Domo and Snowflake, part 1: Data ingestion made easy

Enterprises often face unique challenges when it comes to extracting data. With the sheer amount and range of data they collect, they gravitate toward enterprise data warehouses (EDWs), which work exceptionally well at reading data but aren’t as good at ingesting new datasets.

As a result, the time to value slows, creating unnecessary delays. This is particularly problematic when domain experts need to make clear, insightful decisions, fast.

Modern BI for all begins by shrinking the time between the ingestion of data and the insight it generates. This requires a new approach to data architecture—one that decentralizes data ownership and puts more power into the hands of domain experts. It requires that the rigidity and friction that comprise so many of our older data processes be relaxed and removed.

Much of this rigidity and friction is a result of the problems that come from ingesting data at scale. Data engineers can have a difficult time keeping up with building the databases to demand. Additionally, backlogs can form from processing and cleaning up the data coming in from various sources.

For enterprises with a variety of both legacy and modern systems, datasets can require that new integrations be written and tested in order to get the data fed into the EDW. In an effort to navigate limited resources without sacrificing quality, data engineers have developed processes that run slower than many domain experts would like.

An EDW like Snowflake is designed to store all of an organization’s data from sources across the entire business. For enterprises turning to Snowflake, there are a number of things to consider when making that transition and when trying to derive as much value from the data as possible. Challenges arise from the code and business logic as well as from how workloads are structured. The process is complicated, slow, and (potentially) costly.

The good news is that it doesn’t have to be this way. Domo’s deep integration with Snowflake allows for a combination of Domo’s data integration speed and Snowflake’s robust EDW architecture, which is made possible by features such as loading tools that complement your existing ingestion architecture and a library of more than 1,000 connectors and APIs.

When Domo and Snowflake are used together, data is ingested faster and decision-makers are more likely to have access to the data they need when they need it. Much of the rigidity and friction that characterizes the old model for data ingestion can be done away with.

For enterprises to take advantage of a modern integrated data pipeline, they will need to think differently about how data architecture works. But they won’t actually have to create that architecture, all because Domo and Snowflake have built a dynamic integration that helps enterprises get the most out of their EDW investments.

To learn more about the benefits of using Domo and Snowflake together, stay tuned for posts two and three in this series—or download our whitepaper Accelerating the Data Lifecycle now. To see Domo for Snowflake in action, click here.