Technology-focused discussions about genomics usually highlight the huge growth in DNA sequencing since the beginning of the century, growth that has outpaced Moore’s law and resulted in the $1000 genome. However, future growth is projected to be even more dramatic. In the paper “Big Data: Astronomical or Genomical?”, the authors say it is estimated that “between 100 million and as many as 2 billion human genomes could be sequenced by 2025”,
Organizations analyze logs for a variety of reasons. Some typical use cases include predicting server failures, analyzing customer behavior, and fighting cybercrime. However, one of the most overlooked use cases is to help companies write better software. In this digital age, most companies write applications, be it for its employees or external users. The cost of faulty software can be severe, ranging from customer churn to a complete firm’s demise, as was the case with Knight Capital in 2012.
Over the past year (and through several releases), Apache Impala (incubating) has added numerous new features and performance enhancements better enabling high-performance SQL analytics over big data. Thus, it is time again for an update to the Impala cookbook, which contains best practices for these new features, updated guidelines, and more detailed examples.
Note: This cookbook does not yet capture best practices for the major new advancements available with the recent GA of Kudu.
Cloudera Enterprise 5.10 includes the latest updates of Hue, the intelligent editor for SQL Developers and Analysts.
As part of Cloudera’s continuing investments in user experience and productivity, Cloudera Enterprise 5.10 includes an updated version of Hue. We provide a summary of the main enhancements in the following part of this blog post. (Hue from C5.10 is also available for a quick try in one click on demo.gethue.com.)
The Hue editor keeps getting better with these major improvements:
The number of rows returned is displayed so you can quickly see the size of the dataset.
Time-series analysis is becoming mainstream across multiple data-rich industries. The new spark-ts library helps analysts and data scientists focus on business questions, not on building their own algorithms.
Have you ever wanted to build models over measurements coming in every second from sensors across the world? Dig into intra-day trading prices of millions of financial instruments? Compare hourly view statistics across every page on Wikipedia? To do any of these things, you’d need to do a large sequence of measurements over time.