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首页2017大数据发展趋势Top10白皮书
2017大数据发展趋势Top10白皮书

2016 was a landmark year for big data with more organizations 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 that data. These systems will mature to operate well inside of enterprise IT systems and standards. Each year at Tableau, we start a conversation
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TOP TEN
Big Data
TRENDS FOR 2017

Top 10 Big Data Trends for 2017
2016 was a landmark year for big data with more organizations
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
that data. These systems will mature to operate well inside of
enterprise IT systems and standards.
Each year at Tableau,
we start a conversation
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 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
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