Big Data Articles to Read This Week #1
Interested in big data? Learn about Apache Arrow, the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering, how IoT helps Lady Gaga in her performances, why banks need to adopt omnichannel service and more in our big data digest.
It offers performance improvements of more than 100x on analytical workloads, the foundation says. In general, it enables multi-system workloads by eliminating cross-system communication overhead.
In many workloads, between 70 percent and 80 percent of CPU cycles are spent serializing and deserializing data. Arrow alleviates that burden by enabling data to be shared among systems and processed with no serialization, deserialization or memory copies, the foundation said.
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering. It allows easy reuse of components, simplifying model building and changes.
Intel Helped Lady Gaga to Create Her Grammy’s Show
Watch the video to learn how IoT helps Lady Gaga in her performances.
In its Sept. 2015 survey of 9,000 global bank customers, FIS warned that Canadian banks must adopt omnichannel service or risk losing “young customers (who) are already shifting their behaviours toward usage of alternative financial service providers.”
Despite a high level of interest in big data among banks worldwide, a Dec. 2014 study by EY found that a shortage of analytics talent is a key challenge; only seven percent of those surveyed said they had “sufficient numbers” of data analysts throughout their business. Two other top obstacles to maximizing their data were regulatory issues (cited by 43 percent) and privacy concerns(named by 36 percent).
What’s the big deal about big data?
- Variety — IBM estimates 80% of data is unstructured and, unlike structured data, tools available to analyze it are limited.
Recently, the Power BI team have published a tutorial that analyzes pages on Facebook. While the official tutorial showed you the basics, it didn’t drill down into Facebook page insights, which hold additional crucial data for marketers. Learn how to create a dashboard that compares Facebook insights between different brands. Even those you don’t manage
Did we miss something? Share links to the most interesting big data content in the comments.