As analytics projects move into production, the rubber meets the road, as they say, and analytics have to perform and help create value. Timely data is a critical driver for this value. Without fresh data, dashboards are out of date, and there are either poor or no predictions from models, causing end users to lose trust. So it should be no surprise that an entire field exists to ensure that production data gets from point A to point B when we need it — it's called DataOps.
DataOps (short for data operations and not to be confused with DevOps) is a discipline, a set of processes and technology enabling organizations to put and maintain data in production, typically for analytics projects. Analytics projects can include production reports and dashboards or AI and machine learning to make predictions. Whatever the use case, analytics help organizations make better-informed decisions and drive more engaging and valuable customer experiences. For analytics projects of all types, the quality and timeliness of the data are critical to production success.
From a technology perspective, DataOps automates the delivery of data for production analytics. Visual or code-based data pipelines include a step-by-step process for connecting, merging, and transforming data into a functional form. DataOps turns these pipelines into automated production pipelines that run the steps over and over again. DataOps automation requires systematic checks on metrics to look for variations in data and pipeline operations issues.
DataOps technology relies on the use of processes and proven, ideally prepackaged functions and automation. Using an established data pipeline framework decreases the work to develop and automate pipelines and creates more repeatable and reliable results, minimizing downtime from production issues.
DataOps With Dataiku
Dataiku is best known as a data science and analytics platform. However, users know that Dataiku includes a great data pipeline designer and DataOps capabilities to automate pipelines for production use.
Automating and managing data pipelines (DataOps) with Dataiku
When projects move from design to production, Dataiku enables teams to map production data quickly, test projects on production environments, set up pipeline metrics and data quality checks, and trigger pipelines to run on a schedule or based on conditions.