Martin Fowler in his blog on polyglot persistence proclaims:
“I’m confident to say that if you’re starting a new strategic enterprise applica-
tion, you should no longer be assuming that your persistence should be rela-
tional. The relational option might be the right one--but you should seriously
look at other alternatives.”
You need to match a specific workload to an execution engine tailored to the
workload. You could run full-text searches on your data warehouse, or even your
NoSQL MongoDB cluster, but that approach is not going to beat the performance
of an ElasticSearch Engine. Imagine a world where the organization is supported
by: a Hadoop Distributed File System (HDFS) backed data lake; a massively par-
allel processing data warehouse appliance for the very hungry, joint intensive
queries; Apache Hive on Tez with LLAP for batch SQL queries; Apache Spark for
stream analytics and machine learning activities; an ElasticSearch cluster for
search based analytics; and a MongoDB based Product Catalog.
Developing the infrastructure, processes and skills to build on and support
such a diverse set of technologies requires a fundamental strategic shift and a
long-term commitment to that shift and its price tag on the part of the
enterprise.
Cloud Rather than On-premises
• How do we modernize our analytics infrastructure to support a large spec-
trum of workloads to enable business users to extract maximum value?
• How does the IT department enable self-service and get out of the way
of innovation?
• How do we do this with the best possible economics?
Enterprises need to seriously consider migrating their analytics workloads to
the cloud. This is a self-healing, auto-scaling infrastructure with multiple clus-
ters that support a variety of tools and workloads and enable self-service ana-
lytics. The cloud will significantly lower downtime and TCO with increased
performance and tighter security.
At CTP, we have started this journey with multiple organizations by first making a
careful assessment of the analytics application portfolio and building a TCO model
that highlights the benefits of cloud economics.
Key Considerations for Modernization
We strongly believe that it is going to be increasingly difficult for an enterprise
to build, maintain and evolve an on-premises analytics infrastructure that sup-
ports the complex and varying need for data, analysis and reports across the
organization. Consequently, we make the critical assumption that as an orga-
nization, you are committed to taking full advantage of cloud technology for
data warehouse modernization. Here are some strategic ways to proceed.
26 | THE DOPPLER | SPRING 2017