|By Matt McLarty||
|February 27, 2014 05:00 PM EST||
The API Academy team has a new member: Irakli Nadareishvili who has joined CA Layer 7 as Director of API Strategy. Before joining CA, Irakli served as Director of Engineering for Digital Media at NPR, which is noted for its leadership in API-oriented platform design. He has also participated in the creation of the Public Media Platform, worked with whitehouse.gov and helped a number of major media companies develop publishing solutions using open source software.
I recently sat down with Irakli to discuss what he has in mind as he joins API Academy.
MM: You once told me that you believe the future of Big Data is “linked APIs”? That sounds intriguing. Tell me more about it.
IN: In most people’s minds, “Big Data” is synonymous to “very large data”. You may hear: “Google-large” or “Twitter-large” or “petabytes”. The Wikipedia definition of Big Data is slightly more elaborate:
“Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications”.
In my work, I see the “complex” part of the definition becoming more important than the size. We have gotten pretty good at taming the large sizes of data. Tooling for horizontal partitioning and parallel processing of large data sets is now abundant. Still, most Big Data sets are contained and processed in the isolation of single organizations. This is bound to change very soon. The end of siloed Big Data is near: I believe that the next phase of Big Data challenges will have to do with data sets that cross organizational boundaries.
APIs will play a major role in this. Web APIs represent the most effective available technology that allows data to cross organizational boundaries. APIs efficiently connect and link data at a distance.
MM: Can you give an example of what you mean by “data sets that cross organizational boundaries”? And what challenges do these pose?
IN: You see, a lot of people have the notion that the data they need to process can be stored in a database maintained by a single organization. This notion is increasingly inaccurate. More and more, organizations are having to deal with highly-distributed data sets.
This can be very challenging. The infamous healthcare.gov is a good example of such a distributed system. The main technical challenge of implementing healthcare.gov’s backend was that it had to integrate with data in many existing systems.
The $500 million initial public fiasco of healthcare.gov is also a vivid indication of just how complex it is to build truly distributed systems. Practically the only successful implementation of such a large, distributed information system is the World Wide Web. There’s a lot we can learn from the architecture of the Web. It’s a battle-tested blueprint for building distributed systems at scale.
I believe the Big Data challenges of the future will be solved at the intersection of APIs with Web/hypermedia architecture, linked data and what we currently call Big Data tooling. I call this intersection “Linked APIs”, to differentiate it from the current, siloed state of most Web APIs.
MM: What practical advice would you give to the developers of future Big Data APIs?
IN: I think the most important thing is that we need to stop falsely assuming all of the API data is local data. It is not. Despite the name, an API for a distributed system is really not a “programming interface” to local data/assets. Rather, it is a programmable data index. Think of APIs as a programmable search index for a distributed collection of data sets.
I don’t like to think of the term “API” as an abbreviation anymore. Maybe it was one a while ago but it has since evolved way past that. Much like IBM doesn’t think of itself as “International Business Machines” anymore, APIs aren’t merely “application programming interfaces”. Most of what IBM does these days isn’t even necessarily about “machines”. Likewise, most of what we need out of APIs isn’t about any single application or an interface to it.
MM: Big Data represents one important challenge for computing today. What about IoT?
NN: The Internet of Things is already here, in small ways. The IoT we have today consists of a vast number of Web-connected devices, acting as sensors, sending myriads of signals to the cloud. That, by the way, is what creates many Big Data challenges. The future is much more interesting, however. Once the connected devices start engaging in peer-to-peer interactions, bypassing central authority, we will enter a significantly different realm. The most important challenge in that world, from my perspective, will be identity. Identity is always key in distributed systems but especially so in peer-to-peer networks.
MM: What excites you the most about your new role at Layer 7?
IN: Thank you for asking this question. I will start by telling you what terrifies me the most. The API Academy and Layer 7 teams represent a gathering of ”scary” amounts of world-class brainpower and expertise in the API space. It is extremely humbling to be part of such a distinguished group.
Obviously, it also means that there is a lot of very fundamental thinking and innovation that happens here. Especially now that Layer 7 is part of CA Technologies, there’s really very little that we couldn’t accomplish if we put our hearts to it. That feels extremely empowering. I really care about all things related to APIs and distributed systems, the role they can play for the future of technology. I am super excited about the possibilities that lie ahead of us.
The essence of data analysis involves setting up data pipelines that consist of several operations that are chained together – starting from data collection, data quality checks, data integration, data analysis and data visualization (including the setting up of interaction paths in that visualization). In our opinion, the challenges stem from the technology diversity at each stage of the data pipeline as well as the lack of process around the analysis.
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