November 18th, 2013
Stephen McDaniel
Chief Data Officer Advisor at Freakalytics, LLC
Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here’s our curated summary of relevant news that could help with your future data and analytic projects. We also add commentary on the topic, a summary of the article and the link to read the full article.
There are four articles in this update:
Amazon wades into big data streams with Kinesis
Top 10 Trends in Text Analytics
Effective Customer Analytics Call for Data Integration, Culture Shifts
Your Car Is a Data Platform, What Can It Tell About You?
Missed our last issue of Top News, November 15th? Stories included RapidMiner (free and premium data mining), big data not top CFO priority, the DATA Act passes Senate, SAS replacing PowerPoint and big data sources to consider at your company.
Amazon wades into big data streams with Kinesis
Amazon adds another layer to data storage and streaming options-Kinesis. Kinesis is all about real-time data collection and aggregation in a hosted cloud-scalable from Megabytes to Terabytes per hour! As such, it is a service that keeps your data for a maximum of 24 hours, by which time you presumably used it or stored it in a data warehouse (like Amazon Redshift), Hadoop system (like Amazon Elastic Map Reduce), NoSQL system (like Amazon Dynamo DB) or file store (like Amazon S3!) Do you notice a trend here?
Rapid-I data mining now RapidMiner,
Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here’s our curated summary of relevant news that could help with your future data and analytic projects. 
How Big Data Is Changing Science (and Society)
Whether you are a CIO, data architect, or a data management professional, it is imperative to understand the different approaches, attitudes and needs of the next generation of data warehouse consumers. Traditional data warehouse users include reporting teams, BI teams (who created reports for the rest of the company), statisticians and others. In the past few years, this has been rapidly changing with the new roles of data scientists, the rise of Data Enthusiasts and the burgeoning population of Accidental Analysts. In Part 1 of this series, we focus on successful collaboration between data scientists and data warehouse teams.