Introduction

Organizations are drowning in data and thirsty for knowledge, wisdom, and insights. Data engineering teams in aspiring data-driven organizations are overwhelmed with fast-changing technology and organizational complexities as they look to move from proof of concept (POC) to proof of value (POV) and establish a sustainable operating model with continuous improvement.

For most organizations today, data unification and data integration challenges are growing overwhelmingly complex as they gravitate toward best-of-breed tools in a disaggregated data ecosystem. Data engineering that leverages DataOps and data orchestration is the foundational pillar on which organizations should build their next-generation data platforms in an ever-evolving data ecosystem to scale data teams with all the inherent process variability.

Why do Organizations Need DataOps

All organizations want to be data-driven but there’s a huge disconnect between wanting to be data-driven and getting it done. Bleeding and cutting-edge technologies are immature and not battle-tested and will not get organizations there. It is the operationalization process of technologies that is the key for organizations to become data-driven.

Most data teams do not think about “Day 2” which begins when product teams have completed development and successfully deployed to production. Do they have an end-to-end process to deploy artifacts? Have they tested what they are about to deploy with functional performance, load, and stress tests? Are they ready to roll back production changes if problems happen in production but keep the lights running?

There is a disconnect between doing POCs and POVs with emergent technologies and leveraging them to build and successfully deploy real-life use cases to production. There are a few reasons for this disconnect and most of them can be addressed by the missing component in the data economy: DataOps. Many organizations do DataOps, but it is ad hoc, fragmented, and built without guidelines, specifications, and a formalized process.

Data infrastructures today, spanning ingestion, storage, and processing, are deployed on distributed systems that include on-premises, public and private cloud, hybrid, and edge environments. These systems are a complex mix of servers, virtual machines, networking, memory, and CPU where failures are inevitable. Organizations need tools and processes in place that can quickly do root cause analysis and reduce the mean time to recovery (MTTR) from failures.

DataOps eliminates gaps, inefficiencies, and misalignments across the different set of steps from data production to consumption. It coordinates and orchestrates the development and operationalization processes in a collaborative, structured, and agile manner, enabling organizations to streamline data delivery and improve productivity through multiple process integrations and automations, delivering the velocity to build and deploy data-driven applications with trust and governance.