Learnings from the Strata 2014 Conference

After three hectic days at the Strata conference trying to appreciate the poetry, I am on my way back on the Acela from New York to DC. There was tons to learn from the conference and words can only do so much justice but there is a set of learnings I want to share from my perspective. Caveat: These are all colored by my knowledge, my personal context, my organizational context but a lot of learnings are things that I am sure are going to resonate with a lot of people. Also another caveat that there is no neat structuring of what I am going to share, so treat it as such. So here goes:

1. Map Reduce as we know it is already behind us

MapReduce as a specific set of technologies written in Java (not as an overall philosophy, as indeed, MapReduce has become a philosophy very similar to Agile) is already behind us. Now we had MapReduce 2.0 come out late last year and it has been an improvement definitely on MapReduce 1.0. But when it comes to large-scale ingestion of data and making it usable, the mainstream has shifted to Apache Spark. What is surprising is that Spark as a technology is fairly new and not very stable. But the pace of technology evaluation is such that people are finding use for Spark in a number of really relevant and creative ways. And in 3 years, technologies like Spark will replace what MapReduce almost entirely. (Even though some people are going to argue there is a place for both)

2. Using BigData tools vs investing in custom development on agile technologies is an important decision

With the emergence of the open source software movement and also the ability to easily share software, learning, approaches using a number of internet based platforms , it is no surprise that a lot of startups see open source as an easy way to bootstrap their product development. Over the years, open source software is becoming the norm for driving product development and data infrastructure creation within almost all tech and digital industry leaders.

With the Cambrian explosion of product development in the data space, a lot of the products being released are tools or building blocks that then allow efficiencies around data processing and data pipeline. So an organization that needs to harness and use BigData for its day to day needs has this very important decision in front of them. Should they be doing custom development on the generic open source technologies and therefore allow their solutions to evolve along with the underlying generic technology, or should they bring in third party tools for important parts of their data processing? (This is a variant of the classic Build vs Buy question, but has some nuances because of the open source explosion.)

Each decision comes with its pros and cons. Working with tools improves speed to market, but then forces the buying organization to use a set of constraints that a tool is likely to impose on them. Working on generic technologies removes this dependency and allows for natural product evolution, but this then comes at the cost of development time and lower speed to market, potentially higher costs. And these are not easy decisions. My specific observation here was around how my organization has chosen to ingest data into its HDFS environment. Should we be doing custom development using some of the open source data ingestion frameworks such as Apache Flume or Storm, or should we use a product that comes with a number of desirable features out-of-the-box like Informatica? These are not easy decisions and I think the whole Build vs Buy decision on BigData needs its own blogpost.

3. Open source is here to stay

I think I might have said this before but open source is here to stay and going through a Cambrian explosion. Enough said on that!

4. Innovation to new and dynamic technologies needs to be multi-threaded

As relative late adopters on to the BigData platform, my organization has been following a linear and established path to BigData adoption. The goal here has been being able to get to low-hanging fruit with BigData here around cost savings – by taking spend away from investing in RDBMS platforms. It is a perfectly legitimate goal to have and I think we are going about this goal in a very structured manner. But in a world of fast evolving technologies, this focus creates the risk that we end up having a blind spot within the overall ecosystem around other use-cases of the technology. In our case, real-time data use-cases and streaming analytics is a big blind-spot from my vantage point. The risk here is that by the time we achieve the low-hanging fruit by being systematic and focused, we end up losing a lot of ground in other areas and are similarly behind when the next technology wave happens.

So my view here is that we need to be multi-threaded in our technology adoption. We need to have specific goals and be focused on them to make these new technologies mainstream – but at the same time, we need to be aware of other applications of the technology and make sure there are investments in place to build our capabilities on these areas which are not immediate focus. Also, to have a SWAT team working on even newer technologies and ideas that are likely to become mainstream 12 months from now.

Just a smattering of my immediate thoughts from Strata. Like I promised, not very organized but did want to share some of my unvarnished opinions.

Third time at Strata and why that's like reading poetry

I am heading up to New York to the 2014 Strata conference. It has become a ritual of sorts to go to Strata in October as I have been going for the last three years.

My first trip was in 2012 where admittedly, I was going as a BigData skeptic. Or put more accurately, I was going in with an open mind about the possibilities of BigData but definitely under-exposed in terms of its capabilities and what organizations and professionals could stand to gain. I walked away with some appreciation of the BigData case studies but many of the examples seemed like applying technology for technology sake.

When I went in 2013, my perspective was certainly more informed. I was going to understand the big data ecosystem in more detail, having spent a considerable amount of time both reading up on the technologies as well as working closely with practitioners in the area. I left the conference with a much wider understanding of the entire HDFS and BigData ecosystem. Props here for Silicon Valley Data Sciences whose presentation was both detailed in both its breadth and depth, a difficult thing to accomplish.

Also, I broadened my own perspective in terms of the kinds of problems that can be solved by BigData. Previously, my field of vision was narrowly focused on business problems. The problem with that lens is that business problems are a reflection of the past - how the business operated yesterday and the challenges that were created in a pure economic sense. What this approach is blind to is the huge world of consumer and human problems that need exploring and solving using BigData. Opening my mind to a whole host of consumer/ human challenges made me aware of the need to harness and harmonize different types of data, the mash-ups and insights created and the different world one could vision for customers. I also had the opportunity within my org to work closely on a classic BigData problem - which was building a holistic view of the customer across functional and product silos. And so working with and talking to people with vastly greater experience and hands-on knowledge made me more informed, allowed me to appreciate even finer nuances in the space and form even more bold customer and business value propositions.

So as I am headed up to New York to attend Strata for the third year, my mind goes to poetry. I have always felt that you need to pass the hump of understanding language to understand the intricacies and beauty within the language. Understand the rhythm and the poetry of the language. And that requires deep study. Countless hours of reading, multiple hundreds of tweets. And hard hard hours of whiteboard sketching, debating ideas, learning from the experts. 

And so here's listening to some good poetry over the next three days at Strata. Should be fun.

Real-time analytics infrastructures

As the BigData (Hadoop) explosion has taken hold, architectures that provide analytic access to even larger data for even deeper insights have started becoming the norm in many top data science driven organizations. One of them is the approach used in LinkedIn - the LinkedIn analytic blog is typically rich with ideas on how to approach analytics.

What are some of the challenges that some of these architectures are trying to tackle?

- The need to organize data together from different data sources into one single entity. In most cases, this is typically an individual. In the case of LinkedIn, it is the professional member. For Facebook, one of its 1.2 billion subscribers. For a bank, one of its customers. While the actual integration of the data might sound trivial, the effort involved is highly manual. Especially in legacy organizations like banks that have different source systems (managed by different vendors) performing their billing, transactions processing, bill-pay, etc. the complexity of the data that comes into the bank in the form of raw files can be truly mind-boggling. Think multiple different data transfer formats (I recently came across the good old EBCDIC format), files with specific keys and identities relevant to that system, and so on. These files need to be converted into a common format that is readable by all internal applications, also organized around one internal key.

- Next, the need for the data to be value-enhanced in a consistent manner. Raw data is seldom useful to all users without any form of value-addition. This value addition could be something simple i.e. taking the relationship opening date and converting this into a length of relationship indicator. So, say the relationship opening date is 1/1/2003, the length of relationship is 11 years. Or it could be a more complex synthetic attribute that uses multiple raw data elements and combines them together. An example is credit card utilization, which is the balance divided by available credit limit. The problem with this kind of value enhancement is that different people could choose to do this in different ways. So creating multiple such synthetic attributes in the data ecosystem - which can be confusing to the user. Creating a data architecture which allows these kinds of synthetic attributes to be defined once and then used multiple times can be a useful solution to the problem I just described.

- The need to respond to queries to the data environment within an acceptable time interval. Also known as the service level or SLA that an application demands, any data product needs to meet business or user needs in terms of the number of concurrent users, query latency times. The raw HDFS infrastructure was always designed for batch and not for any real-time access patterns. Enabling these patterns requires the data to be pre-organized and processed through some kind of batch approach - so as to make it consumption ready. That when combined with the need to maintain close to real-time data relevance, means that the architecture needs to use elements beyond just the basic Hadoop infrastructure.

These are just some of the reasons why BigData applications and implementations need to be pay special attention to the architecture and the choice of the different component systems. 

Back to the blog and de-hyping Big Data

Getting back to writing this blog after really long. What happened in the middle? Well, I got lazy and I got somewhat busy.

But obviously the world has not stood over this period. The topic of my previous set of posts, Tesco, came and now has declared its intention to leave the US. Big Data has become, well, really big though my skepticism still is quite intact. And then there was the small matter of a presidential election where analytics and data modeling really came into its own. 

So there’s plenty to catch up on, for the inactivity of my past several months. But hey, New Year resolutions are there for a reason and so it is my commitment to be a lot more regular and disciplined about my posts.

My first post is on big data, as this is clearly going to be an important part of analytics and related infrastructure for the next several years. As you readers probably know, I started out from a place of a little bit of skepticism. My understanding has evolved a little bit over the last few months and I think I am in a much better place to articulate, primarily for myself why big data does make sense – mostly in a business sense and less in a purely tech-geeky sense. So I will try and do that over the next few posts. 

But let me first start off with a reference to a post by Bill Franks, Big Data evangelist and Chief Analytics Officer for Teradata Alliances. Bill has spoken about big data extensively and most recently, has mused whether big data is all hype.

His take is interesting, in that he does not think the big data story is built on an empty premise. There are genuine underlying business problems that need solving and genuine underlying technologies that provide a set of viable options to solve the problems. But he does believe that there are a multitude of technology options coming out – almost on a daily basis and that a shakeout amongst the players is imminent. Also, organizations will realize that just installing a Hadoop cluster is not the Big Data destination. The destination is a analytics and data infrastructure solution that is fast, cheap and scalable which does exist today, but which is “potential that can only be extracted with a concerted, focused, and intelligent effort”. 

My own quest has been to define for myself why does big data make sense from a business standpoint. Especially for a big Fortune 500 company, with the underlying assumption that there are different sets of economic motivators for big organizations vs. start-ups. I have been trying to educate myself through building up a detailed understanding of the underlying technologies, speaking to industry experts and practitioners and attending industry seminars. I will share my findings over the next few posts.