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.