I've seen a number of articles and webinars recently that discuss data integration as a cloud-based service. So I thought it was worth exploring what this really means in the context of big data – specifically when the objective is to exploit many sources of streaming data for analytics. My initial reaction
Learn the top 5 reasons for managing data where it lives – whether it's in database or in memory, in the cloud or in-stream.
I feel like I'm singing a song called Data in the Sky – With Options! The cloud is forever in our minds these days as a lower cost option because it requires fewer resources to address our data needs. Cloud solutions are an increasing part of many organizations' budgets every year. Whether enterprise data is
Many people perceive big data management technologies as a “cure-all” for their analytics needs. But I would be surprised if any organization that has invested in developing a conventional data warehouse – even on a small scale – would completely rip that data warehouse out and immediately replace it with an NoSQL
“Field of dreams warehouse”– a historic phrase I used in the early days of data warehouse development. It describes the frenzy of activity that took place to create enterprise data infrastructure, before the business rationale for the data use was even understood. Those were the early days. In some ways
With Big Data, there are far more technical questions than answers.
There are many ways to do data integration. Those include: Extract, transform and load (ETL) – which moves and transforms data (with some redundancy) from a source to a target. While ETL can be implemented (somewhat) in real time, it is usually executed at intervals (15 minutes, 30 minutes, 1
In my prior two posts, I explored some of the issues associated with data integration for big data and particularly, the conceptual data lake in which source data sets are accumulated and stored, awaiting access from interested data consumers. One of the distinctive features of this approach is the transition
A few of our clients are exploring the use of a data lake as both a landing pad and a repository for collection of enterprise data sets. However, after probing a little bit about what they expected to do with this data lake, I found that the simple use of
Yes. But since this post needs to be more than a one-word answer to its title, allow me to elaborate. Data governance (DG) enters into the discussion of all enterprise information initiatives. Whether or not DG should be the opening salvo of these discussions is akin to asking whether the
