A trend is emerging in which businesses are deprecating ETL (Extract, Transform and Load) - based integrations and replacing them with APIs.
What are the drivers? Why are leading enterprises making this shift for their data?
There’s a fundamental shift in the qualitative nature of today’s data and an explosion of new sources.
Traditionally data was controlled within the enterprise – all of the data that an enterprise gathered were collected when partners and customers interacted with a small number of internal systems. However, in the new apps-based economy, in addition to the systems of record, there are new...
Apigee makes use of the latest and greatest big data technologies such as Cassandra to power its products. Apigee App Services, which power mobile and rich client applications from the cloud, are based on Usergrid (Apigee's open source data platform built on Cassandra).
In last week's Webcast, Building a Mobile Data Platform with Cassandra - Apigee Under the Hood, @edanuff and @landlessness discussed how Apigee implemented multi-tenancy at scale in Usergrid.
"Cachiness factor" is the degree to which your API design supports the caching of responses. Low cachiness means that a relatively higher than optimal number of requests is forwarded to the back end for retrieving data; a high cachiness factor means that the number of requests serviced through the cache layer is reduced and optimized.
Every time a request is sent to the API provider endpoint, the provider incurs the cost of servicing the request. Investing in a good caching mechanism...
The amount of data collected by companies in all sectors and markets has grown exponentially in the past decade. Businesses increasingly interact with customers through social and business networks, through apps (and APIs) and therefore businesses collecting data from new and diverse locations outside the walls of their enterprises and systems-of-record.
The following is a perspective on 5 ways in which data is changing how we do business...
In previous posts on this topic of Big Broad Data, we looked at some of the reasons for and implications of enterprises shifting their focus from the “bigness” and technology hype of “Big Data” to breadth and diversity, signal extraction, analytics and deep insights.
The future is around the easy consumption, the flow and interaction of data, which drives a revolution in the world of Data APIs. The structure of the Data APIs becomes increasingly important.
In my previous post about Making the shift from Big to Broad Data, I made the case for thinking about Big Data not so much as “Big” but as “Broad.” We looked at the explosion of new data sources in today’s economy, which are individually typically smaller and more diverse than the enterprise systems of record of the past. Data comes from a variety of sources like Twitter, Facebook, partners, tens and hundreds of apps (some built around your APIs), and more.
To be responsive and make business decisions, an enterprise simply has to be responsive to data...
In my previous post, I laid out why I think we need to move beyond the hype of Big Data technology and “bigness” to focus instead on the breadth and diversity of data, as well as signal extraction, analytics and deep insights from that broad data.
Here we’ll delve into what we mean by "Broad Data" as well as some of the fundamental changes for businesses in today’s marketplace that compel the need to focus on breadth of data and on data stitching from disparate sources.