Metadata Discovery Doesn’t Have to be Painful
Today’s BI and analytics landscape is more complicated than it ever has been. Most organizations have repositories of data everywhere, many BI and analytics technologies, and multiple streams of data integration and/or data preparation – all with their own sets of metadata. No wonder BI implementers and business users are frustrated, confused, and lost when it comes to using these critical assets.
June 6, 2017
Ask the Expert: Should You Learn MapReduce or Spark?
TDWI Members Only
Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer.
June 19, 2017
Machine Learning – What’s All the Hype About?
Machine learning is the analytics buzz word of the day. While some of the techniques have been around for decades, what has changed is the volume and diversity of data as well as the compute power to find insights in that data faster. That means that machine learning against disparate and big data can be used to get to insight – and fast. Machine learning is being used in predictive analytics in numerous use cases from customer behavior analysis to predictive maintenance to image recognition and more. The value is real and growing.
June 21, 2017
Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment
One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs).
June 22, 2017