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Scan, maintain and transform a catalog of Snowflake datasets and your team’s work.
Materialize models in Snowflake for easy integration and downstream consumption
Deploy production pipelines directly from Datameer or through integration with dbt
We centralize our data in Snowflake and use Datameer to build the data models needed to support the various analytics projects across the organization directly in Snowflake. Historically, these models were built using lots of SQL code taking months to develop and roll out. Compared to other data transformation tools, Datameer’s intuitive, in-Snowflake, no-code solution enables my teams to publish ready-to-use data in hours.
The Datameer platform was a godsend. I was looking at hiring a team of data engineers to hand-code all our data pipelines in Python. In a week, we were able to complete all those complex ETL jobs with Datameer, which otherwise would have required a dozen data engineers to develop and maintain.
Our SQL solution did not scale well with the growing volume of data we were getting, and it was becoming costly to maintain. The bigger the database got, the slower the queries became. Not only did Datameer solve that query performance and scalability issue, but it also managed to make it super easy for our researchers and analysts to use without having to write any code.
Only Datameer could provide us with a product that offered a very fast no-code and low-code environment and could meet the needs of our extremely complex data pipelines and DataOps.
One of the challenges and opportunities with the Internet of things is that there has been an explosion of stream data. Instead of having one row per customer, there may be thousands or millions of rows per customer. With its intuitive Excel-like user interface, Datameer made it extremely easy for my team to get up and running very quickly.
Analytic cycle times covering the bank’s numerous marketing campaigns were reduced from five days to five hours. Thanks Datameer!