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.
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
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.
