Category Archives: olap

Why Larry hates the cloud, and my data trinity.

Last week Oracle certified Amazon EC2 as a supported platform, that same week Larry Elison attacked the concept of cloud computing as pure hype. Obviously, Larry is not happy with this whole cloud thing, and I think it’s not just the threat it poses to the software industry’s traditional licensing model that worries him, rather, as Robert X. Cringely points out in his “Cloud computing will change the way we look at databases” post, it’s the likelihood that it sounds the death-knell for large-scale traditional databases.

This new database paradigm is memory rather than disk centric, with the disk-based element acting as an archive/backup/restore mechanism which can easily be stored on commodity SAN devices ( e.g. Amazon’s ESB). Using MapReduce technology Google effectively holds the whole Internet in memory, not in one big super computer but in lots of cheap commodity servers.

But it’s not just in the realm of mega datasets that RAM based databases threaten traditional models. Excel is a memory-based database engine, so too in-memory OLAP tools such as Palo. Such products’ ability to handle large volumes of data has increased over the years, with the decrease in RAM costs and the appearance of cheap 64 bit machines (which are no longer limited to 2G/3G process working sets).

That doesn’t mean that we’ll throw away SQL databases in their entirety, SQL and the relational model will continue to be useful. But perhaps of greater use in local datastores/caches that as the building blocks for large scale datastores. For such local caches, less will be more; fewer features, easier to configure, more flexibility. That’s why I like SQLite; long after the dinosaurs of the database world have disappeared, I imagine SQLite databases will continue to survive, embedded in mobile phones, browsers, wherever a local datastore is required. And more than likely operating in memory rather than off disk.

By combining Excel with an in-memory SQLite database, linked to a Palo OLAP in-memory server, it’s possible to take advantage of three powerful data-processing technologies (spreadsheets, SQL, multi-dimensional cubes) all within your PC’s RAM. You could do serious datasmithing with such a combination on a pretty mediocre laptop, with most modern machines providing an excess of CPU power, no need for super fast disks, just as much memory as you can muster. And, with Windows on EC2, these three amigos will soon be capable of being used as a cloud bursting platform.

Excel, SQLite and Palo, my data trinity.

Cloudy skies, cloudy apps…

Just back from a break in Clifden, Connemara, summer is nearly over, the kids return to school today, back to work.

Aasleagh Falls, Co. Mayo

Aasleagh Falls, Co. Mayo

Counties Galway and Mayo were like the rest of the country last week, a tad wet, but unlike the developed east of the island, flooding was not a problem; a problematic drainage area is called a lake in the west.

This August has been the wettest and dullest I’ve ever experienced but at least I saw some sunshine earlier in the month thanks to Kristian Raue CEO of Jedox who kindly invited me to visit the company’s offices in Freiburg, Germany.  Freiburg is very green in both senses of the word, surrounded as it is by the Black Forest and its well deserved “eco-city” status.  Its also know as the warmest city in Germany, a reputation it thankfully lived up for this visitor from a rain-soaked Atlantic isle.

August morning, Frieburg Im Breisgau

August morning, Freiburg im Breisgau

If Freiburg left a positive impression on my mind, so too did Jedox.  The overall impression is of a company which intends to use a combination of quality, vision and the judicious use of open-source to build the Jedox brand into one associated with best-of-breed products and consultancy.  This vision can be seen in the evolution of Palo, from its “good enough” beginnings to its current near-best-of-breed 2.5 version, and from talking to some of those working on the product, best-of-breed status is not that far off.

Likewise, ETL-Server which is currently a Palo only “loader”, is to be further  developed into a true ETL tool, while continuing to offer MOLAP-centric specialisms.

I also got a glimpse of the next version of Worksheet Server. “Wow!”, is all I can say.

Existing web based spreadsheet products are fine for simple data analysis or basic data capture purposes but cannot compete with their client-based elder cousins when serious datasmithing is required.  Well, from the demo I saw of Worksheet Server in action, that’s about to change.  The look and, more importantly, the feel is similar to that of traditional spreadsheets, its interface with Palo is identical to that of the existing Excel add-in, and here’s the big one, its open source!  Game-changing or what?

But …

That might enable me to move a lot of my spreadsheet applications to the cloud, but what about those applications that are more suited to an MS Access type solution?

Then try out WaveMaker. It’s open source and built on industry standards, Hibernate,Spring and the Javascript Dojo framework but has the ease of GUI database development more usually associated with MS tools. The resulting applications are packaged as a WAR file which can be hosted by any standards based Java server (e.g. Tomcat or Jetty).  The latest version makes developing Ajax-fronted database applications even easier with the addition of layout templates.  Its existing ability to automatically bind interfaces to SOAP web services has been extended to REST web services by means of a new WSDL auto-discover tool.  And Chris Keene CEO of WaveMaker also informs me that …

We are also releasing a cloud-based IDE in October with Amazon – stay tuned…

We launched in February and will be announcing our first 7 figure deal this month. We run on Mac, Linux and Windows and are currently the #1 developer download on Apple.com (http://www.apple.com/downloads/macosx/development_tools/)

Our goal is to make it easy to build rich internet applications without complex coding – kind of a MS Access for the Web.

Jedox and Wavemaker the new breed of open-source businesses

Talend + SQLite + Groovy the new Oracle …

… well, at least for me.  Let me explain.

For most of my datasmithing career, I’ve had access to corporate Oracle databases and now with the availability of  Oracle10g  Express I can even run my own Oracle instances at home or on EC2.  The combination of a powerful SQL engine, expressive scripting language (PL/SQL) ,OS independence, web front-end (App Express) and the ability to communicate with Excel (via OO4O) made Oracle a natural fit for heavy-duty data manipulation.   But there was always one major problem, Oracle doesn’t play well with other data sources, necessitating a separate ETL bolt-on, which led me to play around with the likes of Kettle and Talend.  But having been seduced by these new shiny (and open source) “toys” I’ve found that rather than just been incidental add-ons they had the potential to totally replace Oracle.  The combination of Talend, SQLite and Groovy, is proving to be particularly magic.

So how will these three tools enable you to leave behind your Oracle past?

Talend (in its Java form) is a superb ETL tool, via JDBC is can access every database type on the planet, it has built-in web-service capability and access to a  multitude of APIs via its Java component for non-database data sources.  The addition of  Groovy makes the use of such Java APIs simpler and quicker and the same Groovy acts as a replacement for PL/SQL when a bit of “if-then-else” logic is required.  And although Talend offers a built-in option to plublish an ETL job as a WAR file exposing a SOAP web service, Java/Groovy also allows for the integration of the powerful, yet simple, Jetty API to embed a web server within Talend itself.  And all this for free, and better than free, open source.

So where does SQLite come in? And, didn’t you say that Excel integration was important, how will Excel communicate with Talend?

As very little corporate data is held in SQLite format, and Talend allows access to every major commercial/free database, the usefulness of SQLite might not be at first obvious.  But if you think of SQLite as a data cache, a fast and efficient local tabular datastore, with a powerful but well understood DSL (i.e. SQL) and a drop-dead-simple setup and backup regime (basically copying and creating files), maybe then you can see its attraction. The ability to extend the DSL by easily creating SQLite user defined functions (UDFs) within Talend using either Java or Groovy is also another powerful feature.

For example…

select customer_id, name,customer, sales_region, getpalodata(“SALES”,customer_id,”All Products”.”Total Sales”,”Euros”,”YTD”)  as customer_YTD, getpalodata(“SALES”,sales_region,”All Products”.”Total Sales”,”Euros”,”YTD”)  as region_total_YTD from list_of_top_customers;

… where getapalodata is a UDF that wraps calls to a Palo cube.

With this type of setup I can easily mix and match list/tabular data with multidimensional data points using SQL (something that Oracle also supports but only if you hand over a large wad of currency). In fact I can create a mini data warehouse, with Palo providing the pivot, ( as SQLite lacks star-query (or even multi-index query) support.)  SQLite would still host the conformed dimensions and the fact tables, but with the fact tables acting as feeds to Palo cubes, supporting finer-grained drill-throughs from cubes or for ad-hoc queries. This is powerful stuff, simple, free, powerful stuff.

… and the spreadsheet access?

A Talend sub-job such as this…

Talend Groovy Jetty web server

Talend Groovy Jetty web server

Example of Groovy code calling Jetty API

Example of Groovy code calling Jetty API

…would provide a simple RESTful (rather than SOAP) web service which could be accessed either with an Excel Web Query or via a VBA macro which would parse the result and allow for more control.  For example …

http://localhost:1234/sqlgateway?sql=select customer_id,name from all_customers&type=HTMLTable

… this would return a list of customers wrapped in an HTML table, or …

http://localhost:1234/job/extractProspects?Rep=JonesTom&Month=JAN&SourceCompany=AXA&type=HTMLTable

…this might call a Talend job called extractProspects, passing in JonesTom, JAN and AXA as context parameters, which would then return a list of prospects extracted from a feed supplied by AXA’s system.

What would the Talend job look like?

The job might operate something like this:

  • It would run either on the client as a service or on a LAN based server (or on a remote server, with a SSH VPN (or Hamachi) to provide security).
  • At start-up, do a bunch of ETL tasks, pulling data from remote sources and databases, transforming and aggregating data etc. Storing the resulting data in local SQLite databases.  It might also build Palo cubes or update larger enterprise databases.
  • The job would then setup a Jetty web server and await requests for data.
  • The requests might be a mixture of raw SQL or requests to run specific Talend transformations which would return a dataset directly to the calling client or maybe just acknowledge the request, queue it up for processing later, sending the resulting dataset by EMail or RSS feed when finished.
  • At a fixed time the service would shut it self down and requeue itself for the next day’s workload.

… or nothing at all like that, and that’s the point, build what you need, add the levels of security (or none at all) that fits your situation, all within a open framework, with zero lock-in (okay, still using Excel, anyone for OpenOffice, Google Apps or Zoho?).  You don’t even need your own server, host it on an EC2 instance, (if you bring up an instance for 10/12 hours every working day, it would cost about $20/$25 a month).

Now tell me that doesn’t make sense?

OLAP Cube as a Mind Map

If you’ve worked with OLAP technologies for any length of time you’ll undoubtedly have been in the situation where you’ve had to explain the concept of an OLAP Cube to a “newbie”.  If the person in question has come across Excel pivot-tables, then you can probably short-circuit the conversation some what, explaining that a pivot table is in essence an OLAP cube, maybe highlighting the differences between it and whatever OLAP tool you’re proposing; ragged hierarchies, ability to update cells and ‘spread’ values down hierarchies etc.  Even better if you can show the user an working cube populated with hierarchies and elements that match the user’s business.

But what if the person is a complete novice to the world of analytics and you don’t have a relevant demo cube that you can demonstrate, what then?  I guess, you could start by first trying to fry the victim’s user’s brain by explaining the concepts behind multi-dimensional spaces and/or a quick intro into the world of de-normalised databases, with examples of snow and star schemas. The glazed look in the eyes of your audience may however suggest this doesn’t always work.  And it’s not just business-users who have problems getting their heads around cubes, many programmers also have difficulty the first time they’re exposed to OLAP concepts.

I’ve found that Mind Maps offer a good way to help both me and the client to visualise the domain model that a cube will eventually address; having worked through the mind mapping process it’s then easier to take the user with you as you translate this model to a physical cube or star-schema.  Well Hugo (who’s back blogging after a long absence) promises to take this method a step further, his modifications to FreeMind will allow for the export of a mindmap as a Palo cube!  Check it out …

Postgres Plus Cloud Edition is boring …

… and that’s good. That’s how I like my databases, boring, reliable, consistent, easy to use.

SimpleDB on the other hand is not boring, it’s an exciting new shiny thing that opens up a myriad of new possibilities; but first, I and the rest of the developer community, need to tool up and cast aside some of our cherished database design patterns (oh like, 3rd normal form, strong typing, joins, nothing major) and embrace a slightly different way of thinking, however, as much as I like a challenge, I also like to get things done.

That’s where EnterpriseDB’s new Postgres Plus Cloud Edition comes in, this is an Amazon Ec2/S3 hosted edition of their Oracle compatible PostgreSQL-based product that offers the scalability of SimpleDB but the familiarity of a traditional relational database. The “magic” is supplied by Elastra, who are also offering the same functionality against MySQL and standard PostgreSQL databases.

A Talend ETL job which I had been developing for a client, had been tested against a “normal” EnterpriseDB instance. This ETL job was part of a BI prototype trialling a Postgres Plus Cloud Edition (the new name for EnterpriseDB’s cloud offering) as the back-end database. So, I exported the job as a Java executable, fired up an EC2 instance, copied up the generated JAR files, changed the database’s hostname to that of the Postgres Plus “cloud” database, ran the ETL job and it worked. As I said, boring, nothing to report, it just worked.

Now you may be wondering what’s so special about these Elastra powered databases, surely EC2 is no different from any other Linux virtual machine, why not simply install a standard database? The problem with EC2, and it is a problem to those of us (i.e. practically every IT pro on the planet) who have come to expect highly reliable RAID backed disk storage, is the non-permanence of its disk systems.

When an EC2 instance is powered down or fails, the disk system is wiped!

That, combined with fixed (if generous) disk sizes (160GB, 850GB or 1690GB), means that often a clustered database environment is a necessity, adding considerably to the complexity. It’s this sort of complexity that SimpleDB and Elastra address.

The obvious use-case for both Elastra and SimpleDB is as data stores for OLTP applications but Elastra’s ability to handle S3-backed massive databases means the possibility of using EC2 as a data warehousing platform is also considerably strengthened. Although not obvious at first glance, SimpleDB could also act as an OLAP data store; SimpleDB massively indexed tuples as “sparse dimensions” pointing to S3 objects (SQLite databases?) that hold the fact data combined with dense/”partioning” dimensions (e.g. Time). Possible ? Yes. Fun to do? Yes. A solution that I can apply tomorrow? No, that’s why I’m glad EnterpriseDB and Elastra are delivery such a boring product!

UPDATE Ec2:

The other big EC2 missing – non-permanent IP addresses – has at last been addressed. EC2 now offers “Elastic IP Addresses”, addresses associated with an account not an instance. If the instance fails or is shut down, the IP address can either be immediately re-assigned to a new instance (no more waiting for Dynamic DNS propagation) or “reserved” for future use at a cost of USD0.01c per hour. Also, the new “multiple locations” facility puts the API changes in place to allow for location selection, hopefully a sign that we here in Europe will have “local” EC2 instances to match our European S3 buckets!

UPDATE EnterpriseDB:

It looks like IBM have invested in EnterpriseDB, possibly as a counter-weight against Sun’s acquisition of MySQL (EnterpriseDB’s targeting of Oracle’s customer base would also be an added benefit!).