October 2nd, 2013

How APIs Grease the Data Wheels

Data MonetizationThis week, I’ve been attending and speaking at Data 2.0 in San Francisco, which is part of the API World Conference & Expo. Plainly, there is a connection between data and APIs.

As an API vendor, I would dearly like to believe the universe is embracing the API; giving it the proverbial uplifted thumb. And there’s no reason to think data doesn’t similarly “like” the API. APIs unlock value by making information available to both developers and applications – and there is plenty of value in data. Unlocking the value of data benefits everyone, especially the new data barons who own, aggregate or analyze the data. If data is the new oil, APIs are the pipelines and tankers (I guess making Hadoop the refiner).

But exposing data via APIs is not the full extent of the connection between data and APIs. The data landscape is getting reshaped by new found capabilities to store, mash, analyze and consume data. APIs provide the pathways for moving the data. But that leaves open the question of who regulates the pathways and the flow of data.

API delivery and management platforms like Layer 7′s represent one option for regulating the pathways and – if I may be so bold – perhaps the right way when data spans the Internet. If data sources, processors and destinations are distributed across the far-flung clouds, devices and apps that make-up the Internet, APIs provide the best way to interconnect the various data stores and actors. But then API delivery and management platforms are needed to govern that data flow.

API delivery and management platforms can simplify the ingestion of data from diverse stores spread out across the Internet. They can scrub, normalize and sanitize the data sets. They can simplify routing and federation across analysis and visualization tools. They can make data more consumable for developers, mobile apps, cloud services and even devices. And in the case of products like Layer 7, they can do this in a way that preserves privacy, integrity and general security.

Enterprises want to unlock value from their data oil. APIs provide the channels for getting the oil to the place where it can make the most difference. API delivery and management platforms ensure that the flow of data is both secure and managed – and always the right fit. As I described in my Data 2.0 talk earlier today, API delivery and management platforms can make the difference between being a data wildcatter and data baron.

August 13th, 2013

What ist DaaS?

DaaSWe live in the age of Big Data but Big Data is not showing up to the party alone. Fast data and open data are also coming along for the ride. This is why we need an “as-a-service” approach to data sharing. In a recent article for Big Data Republic, I explored the concept of data-as-a-service (DaaS) and some of the operational challenges associated with providing access to Big Data.

The fact that these challenges are not just theoretical considerations was driven home to me by one of our customers, who told me that he simply didn’t have enough IT cycles to keep writing and rewriting all those queries and APIs his customers were asking for.

Similarly another recent article on Big Data Republic, refered to three powerful drivers for machine learning identified by Tibco CTO Matt Quinn – drivers that I believe are equally relevant to data APIs:

  • “A surge of data being liberated from places where it was previously hidden (aka big data’s volume challenge)
  • A need for automation that manages the complexity of Big Data in an environment where humans have no time to intervene (aka Big Data’s velocity challenge)
  • An absolute requirement to create adaptable, less fragile systems that can manage the combination of structured and unstructured data without having a human write complex code and rules with each change (aka Big Data’s variety challenge)”

The efficiency gains and resulting agility and potential for innovation created by data-centric APIs are enormous – not just in respect to open data but also the ability to turn data into an active asset and monetize it. For an inspiring story, head over to Andorra via FastCompany.

Meanwhile, an interesting take on the way IoT is increasingly driving data democratization – and creating new governance challenges in the process – comes from  Christopher J. Rezendes and W. David Stephenson in an article at the HBR blog network. Naturally, the best place to implement and enforce data governance is in the API that provides access to the data.

Secure API design and management is not rocket science. Our API Academy is offering best practices and practical advice on everything from API design to API security to API lifecyle management (and yes, that includes versioning). And if you are curious about how Layer7′s API Management Suite can help your Big Data access challenge, download our Data Lens solution brief or contact me at hreinhardt@layer7.com.

April 4th, 2013

Focusing on the Byte-Sized Tree: The IoT Conundrum

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Data Lens for IoTYesterday, we introduced the concept of a Data Lens for aggregating and sharing data. Today, I want to talk about why this concept matters to organizations concerned with the Internet of Things (IoT).

Simply put: “things” generate petabytes of data. Putting sensors on everything, as both Cisco and GE propose, creates a data nightmare. Hadoop has made analyzing big volumes of data much easier but what happens when you want to share a small sliver of that information with a customer or partner? After all, the purpose of “Big Data” collection is not altruism – it’s about monetization. In many situations, this will only be possible if data can be shared easily.

A Data Lens gives IoT data owners – such as manufactures or telco carriers – an easy and secure way to share a focused and billable data set with their customers and partners. Anything outside of the scope of a Data Lens cannot be accessed, whereas anything inside the lens is  “in focus”. The data in focus can be raw or aggregated. There can be any number of Data Lenses on a data set. They can be used internally or shared securely with external partners and customers. Data access through individual Data Lenses can be governed by service level agreements and – through metering – monetized.

For manufacturers and network operators looking at ways to share focused data slices from their Big Data, a Data Lens solves a big problem. By leveraging the Layer 7 API Gateway’s unique ability to focus on small data sets inside larger ones and to present these data sets as secure APIs, customized to specific customers or partners, it’s possible for IoT operators to drive new revenue from their Big Data.

April 3rd, 2013

Getting Perspective on Your Big Data

Data LensAs we see it here at Layer 7, there are two big problems with Big Data:

1. There’s just so much of it that it’s easy to lose sight of the byte-sized trees in the petabyte-sized forest

2. It’s locked away in every recess of the enterprise – from applications to relational databases, to non-relational databases, to in-memory caches, public clouds, Hadoop clusters etc.

Data growth and diversity have made data access harder. But data access is the foundation of mobile app development, anything to do with the Internet of Things (IoT) and all kinds of Big Data analytics. Given this need for data in the face of access complexity, it didn’t come as a total surprise to see some of the most innovative Layer 7 customers start using our API Gateway technology as a novel data access, aggregation and presentation solution. As our resident IoT expert Holger Reinhardt pointed out to me: they are using our products to build highly-customized “lenses” across their distributed data backends. To me, this characterization is perfect because what these customers are looking for is perspective on their data. A lens gives perspective with focus.

Now, a Layer 7 API Gateway is more than just a data integration solution. Our technology has several unique features that make it ideal for collecting, composing and presenting data. First, we can talk to all kinds of data sources natively. That wasn’t easy to achieve and it’s something we developed over many years. Second, we can represent the source data as a RESTful API. Even better, we can dynamically generate a virtual API view for a specific user, app, partner etc. The API then becomes the entry point for accessing the aggregated data. Third, we can add fine-grained access and protection policies that ensure only authorized consumers get visibility to specific slices of data, while also protecting the data sources from attack and misuse. When combined, these capabilities give organizations a way to focus on just the information that is relevant to a particular mobile, IoT or Big Data analytics project and then share selectively with an app, cloud service, developer or partner.

A data lens is born!

If you want to learn more about our Data Lens solution, have a read of this new solution brief. Also, feel free to reach out to us with any questions.