Database Reference
In-Depth Information
A
APPENDIX
Understanding the
Big Data Toolset
Big data refers to a broad set of tools that are designed to work with large data volumes. These tools
are gaining popularity because they solve the limitations of traditional tools by scaling up on hardware
and sharing the workload across many linked computers. Using this divide-and-conquer approach
allows big data technologies to accomplish tasks that are not feasible otherwise.
When it comes to analytics, there are some special considerations regarding big data. You need an
easy way to access and navigate platforms without the need to spend precious time learning new
programming languages such as Java. In the past few years several tools have come available that
offer a big data SQL solution: a tool that sits on top of a big data platform and allows you to write SQL
queries against the big data engine.
In this appendix we review the big data SQL tools on the market now, giving you a sense of the big
data landscape. You'll also discover how you can connect to those tools from Excel.
Big Data SQL Offerings
There are several big data offerings that support a relatively easy-to-use SQL engine on top of
Hadoop or another similar-style platform.
We cover the talked-about entrants into the market as of this writing. Note, though, that
this space is quite new and many companies are jumping into it almost daily.
Note
Most of these technologies have a similar approach to big data SQL. They scale up on hardware and
use a divide-and-conquer approach to break big data volumes into small chunks and distribute the
processing across multiple nodes. The main differentiator among the various technologies is whether
or not they are Hadoop-based. For example, Amazon Redshift does not sit on top of Hadoop, but
instead uses a different technology that allows similar scalability as Hadoop. On the other hand,
Hortonworks and Cloudera are tools that are installed on top of Hadoop and take advantage of its
scalability features.
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