In their work, many data and business analysts spend the bulk of their time on data preparation tasks using a patchwork of tools. I visited with Manan Goel of Paxata to discuss how to reduce this overhead, how to weave together basic and advanced data preparation capabilities, and how analysts can be more efficient and effective in their work.
- By Jake Dolezal
- November 4, 2016
Articles examine analytics tools, hackers and big data, requirements for data lake success, minimizing password risk, and an overview of fog computing.
- By Quint Turner
- November 4, 2016
What does it take to be a data engineer? A background in software engineering doesn't hurt. Although the number of data engineers doubled from 2013 to 2015, that growth rate far outstripped that of data scientists.
- By Steve Swoyer
- November 4, 2016
Today read tips for landing a career in open source development, how to use analytics to improve customer interactions, and the highlights of a new benchmark study for analytics engines on Hadoop.
- By Lindsay Stares
- November 4, 2016
In some emerging best practices, a free-form data lake implemented on Hadoop complements a structured relational data warehouse.
- By Philip Russom
- November 3, 2016
Great data scientists need to be open to a wide variety of perspectives.
- By James E. Powell
- November 3, 2016
How to find value in data that hasn’t been used, what each type of analytics is good for, and making predictions about the future of big data and advanced analytics.
- By Quint Turner
- November 3, 2016
See an updated consolidation of election polling predictions, plus learn how selling data can be a huge opportunity if approached correctly, how to improve the quality of data visualizations, and understand the basic history and current potential of BI.
- By Quint Turner
- November 2, 2016