Next Big Thing: The Data Explosion

These are the main points from The Data Explosion section of my Next Big Thing In Big Data presentation.

• The data explosion started in the 1960s
• Hard drive sizes double every 2 years
• Data expands to fill the space available
• Most data types have a natural maximum granularity
• We are reaching the natural limit for color, images, sound and dates
• Business data also has a natural limit
• Organizations are jumping from transactional level data to the natural maximum in one implementation
• As an organization implements a big data system, the volume of its stored data “pops”
• Few companies are popping so far
• The explosion of hype exceeds the data explosion
• The server, memory, storage, and processor vendors do not show a data explosion happening

Summary

• The underlying trend is an explosion itself
• The explosion on the explosion is only minor so far
• Popcorn Effect – it will continue to grow

Please comment or ask questions

Pile-On: Dan Woods “Lessons From The First Wave Of Hadoop Adoption”

Dan Woods put out a nice piece yesterday on his Forbes blog titled “Lessons From The First Wave Of Hadoop Adoption“.

I agree with him that the insights and advantages of Big Data solutions need to be described in ways other than technology. I’m going to add on to his insights.

1. It’s about more than big data. It’s a new platform.

Yes, it is a new platform.  That means it’s different than the old ones. The fact that you can do some things cheaper than you could before is not the main idea. A bigger story is that some things that were economically not possible before, now are. But the main idea is that this is a new platform, with new capabilities, that needs to fit into your existing data architecture.

2. Don’t get rid of your data warehouse

I completely agree. Big Data technology is a new tool with new characteristics. Using it to replace a Data Warehouse technology that is finely tuned for that use case is not a great idea. Don’t listen to the “Hadoop will replace every database within x years” crowd. No database has managed to replace every database. No database ever will because the variety of the use cases is too large.

3. Think about your data supply chain

Since a Big Data system needs to fit in with everything you currently have and operate, integration is a significant priority. Understand that with Big Data you can build a Big Silo, but a Big Silo is as bad as a small silo (just a lot bigger). You should not be required to pump all your data from every system into Hadoop to get value from it. Design you data architecture carefully, the implications and fallout of getting it right or wrong are significant.

4. It’s complicated

Yes it is. It’s also not cheap to do it well. Sure you can download a lot of open source software and prototype or prove your ideas without a lot of upfront outlay. But putting it into production is a production. Expect that.

Union of the State – A Data Lake Use Case

Many business applications are essentially workflow applications or state machines. This includes CRM systems, ERP systems, asset tracking, case tracking, call center, and some financial systems. The real-world entities (employees, customers, devices, accounts, orders etc.) represented in these systems are stored as a collection of attributes that define their current state. Examples of these attributes include someone’s current address or number of dependents, an account’s current balance, who is in possession of laptop X, which documents for a loan approval have been provided, and the date of Fluffy’s last Feline Distemper vaccination.

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State machines are very good at answering questions about the state of things. They are, after all, machines that handle state. But what about reporting on trends and changes over the short and long term? How do we do this? The answer for this is to track changes to the attributes in change logs. These change logs are database tables or text files that list the changes made over time. That way you can (although the data transformation is ugly) rewind the change log of a specific field across all objects in the system and then aggregate those changes to get a view over time. This is not easy to do and assumes that you have a change log. Typically, change logs only exist for the main fields in an application. There might only be change logs on 10-20% of the fields. So if you suddenly have an impulse so see how a lesser attribute has changed over time you are out of luck. It is impossible because that information is lost.

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This situation is similar to the way that old school business intelligence and analytic applications were built. End users listed out the questions they want to ask of the data, the attributes necessary to answer those questions were skimmed from the data stream, and bulk loaded into a data mart. This method works fine until you have a new question to ask. The Data Lake approach solves this problem. You store all of the data in a Data Lake, populate data marts and your data warehouse to satisfy traditional needs, and enable ad-hoc query and reporting on the raw data in the Data Lake for new questions.

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A Data Lake can also be used to solve the problems of history and trending for workflow applications and state machines. What if these applications write their initial state into the Data Lake and then also write the change of every attribute in there as well? While we are at it, let’s log all the application events coming from the user interface tier as well. From the application’s perspective this is a low-latency fire and forget scenario.

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Now we have the initial state of the application’s data and the changes to of all of the attributes, not just the main/traditional fields. We can apply this approach to more than one application, each with its own Data Lake of state logs, storing every incremental change and event. So now we have the state of every field of (potentially) every business application in an enterprise across time. We have the “Union of the State”.

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With this data we have the ability to rewind the Union of the State to any point in time. What are the potential use cases for the Union of the State?

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Enterprise Time Machine

Suppose something happened a few weeks ago. Decisions were made. Things changed. But exactly what, when, and why? With an Enterprise Time Machine you can rewind the complete state of every major application to any point in time and then step forward event by event, click by click, change by change, at the millisecond level if things happened that quickly. For an e-commerce vendor this means being able to know for any specified millisecond in the past how many shopping carts where open, what was in them, which transactions were pending, which items were being boxed, or in transit, what was being returned, who was working, how many customer support calls were queued and how many were in progress. In different domains such as financial services or healthcare, the applications and attributes are different but the ability is the same.

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In order to reconstruct the state at any point in time we need to load the initial snapshot into a repository and then update the attributes of each object as we process the logs, event by event, until we get to the point in time that we are interested in. A NoSQL store such as MongoDB , HBase, or Cassandra should work well as the repository. This process could be optimized by adding regular snapshots of the whole state into the Data Lake so that we don’t have to process from the very beginning every time. For a detailed analysis you could rebuild the state to a particular point in time and then process forwards in increments of any size. This way the situation of a device failure that led to a catastrophic cascade of events can be re-created and examined millisecond by millisecond.

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Trending

Since we can re-create the state at any point in time we can do trending and historical analysis of any and every attribute over any time period, at any time granularity we want.

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Compliance

When user interface events are logged as well as the attribute changes you have the ability to know not only who changed what information, but also who looked at it. Who was aware of the situation? Why did Bob open a particular record every few hours and cancel out without making changes? This requires the History Machine described above.

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Predictive

One of the main tasks in a predictive exercise is to work out which attributes are predictive of your target variable and which ones are not. This can be impossible to do when you only have 10% of your attributes logged. Maybe the minor attributes are the predictive ones. Now you have all of them. This requires the trending facility described above.

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Doug Moran, a co-founder of Pentaho and product manager for its Big Data products, sees many predictive applications for this kind of data. This includes the ability to derive a model from replays of previous events and use it to prescribe ways to influence the current situation to increase the likelihood of a desired outcome. For example, this could include replaying all previous shopping cart events for a user currently on an e-commerce site to derive a predictive model that prescribes a way to influence their current purchase in a positive way.

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“Dixon’s Union of the State idea gives the Data Lake idea a positive mission besides storing more data for less money,”

said Dan Woods, an IT Consultant to buyers and vendors and CEO of Evolved Media, who has written about the Data Lake for several years.

“Providing the equivalent of a rewind, pause, forward remote control on the state of your business makes it affordable to answer many questions that are currently too expensive to tackle. Remember, you don’t have to implement this vision for all data for it to provide a new platform to answer difficult questions with minimal effort.”

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Architecture

How could this be done?

• Let the application store it’s current state in a relational or No-SQL repository. Don’t affect the operation of the operational system.
• Log all events and state changes that occur within the application. This is the tricky part unless it is an in-house application. It would be best if these events and state changes were logged in real time, but this is sometimes not ideal. Maybe SalesForces or SugarCRM will offer this level of logging as a feature. Dump this data into a Data Lake using a suitable storage and processing technology such as Hadoop.
• Provide the ability to rewind the state of any and all attributes by parallel processing of the logs.
• Provide the facilities listed above using technologies appropriate of each use case (using the rewind capability).

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The plumbing and architecture for this is not simple and Dan Woods points out that there are databases like Datomic that provide capabilities for storing and querying state over time. But a solution based on a Data Lake has the same price, scalability, and architectural attributes as other big data systems.

5 Out Of 6 Developers Are Using Open Source

ZDNet  reports on a Forrester survey that finds 5 out of 6 developers are using or deploying open source.

http://www.zdnet.com/five-out-of-six-developers-now-using-or-deploying-open-source-7000008499/?s_cid=rSINGLE

In the survey they found that 7% of developers are using open source software tools such as Pentaho.

The United States Department of Labor state that, in 2010, there were 913,100 software developers in the USA alone.

http://www.bls.gov/ooh/Computer-and-Information-Technology/Software-developers.htm

7% of 913,100 means about 64,000 developers using open source business intelligence software. Nice.

I Never Owned Any Software To Begin With

My thoughts on the whole Emily White/stealing music topic:

http://www.npr.org/blogs/allsongs/2012/06/16/154863819/i-never-owned-any-music-to-begin-with

http://thetrichordist.wordpress.com/2012/06/18/letter-to-emily-white-at-npr-all-songs-considered/

When she says she only bought 15 albums, I think she is talking about physical CDs. I think she did buy some of her music online. But she clearly states that she ripped music from the radio station and swapped mix CDs with her friends, and she makes it sound like she thinks this is not stealing.

Don’t Blame iTunes

Many people who complain about artist’s income people blame Apple and iTunes. Yes, iTunes propagated the old economic splits and percentages into the digital world. But Apple did not create those splits, they were agreed upon in contracts between the labels/producers and the artists. What iTunes did was to provide an alternative digital distribution medium to Napster. Apple saved artists from the prospect of getting no revenue at all. People who attack and boycott iTunes thinking that they are helping artists are deluded.

It’s Not Just Music

This whole debate also extends to movies, books, news commentary, and software – anything that can be digitally copied. In each of these arenas, the players and economic distribution is different, but the consequence of not paying is the same. If we all behaved this way, ultimately, there would be no books, or movies either. So how does this relate to proprietary software, open source software, and free software?

Proprietary Software

Just like companies that publish books, music, movies etc, proprietary software companies were the gatekeepers. They decided what software was created and made available. When the hardware and software becomes available at the consumer level, independent producers spring up. This happened with freeware software for PCs. The internet enables the distribution of the software, and methods of collecting payment. The costs of creating books, music, and movies have dropped dramatically because of the hardware and software now available. But, if no-one pays for the content created the proprietary software companies will go out of business.

Non-Proprietary

Open source and free software are other ways for creating and distributing software, the difference being that these rely on software (source and binaries) being easy to copy. Don’t steal Microsoft’s BI software and use it without permission. Use our open source BI software – we want you to.

Free Software

Free software requires that the software, and all software that is built upon it, be ‘free’. In this case ‘free’ means you can freely modify it, distribute it, and build upon it, and you give others those same rights. You can still charge for the software, but it makes no sense to (given the rights you give to your ‘customer’).

The ideals of Free Software Foundation (FSF) are based on the notion that when you think of something or invent something, it belongs to the world, you don’t own it. This is a wonderful idea, however most of the world, including many industries,and jobs, and professions, are based on the opposite principle – if you create it, you own it. To my mind I have fewer rights under the FSF view of the world, I don’t have the right to my own ideas.

Because of the freedoms that the Free Software Foundation believe in, they are against Digital Rights Management (DRM) software. DRM tries to protect the rights of artists, producers, and distributors of artistic content. In order to protect these rights, software is needed that is proprietary. If the DRM source code was open, it would make it easy for hackers to decode the content and remove the copy protection. So the Free Software Foundation is taking up the fight against DRM, calling it ‘Digital Restrictions Management’ (http://www.fsf.org/campaigns/drm.html). They call it this because, they say, DRM takes away your right to steal other people’s inventions. If you support of DRM-free software, you are choosing to fight against musicians, authors, actors.

Open Source

The Open Source movement takes a pragmatic approach on this topic. When you have an idea, it is yours. You can choose to do whatever you want with your invention. If it is a software invention, and you choose to put it into open source, that’s great. If you choose not too, that’s fine too, because it is yours. Open Source allows hybrid models – where a producer can decide to put some of their software into open source but not all of it (open core or freemium model). This model enables a software producer to provide something of value to people who would not have paid for anything anyway (this includes geographies and economies where the producer would not sell anyway). These people are willing participants and contributors in other ways. The producer also gets to sell whatever software products it wants.

Doomsday?

For some creative areas, if no-one pays for any content anymore, the creators will disappear eventually, and there will be no more content. But what happens if no-one pays for software anymore?

Proprietary software dies eventually, unless they switch to services models.

The majority of people contributing to open source/free software today are IT developers. There are two main types here: creating/extending/fixing software in the course of getting their project finished, or sponsored contributors. IT is where the majority of software developers are today, so IT/enterprise/business software is safe.

The software that would be at most risk would be software that is created by smaller software companies. Particularly software that has large up-front development costs. Games. The first, and maybe only, software segment to die would be the big-budget, realistic, immersive, loud video games. Who cares most about these games? The same demographic that is stealing all the music.

I say let Generation OMG copy and steal everything they want. All the really cool and fun careers will evaporate. Lots of the stuff they love (movies, music, games) will disappear. After they have spent a decade texting each other about how sucky everything is, they will grow up and have to re-create these industries. Hopefully with better economic structures than the current ones.

Pentaho and DataStax

We announced a strategic partnership with DataStax today: http://www.pentaho.com/press-room/releases/datastax-and-pentaho-jointly-deliver-complete-analytics-solution-for-apache-cassandra/

DataStax provides products and services for the popular Apache No-SQL database Cassandra. We are releasing our first round of Cassandra integration in our next major release and you can download it today (see below).

Our Cassandra integration includes open source data integration steps to read from, and write to Cassandra. So you can integrate Cassandra into your data architecture using Pentaho Data Integration/Kettle and avoid creating a Big Silo – all with a nice drag/drop graphical UI. Since our tools are integrated, you can  create desktop and web-based reports directly on top of Cassandra. You can also use our tools to extract and aggregate data into a datamart for interactive exploration and analysis. We are demoing these capabilities at the Strata conference in Santa Clara this week.

Links

• Product downloads, how-to videos and documents are available at http://www.pentaho.com/cassandra and http://www.datastax.com/pentaho
• Attend the webinar on March 15th to learn more and about using Cassandra’s integration with Pentaho Kettle http://www.pentaho.com/datastax-webinar
• Download, access how-to documents and videos at http://community.pentaho.com/BigData

Pentaho’s Big Data Release

This week at Pentaho we announced a major Big Data release, including:

• Open sourcing of our of big data code
• Moving Pentaho Data Integration to the Apache license
• Support for Hbase, Cassandra, MongoDB, Hadapt
• And numerous functionality and performance improvements

What does this mean for the Big Data market, for Pentaho, and for everyone else?

We believe you should use the best tool for each job. For example you should use Hadoop or a NoSQL database where those technologies suit your purposes, and use a high performance columnar database for the use cases they are suited to. Your organization probably has applications that use traditional databases, and likely has a hosted application or two as well. Like it or not, if you have a single employee that has a spreadsheet on their laptop, you have a data architecture that includes flat files. So every data architecture is a hybrid environment to some extent. To solve the requirements of your business, your IT group probably has to move/merge/transform data between these data stores. You may have an application or two that has no external inputs or outputs, and no integration points with other applications. There is a word for these applications – silos. Silos are bad. Big data is no different. A big data store that is not integrated with your data architecture is a Big Silo. Big Silos are just as bad as regular silos, only bigger.

So when you add a big data technology to your organization, you don’t want it to be a silo. The big data capabilities of Pentaho Data Integration enable you to integrate your big data store into the rest of your data architecture. If you are using any of the big data technologies we support you can move data into, and out of these data stores using a graphical environment. Our data integration capabilities also extend to traditional databases, columnar databases, flat files, web services, hosted applications and more. So you can easily integrate your big data application into the rest  of your data architecture. This means your big data store is not a silo.

For Pentaho, the big data arena is a strategic one. These are new technologies and architectures so all the players in this space are starting from the same place. It is a great space for us because people using these technologies need tools and capabilities that are easy for us to deliver. Hadoop is especially cool because all of our tools and technologies are pure Java and are embeddable, so we can execute our engines within the data nodes and scale linearly as your data grows.

For everyone else our tools continue to provide great bang for the buck for ETL, reporting, OLAP, predictive analytics etc. Now we also lower the cost, time, and skills sets required to investigate big data solutions. For any one application you can divide the data architecture into two main segments: client data and server data. Client data includes things like flat files, mobile app data, cookie data etc. Server data includes transactional/traditional databases and big data stores. I don’t see the server-side as all or nothing. It could be all RDBMS, all big data store, 50/50, or any mix of the two. It’s like milk and coffee. You can have a glass of milk, a cup of coffee, or variations in between with different amounts of milk or coffee. So you can consider an application that only uses a traditional database today to be an application that currently utilizes 0% of its potential big data component. So every data architecture exists on this continuum, and we have great tools to help you if you want to step into the big data world.

If you want to find out more:

 

• Visit http://community.pentaho.com/BigData which has downloads, how-tos, and other resouces
• Connect with the community on irc.freenode.net ##pentaho;
• Join the Pentaho Big Data technical developer mailing list to be notified about future big data product updates and related events.
•  Attend the techcast on Thursday February 9th to learn more about Pentaho Kettle for Big Data, watch a live demo and hear how you can get involved. Register now at http://www.pentaho.com/resources/events/20120209-pentaho-kettle-webinar/
• Hands-on training FREE for attendees at the 2012 Strata Conference in Santa Clara, California. Sign-up for our how-to training session (http://strataconf.com/strata2012) on February 28th during the ‘Tuesday Tutorials.’ Register with Pentaho’s 20 percent discount code: str12sd20 <https://en.oreilly.com/strata2012/public/register> .

 

olap4j V1.0 has been released.

Back in the ’90s and early 2000’s I was involved in the attempts by the proprietary BI vendors to create common standards. Anyone remember JOLAP? The vendors were doing this only because of increasing demand and frustration from their customers – none of them actually these standards. Why? Because, in the short term, standards would only help the customers and the implementers, not the vendors. These efforts were hugely political with many of the vendors taking the opportunity to score points against each other. The resulting ‘standards’ were useless, and none of the large vendors were willing, or able, to support them.

How refreshing, then, to have olap4j reach the 1.0 milestone – http://www.olap4j.org. Created by consumers and producers of open source BI software, olap4j shows the advantage of open collaboration by motivated parties. Already olap4j has a Mondrian driver, and an XMLA driver for Microsoft SQL Server Analysis Services, SAP BW, and Jedox Palo. There are also several clients who use olap4j servers, some from Pentaho, and Saiku, Wabit, and ADANS.

olap4j is very cool stuff. You can read more on Julian Hyde’s blog. Congratulations for everyone that has worked on olap4j.

Big Data in New York City

I’m having an interesting time in NYC this week. I had to retrieve my snowboarding jacket out of the attic for this trip. It’s snowing right now, which is better than the sleet forecast for later. So far I’ve met with a few Big Data customers and prospects and presented at the New York Hadoop User Group. Our hybrid database/Hadoop data lake architecture always gets a good reception and our ability to run our data integration engine within the Hadoop data nodes impresses people.

Being the first Business Intelligence vendor to bring reporting and ETL to the Hadoop space sets us apart from all the other vendors. We have so much recognition in this space that I’ve spoken to a few people in the last month who thought we were ‘THE’ visualization and data transformation provider for Hadoop and didn’t connect to other data sources.

This afternoon I’m meeting with reporters and columnists from a couple of different publications to chat about Big Data / Hadoop stuff. Tonight I’m presenting at the New York Predictive Analytics Meetup to talk about Hadoop from an analysis perspective.

 

 

Meetups and Pentaho Summit(s) coming up in January

It’s going to be a busy month.

January 19th and 20th is our Global 2011 Summit in San Francisco. I have three sessions Pentaho for Hadoop, Extending Pentaho’s Capabilities, and an Architecture Overview. So I’m creating and digging up some new sample plug-ins and extensions. I’m also going to take part in an Q&A session with the Penaho architects since Julian Hyde (Mondrian), Matt Casters (PDI/Kettle), Thomas Morgner (Pentaho Reporting) will all be there. Who should attend?

CTOs, architects, product managers, business executives and partner-facing staff from System Integrators and Resellers, as well as Software Providers with a need to embed business intelligence or data integration software into your products.

We usually have customers and prospects attending our summits as well.

We are also having an architect’s summit that same week to work on our 2011 technology road-map. That should be a lot of fun.

The week after that I’ll be in New York presenting at the NYC Hadoop User Group on Tuesday, January 25 and the NYC Predictive Analytics Meetup on Wednesday January 26th.