Category Archives: big data

HP Vertica at the BBBT: Technology v Solution

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The latest BBBT presentation was from HP Vertica’s Will Cairns and Steve Sarsfield. I know it’s hard to miss HP’s presence in any market, but for those few of you who may have done so HP acquired Vertica in early 2011. Vertica is a columnar database focused on large data sources for analytics. Will and Steve were a good tag team, switching back and forth as need be; so unlike other presentation reviews I will rarely be noting who said what.

The smallest installation they mentioned runs on HP Vertica is 1.5 terabytes up to very large ones such as at Facebook, their largest customer. Without a doubt, HP plays at the larger end of the analytics market. They have a strong and powerful database and it seems HP’s hardware experience and Vertica’s database knowledge seems to have been integrated far better than other HP acquisitions in the previous decade.

The problem I often come back to discuss, whether talking about a startup or a company such as HP, is the issue of technical problems versus business solutions.

Will Cairns did say one thing that should be paid attention to by many who talk about unstructured data. His very accurate point is that “unstructured data doesn’t stay unstructured long.” We talk about conversations as unstructured, but to get information from those, we must part the syntax of sentences, look for key words and meaning, and extract semantics with meaning. Those items can then be similarly structured in order to compare, analyze and draw conclusions.

However, the weak spot in his eyes is his title. He constantly referred to “supporting data scientists” rather than supporting data science. As the programmers who know statistics create more and more packages that can analyze data, it’s the analytical capabilities being provided to business people that matters, not the people who call themselves data scientists who also just exist to serve the end business use.

One interesting techie note about their MPP database is that there isn’t an automatic lead node. While there’s no independent analysis for intelligence allocation of notes other than, it seems, basic load balancing, the idea that you can automatically define a lead node based on balancing, not before, does imply a good ability to manage distributed resources.

One thing I’ve asked a few folks who push columnar databases came up again in this presentation. They were talking about something called projections, which seemed to be ways to index the data for faster access. However, they claimed it’s not indexing but gave no clear explanation.

I then asked the question that always intrigues me. It’s clear that columnar databases have a great strength in analytics across records because indexes aren’t needed for columns, but it’s clear that both row and column based analyses have value, so getting a clearer picture how any database supports both would seem to be important. I pointed out that indexes in row-based databases exist to allow faster search of columns. The question is: What techniques are used to speed up row based searches in columnar databases if no indexes exist. They didn’t have an answer.

One slide that created a great conversation was one of the types of analytics and their definitions. Claudia Imhoff and others questioned the difference between predictive, prescriptive and pre-emptive analytics. While better clarity is definitely needed, the attempt is a great conversation starter for the industry.

HP Vertica - Hindsight to Foresight slide

Summary

HP Vertica seems to be a database that should be evaluated for large data volume analytics. However, they seem to have a focus on the technology not on why companies want the technology. There was no real discussion of results, or of partnerships with BI vendors to provide end user value. I expect that successful sales won’t be purely HP. They are focused purely on IT and programmers who are building very complex algorithms. They’ll need either a channel or ISV partner to round out the picture to an enterprise who needs to see the full business value chain.

It seems to be a very strong product, but only part of the solution.

TDWI, Claudia Imhoff and SAP: Data Architecture Matters

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In a busy week for TDWI webinars, today’s presentation by Claudia Imhoff, Intelligent Solutions, and Lother Henkes, SAP, was about how the continuing discussion of the place in the data world for the data warehouse.

While many younger techies think the latest technology is a panacea and many older techies are far too skeptical for too long, the reality is that while the data warehouse is going nowhere, it has to integrate with the newer technologies to continue improving the information being provided to business knowledge workers.

One of Claudia’s early slides talked about data sources. While most people are focused on both the standard packaged software and the rush of non-structured data from the Web, call centers, etc, Claudia makes clear the item that companies are just beginning to realize and address: Sensor data is just as important as the rest and also driving data volumes. Business information continues to come from further afield and a wider variety of sources and all must be integrated.

Much of her talk, she mentioned, has come out of a couple of years of work between herself and Colin White, in formalizing the changing data architecture environment. Data warehouses are still the place for production reports and analytics, where data provenance and clarity are absolutely necessary while the techniques used on early stage data such as in streaming, Hadoop analytics, etc, are more exploratory and investigative. The duo posit that the combination of data integration, data management (including EDWs), data analysis and decision management are the “glue in the middle,” those things that bind sources, deployment and distribution technologies, and reporting and analytics options into a real system that provides value.

The picture they put together is good and Claudia Imhoff’s presentation should be looked at for a better understanding of where we are; but I wouldn’t be me if I didn’t have a couple of issues.

The first is a that she is a bit too enamored of mobile technology. It’s here and must be addressed, but statements such as “nobody has a desktop, everything is mobile” must be corrected. A JD Power survey last year showed that only 20% of tablets are used for work. On the other side, Forrester Research has pointed out a strong majority of business people are now using two devices for their information.

The issue for business intelligence is not that people are switching from desktops (including laptops in docking stations) but that smart providers of information need to build UIs that address the needs of large monitors, tablets and smartphones, addressing each device’s uniqueness while ensuring a similarity of user experience.

The second issue is a new term thrown out during the presentation. It’s “data refinery” and, as Claudia mentioned in her presentation, it’s the same thing others are calling a data swamp, data lake or numerous other terms. There’s an easy term everyone has used for years: Operational Data Store (ODS). I’m a marketing guy and I understand the urge for everyone to try to coin a term that will catch on, but it’s not needed in this case.

While it’s a separate topic (yeah, another concept for a column!), I’ll briefly point out my objections here. Even back in the late 1990s, during my brief sojourn at Informatica, we were talking about how the ODS can be used for more than only a place to use in order to quickly extract information from operational system so as not to stress them by doing transformations directly from such systems. They’ve always been a place to take an initial look at data before beginning transformations into star schemas and the like. The ODS hasn’t changed. What’s changed is the underlying technologies that support larger data stores and the higher level analytics that let us better analyze what’s in the ODS.

That brings us to one main point Claudia Imhoff made during her wrap-up, the section on business considerations. She points out that people really need to understand the importance of each data source and the data within it. Just because we can extract everything doesn’t mean we need to save everything. Her example was with customer sampling. Yes, you can get all the customer data, but only that which you need to narrow cast. For higher level decision making, those who understand confidence levels know that sampling can get to very high levels of certainty so sampling can still speed decision making and save costs. Disk space might be less expensive in the Cloud, but it’s not free. We’re in the job of helping businesses improve themselves, so we need to look at the bigger picture.

Her presentation was clearly strategic: We need to rethink, not reinvent, data modeling. Traditional techniques aren’t going away and neither are many of the new ones. Data management people need to understand how they combine.

No surprise, that was a great transition to Lother Henkes’ presentation. His key point is that SAP BW now can run on SAP HANA. It’s important even if all the capital letters look like shouting. HANA is SAP’s in memory, columnar database that’s their entry into the Cloud market to manage the high volumes of modern data. It’s a move to bridge the gap between the ODS and relational database arenas with one underlying infrastructure.

In such a brief webinar, it’s hard to see more than the theory, but it’s a clear move by SAP to do what Claudia Imhoff suggested, to take a fresh look at data models in order to understand how to better support the full range of data now being incorporated into business decision making.

Datawatch at BBBT: Another contender and another question of message

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Yesterday’s presentation to the BBBT was by Datawatch personnel Ben Plummer, CMO, and Jon Pilkington, VP Products. As they readily admit, they’re a company with a long history about which most people in the industry have never heard. They were founded in the 1980s and went public in the 1990s. Their focus is data visualization, but much of their business has been reseller and OEM agreements with companies including SAP, IBM and Tibco.

The core of their past success was with basic presentation of flat file information through their Monarch product. It was only with the acquisition of and initial integration with Panopticon in 2013, providing access to far more unstructured data that they rebranded as data visualization and began to push strongly into the BI space.

The demo was very standard. Everyone wants to show their design interface and how easy it is to build dashboards. Their demonstration was in the middle of the pack. The issue I had was the messaging. It’s no surprise that everyone claiming to be a visualization company needs to show visualization, but if you’re not one of the very flashy companies, your message about building your visualization should be different.

Datawatch’s strengths seem to be two-fold:

  • Access a very wide variety of data sources.
  • Access in motion data.
  • Full service from data access to presentation.

While Ben’s presentation talked about the importance of the Internet of Things and that real-time data is transactional, Jon’s presentation didn’t support those points. Datawatch is another company working to integrate structured and non-structured data and they seem to have a good focus on real-time, those need to be messages throughout their marketing, and that means in the demo.

Back from that tangent to the mainline. The third point is a major key. Major ETL and data warehouse vendors aren’t going away, but for basic BI, it adds costs and time to have to look at both and ETL and a data visualization tool which may not work together as the demoware indicates (A surprise, I know…). The companies who can get the full stream data supply chain from source to visualization can much more quickly and affordably add value for the business managers wanted better BI. I know it’s a fine line in messaging that and still working with vendors who overlap somewhat, but that’s why Coopetition was coined.

They seem to have a good vision but they haven’t worked to create a consistent and differentiated message. That could be because of resources and hopefully that will change. In February of this year Datawatch issued a common stock offering that netted them more cash. Hopefully some of that will be spent to focus on created strong and consistent marketing. That also includes such simple things as changing press releases to be visible from the PR link as html, not just pdfs.

Summary

I know you’re getting tired of hearing the following refrain, but here it is again. The issue is that I’ve heard this message before. The market is getting crowded with companies trying to support modern BI that’s a blend of structured and unstructured data. Technologists love to tweak products and think that minor, or even major technical issues that aren’t visibly relevant to the market should sell the product all by themselves. Just throw some key market points on top of them and claim you have no competitors because your technology is so cool.

BI and big data are cool right now and there are a large number of firms attempting to fill a need. Datawatch seems to have the foundations for a good, integrated platform from heterogeneous data access to visual presentation of actionable information. That message needs to quickly become stronger and clearer. This is a race. Being in shape isn’t enough, you have to have the right strategy and tactics to win the race. Datawatch has a chance, will they stumble or end up on the podium?

MapR and Skytree Webinar Shows the Need for Product Marketing

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Yesterday I listened to a webinar by MapR and Skytree supposedly on Hadoop and machine learning. As someone with a background in artificial intelligence (from way back in the 1980s…), I was interested in the topic title. However, to be very sadly blunt, I came away not having learned anything.

The Presentation

There seemed to be two major problems:

1)    They didn’t know who was their target audience.

2)    They don’t know presentations.

I couldn’t tell if they were focused on a technical or business audience.  After all, they didn’t start the presentation by explaining either predictive analytics or machine learning. The MapR guy who opened mentioned he was going to describe machine learning and then only showed a table showing how machine learning can be used in multiple use cases. The table did didn’t differ between historical and predictive analytics nor did it emphasize predictive analysis. However, he talked about MapR as if everyone should understand what it is and how it works, so that might have been aimed at a technical audience but without technical details.

After a bit more blather, he turned the presentation over to the Skytree presenter who opened with the statement that the company exists to “translate big data into actionable intelligence.” That is some differentiator…

While he also failed to give a good explanation for machine learning and how it differs from any other type of analytics, he at least had one good slide about his company’s focus.

Skytree market slide

Skytree for High-Value Problems

How they’re going to do that and how they’re different than their competitors? Well, as the old statement from textbooks says, that’s an exercise left to the student.

The Need for Product Marketing

The entire webinar failed and I, being the humble sort I am, know why. It was not vaguely technical enough for a technical audience nor specific enough for a business audience. They never clearly differentiated their products, sticking to very generic messages. That’s often a clear symptom of something missing from companies: Product marketing.

What they had was one technical guy from MapR and one supposedly marcom guy from Skytree, neither of whom understood how to position a solution in clear terms. The key job of product marketing is inherent in the two halves of the name, understand product and create accurate messages for the market. Unfortunately, smaller companies have founders working closely with development and sales, using marketing just to create basic collateral and presentations.

There doesn’t seem to be someone who knows the market the companies were really trying to hit or how to explain their differentiators to that market. That’s why the presentation spent lots of time on platitudes and almost none of a focused message to communicate differentiators.

There may be a good solution hidden in the partnership, but this presentation did nothing to show it.

Teradata Aster at the BBBT. Is a technology message sufficient?

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Last Friday’s visitors to the BBBT were from Teradata Aster. As you’ve noticed, I tend to focus on the business aspects of BI. Because of that, this blog entry will be a bit shorter than usual.

That’s because the Teradata Aster folks reminded me strongly of my old days before I moved to the dark side: They were very technical. The presenters were Chris Twogood, VP, Product and Services Marketing, and Dan Graham, Technical Marketing.

Chris began with a short presentation about Aster. As far as it got into marketing was pointing to the real problem concerning the proliferation of analytic tools and that, as with all platform products, Aster is an attempt to find a way to address a way to better integrate a heterogeneous marketplace.

As with others who have presented to the BBBT, Chris Twogood also pointed out the R and other open source solutions aren’t any more sufficient for a full BI solution managing big data and analytics that are pure RDBMS solutions, so that a platform has to work with the old and the new.

The presentation was then handed over to Dan Graham, that rare combination of a very technical person who can speak clearly to a mixed level audience. His first point was a continuation of Chris’, speaking to the need integrate SQL and Map Reduce technologies. In support of that, he showed a SQL statement he said could be managed by business analysts, not the magical data scientist. There will have to be some training for business analysts, but that’s always the case in a fast moving industry such as ours.

Most of the rest of the presentation was about his love of graphing. BI is focused on providing more visual reporting of highly complex information, so it wasn’t anything new. Still, what he showed Teradata focusing upon is good and his enthusiasm made it an enjoyable presentation even if it was more technical than I prefer. It also didn’t hurt that the examples were primarily focused on marketing issues.

The one about which I will take issue is the wall he tried to set between graph databases and the graph routines Aster is leveraging. He claimed they’re not really competing with graph databases which was, Dan posited, because they are somehow different.

I pointed out that whether graphs are created in a database, in routines layered on top of SQL or in Java, or were part of a BI vendor’s client tools only mattered in a performance standpoint, that they were all providing graphical representations to the business customer. That means they all compete in the same market. Technical distinctions do not make for business market distinction other than as technical components of cost and performance that impact the organization. There wasn’t a clear response that showed they were thinking at a higher level than technological differences.

Summary

Teradata has a long and storied history with large data. They are a respected company. The question is whether or not they’re going to adapt to the new environments facing companies with the explosion of data that’s primarily non-structured and having a marketing focus. Will they be able to either compete or partner with newer companies in the space.

Teradata is a company who has long focused on large data, high performance database solutions. They seem to clearly be on the right path with their technology and the implications are that they are in their strategic and marketing focus. They built their name focused on large databases for the few companies that really needed their solutions. Technology came first and marketing was almost totally technically focused on the people who understood the issue.

The proliferation of customer service and Web data mean that the BI market is addressing a much wider audience for solutions managing large amounts of data. I trust that Teradata will build good technology, but will they realize that marketing has to become more prominent to address a much larger and less technical audience? Only time will tell.

TDWI Webinar Review: Business- Driven Analytics. Where’s business?

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Today’s TDWI webinar was an overview of their latest best practices report. The intriguing thing was the numbers show that BI & Analytics still aren’t business driven. As Dave Stodder, Director of Research for Business Intelligence, pointed out, there are two key items contradicting that. First, more than half of companies have BI in less than 30% of the organization, pointing out that a large number of businesses aren’t prioritizing BI. Second, most of the responses to questions about BI show that it’s still something controlled and pushed by IT.

One point Dave mentioned was still the overwhelming presence of spreadsheets. They aren’t going away soon. A few vendors who have presented at the BBBT have also pointed out their focus at integrating spreadsheets rather than ignoring all the data that resides in them or demanding everything be collected in a data repository. The sooner more vendors realize they need to work with the existing business infrastructure rather than fight against it, the better off the industry will be.

Another interesting point was the influence of the CMO. I regularly read about analysts and others talking about how the “CMO has a bigger IT budget than the CIO!” The numbers from the TDWI survey don’t bear that out. One slide, a set of tables representing different CxO level positions’ involvement in different areas of the IT buying process show the CMO up near the CIO for identifying the need, but far behind in every other category – categories that include “allocate budget” and “approve budget.” In tech firms, and especially in Silicon Valley, people look around at other firms involved in the internet and forget they’re a small subset of the overall market.

Another intriguing point was brought out in the survey. Of companies with Centers of Excellence or similar groups to expand business intelligence, the list of titles involved in those groups shows an almost complete dearth of business users. It seems that IT still thinks of BI as a cool toy they can provide to users, not something that business users need to be involved in to ensure the right things are being offered. Only 15% show line of business management involved while a pathetic 4% show marketing’s involvement.

The last major point I’ll discuss is an interesting but flawed question/answer table. The question was on how the business-side leadership is doing during different aspects of a BI project. The numbers aren’t good. However, as we’ve just discussed, business isn’t included as much as they should be. There are two things that make me consider:

  • What would the pair of charts look like if the chart was split to look at how IT and business respondents each look at the question?
  • Is it an issue of IT not involving business or business not getting involved when opportunities are presented?

Summary

TDWI’s overview of the current state of business-driven BI & analytics seems to show that there’s a clear demand from the business community but there doesn’t seem to be the business involvement need to finish the widespread expansion of BI into most enterprises.

What I’d like to see TDWI focus on next is the barriers to that spread, the things that both IT and business see as inhibitors to expanding the role of modern BI tools in the business manager’s and CxO suite’s daily decision making.

It’s a good report, but only as a descriptive analysis of current state. It doesn’t provide enough information to help with prescriptive action.

EXASOL at the BBBT: Big Data, fast database. Didn’t I just hear this?

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Friday’s EXASOL presentation to the BBBT brought a strong feeling of déjà vu. I’ve already blogged about the Tuesday Actian presentation and, to be honest, there were technical differences but I came to the same conclusion about the business model. But first, a thanks to Microsoft for the autocorrect feature. Otherwise typing EXASOL in all caps each time would have been bothersome.

The EXASOL presenters were Aaron Auld (@AaronAuldDE), CEO, and Kevin Cox (@KJCox), Director Sales and Marketing.

I mentioned technical differences. First, and foremost, they didn’t start with hardware but with an initial algorithm for massively parallel processing (MPP). They figured it was a great way to speed up database performance and stuck with columnar oriented relational technology. That’s allowed them to work on multi-terabyte systems with fast performance.

They have published some great TPC-H benchmark numbers, often being two orders of magnitude better than the competitors. While admitting that TPC stats are questionable since they’ve been defined by the big vendors to benefit their performance, often don’t reflect real life queries and often don’t use typical hardware, the numbers were still impressive. In addition, it was a smart business move as a small company blowing away the big vendors’ benchmarks helps elevate visibility and get them into doors.

However, let’s look back at Actian. They also talked about TPC, but they used the TPC-DS benchmark. How do you compare? Well, you can’t.

One other TPC factoid is, just like their competitor, there’s no clear information on true multi-user performance in today’s mobile age. No large numbers of connected clients was mentioned.

So results are great, but how do they fight the Hadoop bandwagon? They understand that open source is cheaper from a license standpoint, but also point out their performance saves in direct comparison when you total all costs for an implementation. People forget that while hardware prices have dropped, servers aren’t free.

Unfortunately, from a business model, it looks like they’re making the typical startup mistake of focusing on their product rather than business needs. They understand that ROI matters, but it seems to be too far down the list in their corporate messaging.

Another major advantage they have in common with the previous presenters is the sticking with SQL involves an easier build of ecosystem to include the existing vendors from ETL through visualization. However, they seem to be a bit further behind the curve in building those partnerships. While they have a strong strategic understanding of that, they need to bubble it up the priority list.

Exasol platform offering

One critical business success they have is their inclusion in the Dell Founders Club 50. That means advice and cooperation from Dell to help improve their performance and expand their presence. For a small company to have access not only to Dell at the technical level but also to bring customers to Dell Solution Centers for demonstrations is a great thing.

While they have been focused on MPP and large customers, the industry move to the Cloud also means they are looking at smaller licensing including a potential one-node free trial.

However, as mentioned in the lead, they seem to have the same business model issue as their competitors: They’re focused on the bleeding edge market who think the main message is performance. While they know there are other aspects to the buying decision, they went back, again and again, to performance. They have the whole picture in mind, but they’re not yet thinking of the mass market.

Organizations such as TDWI, Gartner and Forrester have all reported the high percentage of organizations that are considering big data and how to get a handle on the vast volume of information coming from heterogeneous sources. There’s clearly demand building up behind the dam. The problem seems to be they’re trying, as major IT organizations always do, to understand how best to integrate new technologies and capabilities with as little pain as possible. Meanwhile, the vendors seem to still be focused on the early adopters with their messaging. That leaves dollars on the table and slows adoption of new technology.

Summary

EXASOL seems to have a strongly performing and highly scalable database technology to work with large data sets. Yet, like many companies in the business intelligence space it comes back to audience. Are they still aiming at early adopters or will they focus on the mass market?

Have BI and big data advanced to the point where people need to think about the chasm and how to better address business needs not just technical issues. I think so, and I hope they adjust their business focus.

The company seems to have great potential, but will they turn that into reality? As the great Yogi Berra said, “It’s like deja vu all over again.”

Actian at the BBBT: Hadoop Big Data for the Enterprise Mass Market?

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In the mid-90s, Sybase rolled out its new database. It was a great leap forward in performance and they pushed it like crazy. Sybase’s claims were justified, but it was a new way to look at databases and Sybase loudly announced how different it was from what people were used to using. Oops. They sold almost none of it and hit a financial wall and they never quite recovered.

That came to mind during yesterday’s BBBT presentation by Actian. Their technology foundation goes back to Ingres and that means they’ve been in the database market a long time. The question is whether or not they’ve learned from past case studies.

The presenters were John Santaferraro, VP of Solution and Product Marketing, and Emma McGrattan, SVP Engineering. They gave a great technical overview of Actian’s offerings. Put simply, they’re providing a platform for Big Data access. At the core is Hadoop, but they’ve taken their deep understanding of RDBMS technology and incorporated SQL access. That clearly opens up two things:

  • Better access to partners for ETL and analytics
  • The ability for the mass of business analysts to get at Hadoop data to more easily perform their jobs.

That’s a great thing and I’ll discuss later whether they’re taking that technology to the right markets. Before that, however, I should point out the main competitive point they repeatedly hit on. TPC benchmarks are public, so they went out and compared themselves to who they consider, rightly, to be their main competition: Cloudera Impala. Their results are seen in the chart below.

Actian performance comparison

Actian’s TPC-DS comparison with Cloudera Impala

 

They returned to this time and time again. On the other hand, they discussed the full platform intelligently but only briefly.

They also covered more of the technology, and there’s a lot of it. As a Computer Associates company, they grow by acquisition. It’s not just a renamed Ingres, but has acquired, VectorWise, Versant, Pervasive and ParAcell. Many companies have had trouble acquiring and integrating firms, but the initial descriptions seem to be showing a consolidated platform.

One caveat: We had no demo. The explanation was the Hadoop Summit demo went so well that they’re in the middle of moving it to a new server and IT didn’t give a heads up. Believable, but again I personally am not too worried. As a former field guy, I know how little emphasis to put into a short demo.

So what did I think was the key technology, if not performance? That’s next.

Hadoop meets SQL

To folks focused on the largest data sets and others, as in car ownership, who like speed for the pure sake of it, the performance is impressive. To me, that’s not the key. Rather, it’s the ability to bridge the Hadoop-SQL divide. As John Santaferraro pointed out, orders of magnitude more business analysts and business users know SQL than know MapReduce and the related underpinnings of Hadoop.

Actian Hadoop platform for big data

Actian platform

While other Big Data companies have been building bridges to ETL, data cleansing, analytics and other tools in the ecosystem, custom work to do that is time consuming. Opening the ability to use standard, existing SQL tools means you can more quickly build a stronger ecosystem.

Why does that matter?

What is the market

During the presentation, the Actian team was asked about their sweet spot. Is it folks already playing with Hadoop who want better access to enterprise data or is it companies who’ve heard about Hadoop but haven’t stepped in yet to try because of all the questions. Their answer was the first group. I think that’s wrong, however, I understand why they are

Another statement from John was that they are in Silicon Valley and everyone there thinks everyone uses Hadoop because everyone there does. He admitted that’s not true out of the small region. However, sometimes it’s hard to fight the difference between what you intellectually know and what you’re used to. I’ve seen it in multiple companies, and I think it’s happening here.

The mass of global businesses haven’t yet touched Hadoop. It’s very different from what the typically overburdened and underfunded IT organization does, and that much change is scary. Silicon Valley is full of early adopters, it attracts them. In addition, there are plenty of early adopters out there for the picking. However, there are now a lot of vendors in the BI and big data spaces and we’re getting close to a tipping point. The company that figures out how to cross the chasm first is the one who will make it big.

It’s not pure performance that will attract the mass market, it’s how to get the advantages of big data in the most affordable way with the easiest transition path. It’s the ability to quickly leverage existing IT infrastructure and to join it with the newest technology.

Once again, it’s evolution rather than revolution that will win the day.

Summary

From what I saw of the platform, it’s a great start. The issue I see is the focus on the wrong market. The technology will always be important, but though it’s critical it only exists to solve the business problems. Actian seems to have a good handle on the technology and are on a path to integrate and leverage all the acquisitions into a solid platform, but will they be able to explain why that matters to the right market?

There is hope for that. One thing discussed is that their ability to bridge SQL and Hadoop means they are working on building partnerships with major vendors to extend their ecosystem. If they focus on that, they have a great chance of being very successful and being the company that brings Hadoop to the wider IT market.

Twitter: @actiancorp, @santaferraro & @emmakmcgrattan