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.
What Is DataOps?
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.
DataOps vs. DevOps
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.
Why DataOps Is Needed
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 result in significant reduction of costs 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 ensuring 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 ensures high data quality.
The Snowflake Model for DataOps
The Snowflake Data Cloud is an ideal foundation for building and sustaining an excellent DataOps process. Snowflake’s multistep framework of best practices for DataOps taps into many of the platform’s most powerful features for developing and delivering reliable, high-quality data products that meet the needs of any organization.
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 Snowflake, 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.
Agility and CI/CD
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.
Governance, security, and change control
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. Snowflake’s native features facilitate easy change replication and rollback. In addition, Snowflake’s robust data governance capabilities enable customers to reduce risk and achieve compliance by helping them easily understand their data and control access. Snowflake also includes a multitude of built-in security features such as dynamic data masking and end-to-end encryption for data in transit and at rest, ensuring all data stored in Snowflake stays protected.
Traditional data product development involves very few changes, manually reviewing when periodic changes are made, and a few tests before being placed into production. This approach can result in lapses in data quality. Snowflake’s elastic storage and compute capabilities make it possible to adopt an automated testing approach where it’s possible to run thousands of tests in minutes.
Collaboration and self-service
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 can easily orchestrate data sharing by placing different subsets of data into different Snowflake accounts so that it can be tracked and masked appropriately.
DataOps and Snowflake
The Snowflake Data Cloud for data engineering streamlines the DataOps processes, making it possible to rapidly develop and deploy data products that produce invaluable 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.
Future-proof your DataOps infrastructure by investing in a secure, flexible, and near-zero maintenance solution that easily scales to meet your needs. Break down data silos, capitalize on near-unlimited performance, and create a single source of truth by bringing your diverse data together.
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