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It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every year, this type of enterprise Data Management instead became cumbersome, and the monolithic nature of the legacy data warehouse model slowed down the data analytics process instead of improving it.
The problem with traditional data warehouses can often be traced to detailed but fragile data pipelines. From the earliest RDBMS to Hadoop and the cloud, complex pipelines that couldn’t support new data or scale to meet demand often slowed data to a trickle and made even the most advanced applications behave like digital dinosaurs. As development improved with the adoption of DevOps, the apps became more efficient, but it wasn’t until recently that the same agility was possible in databases via the rapid iteration offered by DataOps. Coupled with the increasing popularity of large cloud data warehouses from vendors like Snowflake, Databricks, or Cloudera, data pipelines are now overdue for their own modernization.
Enterprise architecture has always been a highly-siloed affair, separated from front-end, business analytics, and even some IT teams. However, as the race to capture and process more and more data continues, the efficiency with which that data is moved and analyzed has become a competitive advantage — hence the growing importance of DataOps. Just like DevOps, DataOps is a process that emphasizes collaboration and agility in Data Management and analysis, allowing more people to interact with the data to continuously improve its value. It also focuses broadly on the way data is transferred, revising not just existing Data Management policies but the very structure of data pipelines responsible for moving information to and from a data warehouse.
The reason DataOps is driving this evolution in data pipelines is that they’ve become central to running a data-driven business. Due to the complex nature of some data architectures, data analytics pipelines are often custom-built by individual data engineers — a process that’s time-consuming and hard to scale. While not a problem for small teams and organizations, as a business grows, new data sources, new workloads, and new business units all need their own pipelines, lowering the efficiency of legacy infrastructure, hampering innovation, and slowing down general decision-making. To top it all off, users that grow disillusioned by slow systems will often try to find workarounds and self-service solutions, creating security issues and animosity between different teams. DataOps tries to solve these problems by reworking the way data pipelines are built, increasing agility and cycle times, and reducing long-running performance problems.
Agile Pipelines for an Agile World
Every data pipeline is different, but all are made for roughly the same purpose — to ingest raw data and turn it into usable information for a particular user or application. This process could be relatively straightforward but often has dozens — if not hundreds of individual steps. With DataOps, every part of the existing pipeline is simplified by being broken down into mini-pipelines, creating opportunities for automation and orchestration of individual tasks. These parts are also more standardized, increasing the interoperability of new pipelines and lowering the need for unsecured workarounds.
By standardizing more time-consuming parts of the pipeline, DataOps creates an environment where agile methodologies can be effectively applied to ensure that data is continuously refined before moving to the next stage in the process. The result is higher-quality information that’s accessible to a greater number of teams through a wider variety of tools or technologies.
Up Next: Automated Data Preparation and Data Catalogs
Adopting DataOps creates opportunities to automate data preparation — one of the most time-consuming and error-prone aspects of Data Management. With a growing number of different data sources and formats, deploying automation tools that help filter out useless data and transform useful information into actionable insights creates a much-needed lifeline for overtaxed data engineers. In addition, the complexity of machine learning workloads, as well as any modern data warehousing or data mining project, makes automated data preparation essential for maintaining productivity and consistency of format when working with dynamic datasets.
Finally, DataOps opens the door to the deployment of data catalogs, which provide secure, enterprise-scale repositories of all the data an organization stores, including metadata, historical, and lineage information. Utilizing a data catalog offers visibility of every piece of information regardless of where it’s stored and simplifies Data Governance policies, helping to limit access to sensitive or proprietary repositories. Data catalogs also allow different teams to collaborate on data verification and data preparation, reducing the time needed to derive fresh insights from new information.
DataOps Means Full Speed Ahead
DataOps isn’t a single solution but rather a series of principles that improve existing data infrastructure through collaboration and continuous iteration. By reimagining how data is collected, transformed, and analyzed, DataOps expands the definition of Data Management and creates new types of data pipelines that are more efficient when supporting complex workloads.
Compared to the brittle, complicated data pipelines that many data engineers are used to, DataOps embodies a leap forward for businesses looking to improve the way they perform Data Management and accelerate the delivery of more valuable insights that create active intelligence for workers at every level of the organization. As workloads and data analytics continue to evolve and increase in complexity, DataOps offers a viable path forward for data-driven businesses that are rapidly transforming to maintain a competitive edge.