DataOps for data speed and quality

Modern business moves quickly, and risks and opportunities must be acted on with speed. For a company to operate successfully in today’s world, every team within the organization must be able to access data-driven insights at the speed of business. Data operations (DataOps) is a methodology based on the agile model that’s designed to reduce the time between data need and insight.

DataOps is a process powered by a continuous-improvement mindset. The primary goal of the DataOps methodology is to build increasingly reliable, high-quality data and analytics products that can be rapidly improved during each loop of the DataOps development cycle. Faced with a rising tide of data, organizations are looking to the development operations (DevOps) methodology as a model for quickly developing and releasing high-quality data products in a dynamic development environment. Although many similarities exist between DataOps and DevOps, these two processes share distinctly different goals.

Although DataOps is often referred to as “DevOps for data,” this process is now firmly established as an independent methodology. Let’s look at the differences between the two.

DevOps: The DevOps framework marries the engineering component of product development with the operational side of product delivery. This continuously looping process starts with the development team planning, creating and packaging software deliverables. Once completed, the operations team releases the product and monitors its deployment. When new features or fixes to the current product are needed, the operations team provides this information to the development team, and the continuous build and delivery lifecycle begins anew.

DataOps: The primary goal of DataOps is to quickly identify and prepare the right data to satisfy a business need. It emphasizes efficient collaboration between business users, data scientists, analysts, IT teams and developers. Borrowing from its DevOps heritage, DataOps leverages iterative processes for quickly building data pipelines capable of funneling high-quality data to end users for analysis and interpretation. With the initial build complete, the focus of DataOps shifts to continuous improvement, fine-tuning data models, dashboards and visualizations to meet the evolving data needs required to best achieve the desired business objectives. This iterative, continuously looping cycle of improvement offers many advantages over more-static approaches to data collection, processing and analysis.

DataOps is a highly effective solution for harnessing the power of today’s rapidly evolving data streams. This agile, automated process allows smaller data teams to develop and deploy data solutions in less time. Shortened development time frames can result in cost reduction and allow organizations to achieve their goals more quickly.

Multiple teams work in parallel on the same data project, allowing each group to deliver results in tandem. In addition, the DataOps framework easily integrates data from multiple sources in a variety of formats, accelerating the process while helping ensure that all relevant data is incorporated into the finished data product.

DataOps’s abbreviated development and deployment cycle provides stakeholders with quicker access to insights, while the continuous development, testing and deployment cycle enables high data quality.

To build the ideal foundation for building and sustaining an excellent DataOps process, the following components should be considered as essential:

Modern cloud data warehouses enable data to be transformed after loading through the extract, load, transform (ELT) process. ELT speeds DataOps because it allows data to be loaded to the final destination system rather than first passing through staging. With a modern data platform, data can be transformed within the platform itself rather than extracting it to transform it off-platform. This reduces latency and increases agility, enabling faster time to insight.

Any organization’s DataOps process should include a standardized, easily repeatable process for both data and schemas. Developing and maintaining a consistent set of operational procedures makes continuous improvement and continuous development (CI/CD) possible.

Data processes work best when they mirror current software development best practices by creating small, independent pieces that can then be easily assembled to create a larger, finished product. Thinking small makes it much easier to understand, test and maintain more-complex data products.

Successfully managing the DataOps development environment involves building production, development and test instances that support the principles of CI/CD, including managing trunk and feature branch databases.

With multiple teams working on the same data product simultaneously, it’s critically important that every change must be recorded in a shared repository so it can be tracked, replicated (or rolled back), approved and reported on for audit. Robust data governance capabilities enable customers to reduce risk and achieve compliance by helping them easily understand their data and control access. Also desirable are built-in security features such as dynamic data masking and end-to-end encryption for data in transit and at rest, helping protect data.

Traditional data product development involves very few changes, manual review when periodic changes are made, and a few tests before being placed into production. This approach can result in lapses in data quality. A modern data platform with elastic storage and compute capabilities make it possible to adopt an automated testing approach where it’s possible to run hundreds or thousands of tests in minutes, depending on the scenario.

Using a cloud data platform that enables users and teams to collaborate using self-service data results in faster development and more-comprehensive finished data products. Allowing the entire organization to access governed data can be achieved using structured anonymization. Organizations should be able to easily orchestrate data sharing by placing different subsets of data into different accounts so that it can be tracked and masked appropriately.

The Snowflake AI Data Cloud streamlines DataOps processes for data engineering with Snowpark, making it possible to rapidly develop and deploy data products that produce valuable business insights. 

Snowpark is a developer framework for Snowflake that brings data processing and pipelines written in Python, Java and Scala to Snowflake's elastic processing engine. Snowpark allows data engineers, data scientists and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using their language of choice.