Why do we need DataOps Observability?
DevOps was started more than a decade ago as a movement, not a product or solution category.
DevOps offered us a way of collaborating between development and operations teams, using automation and optimization practices to continually accelerate the release of code, measure everything, lower costs, and improve the quality of application delivery to meet customer needs.
Today, almost every application delivery shop naturally aspires to take flight with DevOps practices, and operate with more shared empathy and a shared commitment to progress through faster feature releases and feedback cycles.
DevOps practices also include better management practices such as self-service environments, test and release automation, monitoring, and cost optimization.
On the journey toward DevOps, teams who apply this methodology deliver software more quickly, securely, reliably, and with less burnout.
For dynamic applications to deliver a successful user experience at scale, we still need DevOps to keep delivery flowing. But as organizations increasingly view data as a primary source of business value, data teams are tasked with building and delivering reliable data products and data applications. Just as DevOps principles emerged to enable efficient and reliable delivery of applications by software development teams, DataOps best practices are helping data teams solve a new set of data challenges.
If “data is the new oil,” as pundits like to say, then data is also the most valuable resource in today’s modern data-driven application world.
The combination of commodity hardware, ubiquitous high-bandwidth networking, cloud data warehouses, and infrastructure abstraction methods like containers and Kubernetes creates an exponential rise in our ability to use data itself to dynamically compose functionality such as running analytics and informing machine learning-based inference within applications.
Enterprises recognized data as a valuable asset, welcoming the newly minted CDO (chief data officer) role to the E-suite, with responsibility for data and data quality across the organization. While leading-edge companies like Google, Uber and Apple increased their return on data investment by mastering DataOps, many leaders struggled to staff up with enough data scientists, data analysts, and data engineers to properly capitalize on this trend.
Progressive DataOps companies began to drain data swamps by pouring massive amounts of data (and investment) into a new modern ecosystem of cloud data warehouses and data lakes from open source Hadoop and Kafka clusters to vendor-managed services like Databricks, Snowflake, Amazon EMR, BigQuery, and others.
The elastic capacity and scalability of cloud resources allowed new kinds of structured, semi-structured, and unstructured data to be stored, processed and analyzed, including streaming data for real-time applications.
As these cloud resources quickly grew and scaled, they became a complex tangle of data sources, pipelines, dashboards, and machine learning models, with a variety of interdependencies, owners, stakeholders, and products with SLAs. Getting additional cloud resources and launching new data pipelines was easy, but operating them well required a lot of effort, and making sense of the business value of any specific component to prioritize data engineering efforts became a huge challenge.
Software teams went through the DevOps revolution more than a decade ago, and even before that, there were well-understood software engineering disciplines for design/build/deploy/change, as well as monitoring and observability.
