Tableau：2017年十大大数据趋势-13页.pdf

TOP TEN
Big Data
TRENDS FOR 2017
Top 10 Big Data Trends for 2017
2016 was a landmark year for big data with anizations
storing, processing, and extracting value from data of all forms
and sizes. In 2017, systems that support large volumes of both
structured and unstructured data will continue to rise. The
market will demand platforms that help data custodians govern
and secure big data while empowering end users to analyze
Each year at Tableau, that data. These systems will mature to operate well inside of
we start a conversation enterprise IT systems and standards.
about what’s happening
in the industry.
The discussion drives
our list of the top big-
data trends for the
following year. These are
our predictions for 2017.
Big data es fast and approachable:
Options expand to speed up Hadoop
1 Sure, you can perform machine learning and conduct sentiment analysis on Hadoop, but the
first 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
Big data no longer just Hadoop:
Purpose-built tools for Hadoop
2 e obsolete
In previous years, we saw several technologies rise with the
big-data wave to fulfill the need for analytics on Hadoop. But
enterprises plex, heterogeneous environments no
longer want to adopt a siloed BI access point just for one