Big data becomes fast and approachable:
Options expand to speed up Hadoop
Sure, you can perform machine learning and conduct sentiment analysis on Hadoop, but the
rst question people often ask is: How fast is the interactive SQL? SQL, after all, is the conduit
to business users who want to use Hadoop data for faster, more repeatable KPI dashboards as
well as exploratory analysis.
This need for speed has fueled the adoption of faster databases like Exasol and MemSQL,
Hadoop-based stores like Kudu, and technologies that enable faster queries. Using SQL-on-
Hadoop engines (Apache Impala, Hive LLAP, Presto, Phoenix, and Drill) and OLAP-on-Hadoop
technologies (AtScale, Jethro Data, and Kyvos Insights), these query accelerators are further
blurring the lines between traditional warehouses and the world of big data.
FURTHER READING: AtScale BI on Hadoop benchmark Q4 2016
1
评论1