Improving analytics, visualization and decision-making with Databricks
Following several proofs of concept to enhance its infrastructure, Grammarly decided to migrate from its in-house built solution to the Databricks Data Intelligence Platform. Other vendors that were evaluated, like Snowflake, fell short – they did not support data science and machine learning capabilities, had unpredictable costs with growing scale, and most important for Grammarly, did not enable complete control and ownership over its own data. Bringing all the analytical data into the lakehouse created a central hub for all data producers and consumers across Grammarly, with Delta Lake at the core.
Using the lakehouse architecture, data analysts within Grammarly now have a consolidated interface for analytics, which leads to a single source of truth and confidence in the accuracy and availability of all data managed by the data platform team. Across the organization, teams are using Databricks SQL to conduct queries within the platform on both internally generated product data and external data from digital advertising platform partners. Now, they can easily connect to Tableau and create dashboards and visualizations to present to executives and key stakeholders.
“Security is of utmost importance at Grammarly, and our team’s number-one objective is to own and protect our analytical data,” says Locklin. “Other companies ask for your data, hold it for you, and then let you perform analytics on it. Just as Grammarly ensures our users’ data always remains theirs, we wanted to ensure our company data remained ours. Grammarly’s data stays inside of Grammarly.”
With its data consolidated in the lakehouse, different areas of Grammarly’s business can now analyze data more thoroughly and effectively. For example, Grammarly’s marketing team uses advertising to attract new business. Using Databricks, the team can consolidate data from various sources to extrapolate a user’s lifetime value, compare it with customer acquisition costs and get rapid feedback on campaigns. Elsewhere, data captured from user interactions flow into a set of tables used by analysts for ad hoc analysis to inform and improve the user experience.
By consolidating data onto one unified platform, Grammarly has eliminated data silos. “The ability to bring all these capabilities, data processing and analysis under the same platform using Databricks is extremely valuable,” says Sergey Blanket, Head of Business Intelligence at Grammarly. “Doing everything from ETL and engineering to analytics and ML under the same umbrella removes barriers and makes it easy for everyone to work with the data and each other.”
To manage access control, enable end-to-end observability and monitor data quality, Grammarly relies on the data lineage capabilities within Unity Catalog. “Data lineage allows us to effectively monitor usage of our data and ensure it upholds the standards we set as a data platform team,” says Locklin. “Lineage is the last crucial piece for access control. It allows analysts to leverage data to do their jobs while adhering to all usage standards and access controls, even when recreating tables and data sets in another environment.”