Enterprise data warehouses (EDWs) are often
deemed the most valuable asset in the data center, serving as the backbone of
the business. The ongoing insight gained from these solutions has justified the
significant up-front capital investments and ongoing operational costs, but the
rigidity of the traditional EDW is forcing organizations to reevaluate their
approach to analytics and business intelligence.
While legacy EDW solutions were all about
throwing as much computational power as possible at a relatively static data
set, with the inflow of new and valuable sources of data and the emergence of
all-encompassing analytics initiatives, the success of today’s EDW solutions
depends more on operational and resource agility than raw horsepower.
Being able to dynamically adjust to the
needs of the business, integrate into operational processes, and quickly react
to emerging opportunities can place an organization at a distinct competitive
Today’s EDW solutions must act as a global
repository of information, provide the agility to scale up or down on demand,
and seamlessly integrate with other analytics tools and services used
throughout a data-driven organization.
Over the last two years, ESG has conducted
detailed studies quantifying the economic value of Google data analytics
services. The first evaluated Google BigQuery compared to on-premises Hadoop and AWS redshift. The second focused on Google DataProc compared to DIY Spark and Hadoop approaches. Here’s
the next iteration of our economic analysis, extending the BigQuery study to
incorporate a comparison to legacy enterprise data warehouses, both on-premises
and in the cloud.
Through publicly available pricing and
in-depth qualitative customer interviews, ESG was able to assert a base set of
assumptions that power a dynamic model, incorporating up-front capital
investments, deployment and migration costs, expected monthly cloud costs,
administrative costs, and operational costs associated with legacy on-premises
EDWs, cloud-based EDWs, and Google BigQuery.
The crux of the results show organizations
can save up to 52% by using BigQuery over on-premises EDWs and up to 41% over
cloud-based EDWs. Unlike legacy on-premises EDWs, BigQuery provides
organizations with the key abilities that are essential to delivering a modern
EDW solution, most notably the ability to integrate across other Google Cloud
Platform services, including its market leading AI-based solutions and
Although not called t out directly in the
published report, ESG’s models indicate that the savings achieved by migrating
an on-premises EDW solution to Google BigQuery may actually be more cost
effective than simply continuing to operate an existing on-premises EDW