Powering Customer-Facing Dashboards at Scale Using Rockset with PostgreSQL at DataBrain
Customer Company Size
Startup
Region
- America
Country
- United States
Product
- Rockset
- PostgreSQL
- Amazon RDS
- Amazon S3
Tech Stack
- Serverless Architecture
- SQL
- Data Lake
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Data Management Platforms
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Software
- Professional Service
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
DataBrain is a SaaS company that provides go-to-market teams with data-driven insights about the health of their accounts by leveraging real-time customer data. By connecting to a wide range of existing SaaS tools and analyzing the data, DataBrain’s dashboard surfaces recommendations for account teams, allowing them to drill down into data to discover valuable insights. The company focuses on helping GTM teams such as customer success, sales operations, and product teams to focus their time and craft their communication based on real-time account data. DataBrain is described as 'the operating system for GTM teams' by its CEO and founder, Rahul Pattamatta. As a quick, fast-growing company in a competitive space, DataBrain aims to provide the fastest user experience and minimize the time customers take to reach their 'aha moment' in the market.
The Challenge
DataBrain, a fast-growing SaaS company, faced several challenges with its data stack as it scaled. Initially using PostgreSQL through Amazon RDS for landing and querying customer data, DataBrain encountered high query latency and inefficiencies in handling large volumes of data. The dynamic schema of incoming customer data further complicated the process, requiring significant effort to manage schema changes. Additionally, DataBrain needed to accelerate customer time-to-value by providing fast, actionable insights without requiring extensive setup or engineering support.
The Solution
To address its challenges, DataBrain adopted Rockset, a serverless data platform, to enhance its data processing capabilities. Rockset's native integration with PostgreSQL allowed DataBrain to sync desired datasets instantly and automatically, preparing the data for queries in seconds. This integration enabled DataBrain to offload a significant portion of its data from PostgreSQL to an S3 data lake, optimizing storage costs. Rockset's support for schemaless ingestion of semi-structured data eliminated the need for complex ETL pipelines, allowing DataBrain to manage dynamic schemas more efficiently. Additionally, Rockset's Converged Index technology provided fast query results, enabling DataBrain to deliver real-time insights to its customers. This solution allowed DataBrain to scale its operations, improve performance, and maintain a competitive edge in the market.
Operational Impact
Quantitative Benefit
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