Joyce Norris-Montanari poses the question: Is Hadoop/big data technology actually ready for MDM?
As I discussed in the first two blogs of this series, metadata is useful in a variety of ways. Its importance starts at the source system, and continues through the data movement and transformation processes and into operations. Operational metadata, in particular, gives us information about the execution and completion
In the first blog of this four-part series, we discussed traditional data management and how we can apply these principles to our big data platforms. We also discussed how metadata can help bridge the gap of understanding the data as we move to newer technologies. Part 2 will focus on
Traditional data management includes all the disciplines required to manage data resources. More specifically, data management usually includes: Architectures that encompass data, process and infrastructure. Policies and governance surrounding data privacy, data quality and data usage. Procedures that manage a data life cycle from creation of the data to sunset
It's that time of year again where almost 50 million Americans travel home for Thanksgiving. We'll share a smorgasbord of turkey, stuffing and vegetables and discuss fun political topics, all to celebrate the ironic friendship between colonists and Native Americans. Being part Italian, my family augments the 20-pound turkey with pasta –
Just in time for the Strata + Hadoop World Conference, SAS became the first software vendor to achieve ODPi Interoperability with our Base SAS® and SAS/ACCESS® Interface to Hadoop products. Now, that's a lot to digest – so let me back up a second and give some background as to what this
Hadoop has driven an enormous amount of data analytics activity lately. And this poses a problem for many practitioners coming from the traditional relational database management system (RDBMS) world. Hadoop is well known for having lots of variety in the structure of data it stores and processes. But it's fair to
In my last post, I talked about how data still needs to be cleaned up – and data strategy still needs to be re-evaluated – as we start to work with nontraditional databases and other new technologies. There are lots of ways to use these new platforms (like Hadoop). For example, many
If your enterprise is working with Hadoop, MongoDB or other nontraditional databases, then you need to evaluate your data strategy. A data strategy must adapt to current data trends based on business requirements. So am I still the clean-up woman? The answer is YES! I still work on the quality of the data.