The Need for Full-Stack Orchestration in the Age of the Data Product
3 min read |
Our reliance on data has evolved a lot over the past decade. Once regarded
simply as the rows and columns loaded into data warehouse tables to power
BI dashboards, data is now so much more. It has become a “product”,
powering everything from analytics and AI to data-driven applications that
drive insights and actions within live applications.
There are many reasons why enterprises are rushing to adopt data products.
Key drivers include the improved reliability and trust in data,
composability and reusability, democratized development and usage, faster
innovation with agility and adaptability, closer alignment to the
business, heightened security and governance — all underpinned by lower
cost and risk.
Data Products Bring New Challenges
As all Chief Data Officers and Data Engineering leaders know, while the
timely and reliable delivery of every product recommendation, dashboard,
or fine tuned AI model looks easy, the reality is very different. This is
because:
-
Data products are reliant on a complex web of intricate and opaque
interactions between ecosystems of software, systems, tools, and
engineering teams, each with their own dependencies. -
Orchestration and observability are fragmented across multiple
layers of the data platform, obscuring visibility into the quality of the
data product. -
Platform and data engineers are powerless to prevent data downtime
and pipeline errors. -
Infrastructure provisioning has no awareness of the real time
computational demands of the data pipeline. This results in either wasted
costs or missed SLAs.
Custom tooling and stifled developer experience reduces the pace of
innovation.
Figure 1: Illustrating some of the key responsibilities at each layer
of the stack. Errors or delays in each of these responsibilities can
impact the reliable delivery of a data product.
Data products have become business critical — any failure can have a
direct impact on revenue and customer satisfaction. But the methodologies,
frameworks, and infrastructure for developing, testing, and operating them
in most organizations is at best immature, and in many cases,
non-existent.
What Needs to Change
The way we develop, orchestrate and observe data products needs to change.
What we need to do is unify orchestration with observability across the
full data stack in a single platform while applying best practices from
software engineering to data engineering.
Modern, full-stack orchestration is a new approach designed for the age of
the data product. By unifying orchestration and observability across the
stack, the reliability and trust of data products is improved, development
velocity is increased, costs are lower, data and platform engineering
teams are more productive, and critical data assets are better secured and
governed.
Figure 2: Progressively meeting the needs for modern full-stack
orchestration
Rigorous management of costs, reliability, and productivity is a major
step forward, but the opportunities don’t end there. Modern full-stack
orchestration extends how organizations use data products to drive
competitive advantage by elevating data products into strategic asset
classes that drive innovation.
Getting Started
In our new full-stack orchestration
guide,
we’ll cover the evolution of orchestration, the required capabilities
needed for any modern orchestration platform, and the benefits data and
platform engineering teams can expect from adopting the best-in-class
solution. We’ll highlight engineering teams who are embracing modern
orchestration today along with the results they are seeing before wrapping
up with resources to get you on the journey to modern, full-stack
orchestration.
