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Monday 29 April 2013

Analytics infrastructure of tomorrow

If you happen to be interested in the technologies that enable advanced business analytics, like I am, the last year has been an interesting one. A lot is happening, on all levels of the tech stack from raw infrastructure to cloud platforms and to functional applications.

As Hadoop has really caught on and is now a building block for even conservative corporations, several of its weaknesses are also beginning to be tackled. From my point of view, the most severe has been the terrible processing latencies of the batch- and filesystem-oriented MapReduce approach, rather than solutions designed on top of streaming data. That's now being addressed by several projects. Storm provides a framework for dealing with incoming data, Impala makes querying stored data more processing-efficient, and finally, Parquet is coming together to make the storage itself more space- and I/O efficient. With these in place, Hadoop will move from its original strength in unstructured data processing to a compelling solution for dealing with massive amounts of mostly-structured events.

Those technologies are a bear to integrate and, in their normal mode, require investment in hardware. If you'd prefer to get a more flexible start to building a solution, Amazon Web Services has introduced a lot of interesting stuff, too. Not only have the prices for compute and storage dropped, they now offer I/O capacities comparable to dedicated, FusionIO-equipped database servers, very cost efficient long-term raw data storage (Glacier), and a compelling data warehouse/analytics database in the shape of Redshift. The latter is a very interesting addition to Amazon's already-existing database-as-a-service offerings (SimpleDB, DynamoDB and RDS), and, as far as I've noticed, gives it a unique capability other cloud infrastructure providers are today unable to match - although Google's BigQuery comes close.

The next piece in the puzzle must be analytical applications delivered as a service. It's clear that the modern analytics pipeline is powered by event data - whether it's web clickstreams (Google Analytics, Omniture, KISSMetrics or otherwise), mobile applications (such as Flurry, MixPanel, Kontagent) or internal business data, it's significantly simpler to produce a stream of user, business and service events from the operational stack than it is to try to retrofit business metrics on top of an operational database. The 90's style OLTP-to-OLAP Extract-Transform-Load approach must die!

However, the services I mentioned above, while excellent in their own niches, can not produce a 360-degree view across the entire business. If they deliver dashboards, customer insight is impossible. Even if they're able to report on customers, they don't integrate to support systems. They leave holes in the offering that businesses have to plug with ad-hoc tools. While it's understandable, as they're built on technologies that force nasty compromises, those holes are still unacceptable for a demanding digital business of today. And as the world increasingly turns more digital, what's demanding today is going to be run-of-the-mill tomorrow.

Fortunately, the infrastructure is now available. I'm excited to see the solutions that will arrive to make use of the new capabilities.

Wednesday 12 December 2012

A marriage of NoSQL, reporting and analytics

Earlier today, I mentioned in a tweet that fired off a chat covering various database-related topics each worth a blog post of their own, some of which I've written about here before:

One response in particular stood out as something I want to cover in a bit more detail that will fit in a Tweet:

While it's fair to say I don't think MongoDB's query syntax is pretty in the best of circumstances, I do agree that at times, given the right kind of other tools your dev team is used to (such as, when you're developing in a JavaScript-heavy HTML5 + Node.js environment) and the application's context is one where objects are only semi-structured, it can be a very good fit as the online database solution. However, as I was alluding to in the original tweet and expounded on in its follow-ups, it's an absolute nightmare to try to use MongoDB as the source for any kind of reporting, and most applications need to provide reporting at some point. When you get there, you will have three choices:

  1. Drive yourselves crazy by trying to report from MongoDB, using Mongo's own query tools.
  2. Push off reporting to a 3rd party service (which can be a very, very good idea, but difficult to retrofit to contain all of your original data, too).
  3. Replicate the structured part of your database to another DBMS where you can do SQL or something very SQL-like, including reasonably accessible aggregations and joins.

The third option will unfortunately come with the cost of having to maintain two systems and making sure that all data and changes are replicated. If you do decide to go that route, please do yourself a favor and pick a system designed for reporting, instead of an OLTP system that can simply do reporting, when pushed to do so. Yes, that latter category includes both Postgres and MySQL - both quite capable as OLTP systems, but you already decided to do that using MongoDB, didn't you?

Most reporting tasks are much better managed using a columnar, analytics-oriented database engine optimized for aggregations. Many have spawned in the last half a decade or so: Vertica, Greenplum, Infobright, ParAccel, and so on. It used to be that choosing to use one might be either complicated or expensive (though I'm on record saying Infobright's open source version is quite usable), but since last week's Amazon conference and its announcements, there's a new player on the field: Amazon Redshift, apparently built on top of ParAccel, priced at $1000/TB/year. Though I've yet to have a chance to participate in its beta program and put it through its paces, I think it's pretty safe to say it's a tectonic shift on the reporting databases market as big or bigger as the original Elastic Compute Cloud was to hosting solutions. Frankly, you'd be crazy not to use it.

Now, reporting is reporting, and many analytical questions businesses need to solve today really can't be expressed with any sort of database query language. My own start-up, is working on a few of those problems, providing cloud-based predictive tools to decide how to serve customers before there's hard data what kind of customers they are. We back this with a wide array of in-memory and on-disk tools which I hope to describe in more detail at a later stage. From a practical "what should you do" point of view though -- unless you're also working on an analytics solution, leave those questions to someone who's focused on that, turn to SaaS services and spend your own time on your business instead.

Saturday 3 October 2009

Some scaling observations on Infobright

A couple of days ago, Baron Schwartz posted some simple load and select benchmarking of MyISAM, Infobright and MonetDB, which Vadim Tkachenko followed up with a more realistic dataset and interesting figures where MonetDB beat Infobright in most queries.

Used to the parallel IEE loader, I was surprised by the apparent slow loading speed of Baron's benchmark and decided to try and replicate it. I installed Infobright 3.2 on my laptop (see, this is very unscientific) and wrote a simple perl script to generate and load an arbitrarily large data set resembling Baron's description. I'm not going to post my exact numbers, because this installation is severely resource-constrained below Infobright's recommended smallest installation. However, you can reproduce the results yourself with the attached script, and I will note some observations.

Continue reading...

Monday 21 September 2009

A peek under the hood in Infobright 3.2 storage engine

I've been meaning to post some real-world data on the performance of the Infobright 3.2 release which happened a few weeks ago after an extended release candidate period. We're just preparing our upgrades now, so I don't have any performance notes over significant data sets or complicated queries to post quite yet.

To make up for that, I decided to address a particular annoyance of mine in the community edition, first because it hadn't been addressed in the 3.2 release (and really, I'm hoping doing this would include it into 3.2.1), and second, simply because the engine being open source means I can. I feel being OSS is one of Infobright's biggest strengths, in addition to being a pretty amazing piece of performance for such a simple, undemanding package in general, and not making use of that would be shame. Read on for details.

Continue reading...

Wednesday 27 May 2009

What we're looking for in a data integration tool

As our data warehousing process grows and the workflows get more complex, we've revisited the question of what tools to use in this process. Out of curiosity, I had a look at basing such a process on Hadoop/Hive for scalability reasons, but the lack of mature tools and the sacrifices on efficiency that would entail meant we're better off using something else as long as a distributed processing platform is the only thing that can get the job done. I'm also curious about the transition to continuous integration, a model I noticed showing up a couple of years ago and now getting some air under its wings as CEP, IBM's Infosphere Streams, and other similar approaches. Still, I think I'll continue to rely on something else for a while and see how things shake out. Continuous integration clearly is the future, but there are many ways to get there.

So, we had a look at what's going on in the Open Source data integration field. It seems the leaders in that field are Pentaho with Kettle/Pentaho Data Integration, and Talend with Open Studio and Talend Integration Suite. Both seem pretty even in terms of features. Both companies are a bit difficult to approach as a potential customer, so I figured I should also try what would come up from the OSS approach of just posting my thoughts on the Interweb ;)

Besides the technical pilot implementations we've made to compare basic workflow of the various tools, below is a sample of the kind of questions we're considering when evaluating the suitability of the tools.

Product roadmap, release schedule and size of the development team

  • How often and of what scope of changes should we expect and prepare ourselves for platform upgrades?
  • Past track record on keeping to a regular updates schedule

Data lineage and dependency, Impact analysis

  • How to find out which tables are being used to for deriving DWH dimensions and facts?

Logging, auditing, monitoring on row and job level

  • How to monitor and archive workflows on a row level (amount of rows being inserted/updated/deleted)?
  • How to maintain, access and query a job execution history (start time/end time/return code)?

Version control

  • How to track and restore changes in jobs?

Multi-user environment

  • How can several developers work together?

Change Data Capture

  • How to assist incremental loads?

Data profiling

  • How can data source be examined?

Job recovery

  • How to recover from possible failures in jobs (such as lost database connection)?

Deploy jobs

  • How to move jobs from one repository to another (development to testing to production)?

Thursday 23 April 2009

Three domains of data

My MySQL Conference presentation on Tuesday discussed my practical findings on how Infobright's technology works in developing a MySQL-based data warehouse. I also touched on a more high-level question of how to select a technology for a different kinds of data-related problem areas, and this article expands on that discussion.

Continue reading...

Wednesday 19 November 2008

Looking for a ETL engineer for our BI team

So, I mentioned earlier that I was looking at Infobright's Brighthouse technology as a storage backend for heaps and heaps of traffic and user data from Habbo. Turns out it works fine (now that it's in V3 and supports more of the SQL semantics), and we took it into use. Been pretty happy with that, and I expect to talk more about the challenge and our solution at the next MySQL Conference in April 2009.

However, our DWH team needs extra help. If you're interested in solving business analytics problems by processing lots of data and the idea of working in a company that leads the virtual worlds industry excites you, let us know by sending us an application. Thanks for reading!