DataBrain: Buyer-Dealing with Dashboards on Rockset & Postgres



  • DataBrain, a SaaS firm, was utilizing PostgreSQL by Amazon RDS to land and question incoming buyer knowledge.
  • Nonetheless, PostgreSQL couldn’t scale, rapidly ingest schemaless knowledge, or effectively run analytics as DataBrain’s knowledge grew.
  • Plus, incoming buyer knowledge had a dynamic schema, making it painful and costly for DataBrain to wash the info for PostgreSQL and run queries.
  • Rockset solved these knowledge issues, delaying the necessity to rent an information engineer and saving DataBrain storage prices by offloading some knowledge to Amazon S3.

The Working System for GTM Groups

Organizations perceive that their means to make their prospects comfortable and profitable is straight correlated to the standard of insights they’ll draw about every buyer. And these insights should not solely be related, however actionable in actual time. Realizing a buyer is confused as we speak as a substitute of tomorrow might be the distinction between conserving the client comfortable and conserving the client, interval. This downside is very acute for groups whose job is to proactively interact with prospects. That is the place DataBrain steps in.

DataBrain offers go-to-market groups with data-driven insights in regards to the well being of their accounts by leveraging real-time buyer knowledge. By connecting to a variety of current SaaS instruments after which analyzing the info, DataBrain’s dashboard surfaces suggestions for account groups, in addition to permits them to drill down into knowledge to find useful insights.

Maybe the account hasn’t been adopting new options, or it has had vital contact factors with assist not too long ago. That highlights a possible churn threat. Or maybe the account has taken benefit of recent capabilities, highlighting an upsell alternative. DataBrain analyzes a variety of information factors throughout the client’s system and recommends potential actions.


With DataBrain, GTM groups corresponding to buyer success, gross sales operations and even product know the best way to focus their time and craft their communication based mostly on real-time account knowledge. CEO and founder Rahul Pattamatta describes DataBrain as “the working system for GTM groups.”

However as a fast, fast-growing firm in a aggressive house, DataBrain was operating into a number of challenges with its knowledge stack.

Problem 1: Scaling PostgreSQL for Analytics

DataBrain was utilizing PostgreSQL by Amazon RDS to land and question each incoming buyer knowledge in addition to inner firm knowledge. This made sense when DataBrain didn’t have massive quantities of information or advanced queries to run. PostgreSQL within the cloud was additionally simple to arrange and well-established as a expertise.

Nonetheless, DataBrain’s buyer base and utilization was rising quick. One buyer was already producing 60 million rows of information. That was when DataBrain began to run into the pure limitations of PostgreSQL: excessive question latency for any sort of analytical question. PostgreSQL is simply not optimized for analytics. This was particularly obvious at scale.

“Writing SQL in opposition to an RDS occasion was simply unattainable,” Pattamatta stated. “Our queries had been taking too lengthy and our app began to day trip. This was unacceptable to our prospects.”

DataBrain initially experimented with the extra analytics-optimized Amazon Redshift, however discovered it too gradual for its use case, with queries taking near 10 seconds.

Problem 2: Managing Always-Altering Schema on Buyer Information

One other downside DataBrain confronted was efficiently ingesting the semi-structured buyer knowledge into PostgreSQL.

“We have now to handle a dynamic schema and folks defining a bunch of various metrics of their JSON,” Pattamatta stated. “It was actually onerous for us to know what they had been sending us.”

Each time new columns had been added to JSON, the engineers at DataBrain went by nice effort to scan and establish the modifications within the schema earlier than updating the info. This wasn’t sustainable. DataBrain wanted a more-automated approach to detect and handle schema modifications.

“I didn’t need to rent an information engineer to jot down ETL scripts to make these transformations everytime,” Pattamatta stated.

Problem 3: Accelerating Buyer Time-To-Worth

Lastly, DataBrain wanted to spice up its efficiency.

“It is a aggressive house and as a way to stand out, I needed to verify our product has the quickest person expertise and our prospects expertise the least time to their aha second available in the market,” Pattamatta stated.

This meant with the ability to mechanically index the info throughout the preliminary ingest in order that prospects can effortlessly get insights straight away on no matter questions they’ve.

“I need our product to be as self-service as attainable,” Pattamatta stated. ”I noticed different options that required prospects to spend quarter-hour with an engineer to arrange the preliminary integrations. I need my prospects to simply level their integrations at us and have it work inside seconds.”

Serving to DataBrain Scale and Speed up

Pattamatta heard about Rockset on a podcast with Rockset’s CTO and co-founder Dhruba Borthakur.

“I used to be initially drawn to Rockset as a result of it appeared to supply a sublime resolution to my schema downside,” Pattamatta stated. “The truth that it may do analytics rapidly was additionally necessary.”

Pattamatta was impressed by how straightforward it was to deploy Rockset.

“The serverless nature of Rockset made it extremely easy to start out on,” he stated. “It took us solely a pair days to arrange our knowledge pipelines into Rockset and after that, it was fairly straight ahead. The docs had been nice.”

Resolution 1: Scale utilizing Rockset’s PostgreSQL integration

DataBrain took benefit of the native integration Rockset has with PostgreSQL. Desired datasets are immediately and mechanically synced into Rockset, which readies the info for queries in a couple of seconds. Rockset then returns question outcomes, even for advanced analytical ones, in milliseconds.

Most significantly, Rockset is horizontally scalable. Compute and storage are fully decoupled in Rockset, enabling DataBrain to cost-optimize for the specified efficiency stage. Moreover letting DataBrain keep away from doing analytics in expensive PostgreSQL, Rockset additionally allowed DataBrain to dump a big portion of its knowledge from PostgreSQL into an S3 knowledge lake, saving considerably on storage prices. And with a comparable connector for S3 (and many different sources), Rockset can mechanically keep in sync with each supply databases by studying their change streams.

Resolution 2: Ingest Dynamic, Semi-Structured Information

Rockset helps schemaless ingestion of uncooked semi-structured knowledge. The schema doesn’t must be recognized or outlined forward of time, and no clunky ETL pipelines are required. In different phrases, Rockset doesn’t require a schema however is however schema-aware, coupling the flexibleness of schemaless ingestion at write time with the flexibility to deduce the schema at learn time. That is precisely what Databrain was searching for. By adopting Rockset, DataBrain didn’t want to rent an information engineer simply to handle ETL scripts.

Resolution 3: Rockset’s Converged Index

DataBrain wanted its prospects’ semi-structured knowledge to be listed rapidly so it may question the info instantly and present insights to prospects straight away. Rockset solves this by it’s Converged Index expertise, which creates three completely different indexes — a row index, a columnar index, and inverted search index — every optimized for various entry patterns, together with key-value, time-series, doc, search, and aggregation queries.

Whereas most databases are optimized just for sure sorts of knowledge or queries, Rockset can return very quick question outcomes with out realizing prematurely the form of the info or the kind of queries. Each level lookups and combination queries might be extraordinarily quick. Rockset’s P99 latency for filter queries on terabytes of information is within the low milliseconds.

This gave DataBrain each the pace and adaptability to considerably enhance the efficiency of its service whilst its buyer base grows.

Rockset Provides DataBrain Flexibility and Pace

In abstract, DataBrain was in a position to benefit from Rockset’s out-of-box integration with PostgreSQL to dump its analytical workloads into the sooner, extra cost-efficient Rockset. Rockset’s Good Schema function was additionally crucial, permitting DataBrain to make use of real-time SQL queries to extract significant insights from uncooked semi-structured knowledge ingested with no predefined schema. Lastly, Rockset’s Converged Index permits low knowledge latency and question latency, giving DataBrain the pace to remain forward of its opponents.