DataOps.live Enables Data Engineering Acceleration

I have previously written about the critical importance of data management to the development of artificial intelligence (AI) applications and agentic AI. The importance of data management is nothing new, but automation of business processes and decision-making raises the stakes in terms of the expectations and the risks. The need for enterprises to have trust in their data governance and data management processes and platforms has never been greater. That need for trust also extends to data engineering and data operations, including the development, orchestration and monitoring of data pipelines used to support the creation of data products. One of the software providers at the forefront of addressing this requirement, DataOps.live, has recently updated and expanded its product portfolio to facilitate the operationalization of data to support trusted AI.

DataOps.live was founded in 2018 with the goal of helping enterprises reduce time to insight and accelerate time to value from analytics and data initiatives. The company was an early participant in the DataOps movement, which focuses on the application of agile development, DevOps and lean manufacturing by data engineering professionals in support of data production. In 2020, the company shared details of its TrueDataOps philosophical approach, as well as the first iteration of its DataOps.live platform, providing capabilities for the automation, orchestration and monitoring of data engineering pipelines and data products for users of Snowflake’s AI Data Cloud. I assert that by 2027, more than 3 in 5 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to domain-based data ownership. DataOps.live remains focused on Snowflake environments, and Snowflake is also an investor in the company, having contributed to DataOps.live’s $10.3 million seed funding round in January 2022, as well as its $17.5 million Series A funding round in May 2023 and a further investment in March 2025. Other investors in the company include Notion Capital and Anthos Capital. DataOps.live was classified as Exemplary in the 2025 ISG Buyers Guides for Data Pipelines and Data Observability and Innovative in the 2025 ISG Buyers Guides for DataOps, Data Orchestration and Data Products.

The DataOps.live platform has been enhanced since it was introduced and now provides a combination of data pipeline orchestration and management, federated governance and data observability, and data platform automation and cost optimization capabilities. The company announced a major update to its DataOps.live platform in September with the launch of its Momentum release. The latest release was designed to facilitate the management and preparation of data for use with AI. This is a significant challenge for data practitioners. More than one-half (54%) of participants in the ISG AI and Data Market Lens Study rated data usability for AI as their primary data challenge. Key capabilities delivered by DataOps.live to better address the preparation of data for use with AI include the continuous development, testing and deployment of data pipelines to align with Continuous Integration and Continuous Delivery of enterprise applications, the continuous monitoring and availability of data pipelines and the operationalization and enforcement of governance policies.

New capabilities delivered with the latest release include AI-Ready Scoring to validate data for use in AI use cases. In addition to the assessment of data products to ensure they are discoverable, accessible, interoperable and reusable, the new capability evaluates data using multiple quality and integrity factors including accuracy, timeliness, completeness and alignment with governance policies. DataOps.live has also evolved its DataOps.live Assist functionality to become the Metis Data Engineering AI Agent, which is designed to automate key data engineering tasks, including pipeline generation, testing, documentation and governance enforcement. The Momentum release also delivered data product lineage capabilities to provide visibility into how data products are created, modified and used. As I previously noted, while many data product platform providers offer functionality that enables users to publish and discover data products, fewer provide capabilities to monitor data product usage, evaluate performance metrics or develop and manage data contracts. Prior to the DataOps.live platform update, the company also recently introduced the first two deliverables of its Dynamic Suite, which includes free Snowflake Native Apps delivering discrete functionality to address key data engineering challenges and provide an on-ramp to adoption of the wider platform. The first two Dynamic Suite apps are Dynamic Transformation to automate, orchestrate and govern transformation pipelines built using dbt Core and Dynamic Delivery to automatically validate, test and deploy Snowflake schema changes. Both are available in the Snowflake Marketplace and are free to use up to 500 credits (essentially 500 minutes of runtime) per month.

In addition to these new releases, DataOps.live has also enhanced the ability to take advantage of Apache Iceberg tables to access and manage data stored in Parquet files on S3 in the creation of data products using DataOps SOLE (Snowflake Object Lifecycle Engine) for Data Products. As I previously explained, widespread adoption of Apache Iceberg raises the potential for it to become the lingua franca for storing and analyzing both transactional and event data across multiple providers. If, as seems likely, enterprises adopt Apache Iceberg as a common data plane across data platforms from multiple providers, there is the potential for DataOps.live to evolve its focus beyond Snowflake to provide a common control plane for data engineering that spans multiple data platforms. For now, DataOps.live remains focused on Snowflake AI Data Cloud, which naturally limits the company’s addressable market and presents a potential risk of being disrupted by Snowflake developing or acquiring similar functionality. Nevertheless, I recommend that enterprises interested in delivering data as a product and using Snowflake evaluate DataOps.live and its capabilities for building, testing and deploying data products and applications.

Regards,

Matt Aslett