to build cost-effective and scalable Big Data Analytics solutions that match or even exceed the functionality offered by costly proprietary solutions from leading BI/Analytics software vendors at a fraction of the cost.

Vendors will argue that software licensing represents just a small fraction of the costs of implementing BI or data analytics. What they’ll leave out is the costs of acquiring know-how and more important, the costs of maintenance and modernization of their solutions.

Original title and link: Hadoop, HBase and R: Will Open Source Software Challenge BI & Analytics Software Vendors? (NoSQL database©myNoSQL)

via: http://smartdatacollective.com/hkotadia1/45540/big-data-will-open-source-software-challenge-bi-analytics-software-vendors

Data science and BI differ in the foci of their  investigations. DS is consumed with supporting the development of data products. As Monica Rogati of LinkedIn notes, “On one side, I’ve been working on building products … The other side is finding interesting stories in the data.” BI, on the other hand, is all about measuring and managing business performance. At their best, though, both disciplines have an evidenced-based “science of business” foundation that makes me reject the contention by some that data science has a higher calling and is more scientifically sophisticated than BI.

Steve Miller puts the accent on the difference of maturity of the two fields. I’d say the difference in the approaches is even more important.

Original title and link: Data Science and BI: Similarities and Differences (NoSQL database©myNoSQL)

via: http://www.information-management.com/blogs/data-science-BI-database-Hadoop-Enzee-10021757-1.html

Alistair Croll for O’Reilly:

Big data is a successor to traditional BI, and in that respect, there’s bound to be some bloodshed. But both BI and big data are trying to do the same thing: answer questions. If big data gets businesses asking better questions, it’s good for everyone.

Big data is different from BI in three main ways:

• It’s about more data than BI, and this is certainly a traditional definition of big data.
• It’s about faster data than BI, which means exploration and interactivity, and in some cases delivering results in less time than it takes to load a web page.
• It’s about unstructured data, which we only decide how to use after we’ve collected it and need algorithms and interactivity in order to find the patterns it contains.

Original title and link: What’s the Relationship Between Traditional Business Intelligence (BI) and Big Data? (NoSQL database©myNoSQL)

via: http://radar.oreilly.com/2011/11/big-data-business-enterprise.html

Jeff Kelly:

To reiterate, there’s still plenty of work to do on the infrastructure layer of Hadoop and other Big Data approaches. But the focus of the Big Data industry is — and should be — moving to include analytics and visualization.

Differently put data is not the end goal.

Original title and link: Big Data Focus Shifting to Analytics and Visualization (NoSQL database©myNoSQL)

via: http://wikibon.org/blog/hadoop-big-data-focus-shifting-to-analytics-and-visualization/

According to Bill Cabiro citing Tableau software the answer is visual analysis:

[…] visual analysis is not a graphical depiction of data. Virtually any software application can produce a chart, gauge or dashboard. Visual analytics offers something much more profound. Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces.

I’m not sure that a tool providing data visualization with investigative capabilities qualifies as a business intelligence solution. But I can agree it can be quite sexy for the C-level people.

Original title and link: What Is Business Intelligence 3.0? (NoSQL database©myNoSQL)

via: http://blog.strat-wise.com/2011/08/04/what-is-bi-30.aspx