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Datameer is a Cutting Edge Solution for Snowflake Data Preparation

Snowflake has helped democratize the data platform eliminating layers of technology and administrative traditionally required to enable data workers. The next generation of data preparation tools arrived in recent years and continues to accelerate the process for preparing business ready data assets.

In my previous role managing data and analytics, I had a big problem. All of my data was staged in Snowflake but data engineering was backlogged with requests and my analysts were stuck between SQL and the last generation of desktop BI data preparation tools. I solved this problem and tripled my team’s throughput with Datameer.

Datameer is a native Snowflake data preparation and analysis solution that does not require extracting any data out of Snowflake. I used for all of my BI reporting and dashboard projects, allowing me to roll out 3 times more business ready data assets in 2 months than the first 5 months of my Snowflake initiative.

There are a few key features of Datameer that helped us wrangle data faster. Enterprise data is imperfect, so you need the right tools to profile, explore, and understand imperfections while you build.

Data-Driven Join Analysis

One of these features is Join Analysis, which offers unmatched Rows that enable me to quickly see which records fall out of the left and right side of the join. With this feature, I can easily identify records that are missing IDs or recognize that I didn’t fully understand the grain of data before I joined. The Join Keys analysis feature also identifies duplicate records and highlights which data source is causing duplicates or potential cartesian products as a result of duplicate keys. These features enable me to understand my data both inside and out of each join, allowing me to move forward more efficiently in my data flow.

Tutorial: Learn how to join data intelligently with Datameer

Explore and Share Data in One Place

Another useful feature of Datameer is inline no-code Data Exploration. This feature is essential when exploring data and validating it with collaborators. Datameer provides intuitive and fast exploration capabilities so you can create many cuts of data through your data pipeline. You can employ filtering, aggregation, binning, and sorting. It only takes about 5 minutes to master this feature, and it has enough functionality to cover most real-world slicing and dicing. For repeatable or reusable scenarios, the exploration nodes feature enables me to make my exploration view available or deploy it as its own view back to Snowflake for recurring validation.

Tutorial: Explore and share data in Datameer

One Click Field Profiling

Field Exploration is yet another useful feature of Datameer, as it prepares a summary profile for each field and provides a visual reference point for quickly identifying outliers, NULLs, district values, and unique records. This feature is similar to Snowflake and helps me quickly and efficiently understand my data.

Tutorial: Field Profiling and Exploration

Datameer is No-code where you want it.. SQL Coding where you need it

Datameer offers a no-code user experience that will technically allow you to build and deploy business intelligence views and tables without writing a line of code. There are conversely many experienced SQL developers who are more proficient writing SQL than using no-code interfaces. Datameer is best of both worlds because you can visually abstract your SQL code into a flow and have all Snowflake SQL functions on hand. This way you can still beneifit from the aforementioend features while coding. Datameer with generate your SQL as a CTE that runs natively on Snowflake.

Change Tracking and Revisions

In addition to a rich meta and tagging, Datameer offers deployment history and version control natively, allowing you to comment revisions, single click restoration to previous deployments in Snowflake, and full access to the SQL code.

Overall, I am impressed with Datameer’s capabilities and look forward to every release with incremental updates focused on bringing data teams and data analysts together in a practical solution.

author avatar
Ryan Goodman Founder
Ryan Goodman has been in the business of data and analytics for 20 years as a practitioner, executive, and technology entrepreneur. Ryan recently created DataTools Pro after 4 years working in small business lending as VP of Analytics and BI. There he implanted an analytics strategy and competency center for modern data stack, data sciences and governance. From his recent experiences as a customer and now running DataTools Pro full time, Ryan writes regularly for Salesforce Ben and PactBub on the topics of Salesforce, Snowflake, analytics and AI.