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Orb Delivers Real-Time Observability into Billing Operations
“Druid powers our core responsibility, which is keeping invoices up to date. So with all these events in Druid, we're executing the queries that customers have defined, which power their line items and the quantities on those line items. We're constantly keeping our customers' customers’ invoices up to date with the data that's in Druid.”
Kshitij Grover, CTO and Co-Founder | Orb
Summary
Orb scaled its billing platform with Kafka event streams, optimized query workloads, and saved 20–30 engineering hours/month while extending visibility into revenue reporting.Overview
Orb is on a mission to provide every business with the infrastructure to unlock their revenue. Designed specifically for organizations with complex pricing plans and business models, Orb's modern pricing platform enables companies to bill for seats, consumption, and everything in between.
By using Orb, companies can introduce transparency into the billing process, fine tune their price strategies, include real-time data in invoices, and remove errors in revenue reporting. Customers span a range of industries, such as infrastructure, developer tooling, fintech, SaaS, and AI.
Challenge
Orb initially chose PostgreSQL for its database needs. However, as its customer base and data volumes expanded, Orb encountered scalability issues with PostgreSQL, particularly concerning ingestion and query performance. Additionally, Orb required a database solution specialized in time-series data and optimized for aggregations, which PostgreSQL couldn't fully provide. They needed a database solution that offered scalability, real-time analytics, and compatibility with SQL without excessive engineering complexity.
Solution
After exploring various options including Snowflake, Clickhouse, and Apache Pinot, Orb ultimately chose Apache Druid as its database. Druid's native support for Apache Kafka ensured exactly-once ingestion for streaming data, streamlining the integration process and reducing engineering overhead.
Additionally, Druid's scalability and mature managed services provided by Imply made it the right choice for Orb's needs. Druid’s built-in compatibility with SQL simplified debugging and issue resolution for Orb's engineers, facilitating seamless integration into their workflow. By choosing to use Imply for their Druid implementation, Orb gained access to tools like Imply Clarity for monitoring and analyzing Druid operations, enhancing visibility into cluster performance and workload efficiency.
Results
By implementing Apache Druid with Imply, Orb successfully addressed its scalability and performance challenges while minimizing engineering complexity. The streamlined integration with Kafka and the scalability of Druid's infrastructure allowed Orb to enhance its billing services while maintaining focus on its core competencies.
"One of the things people want to know about billing vendors is that—as usage grows, are they going to be able to rely on us for critical infrastructure? Because we can scale Druid up pretty easily, we can say, ‘Hey if you’re a big company and you want to do this large stress test, we can provision an appropriate cluster for you as your usage grows.’ That talk track has been really positive, and people can understand that."- Kshitij Grover, CTO and co-founder, OrbWith enhanced observability and support from Imply, Orb optimized query performance and workload efficiency, saving valuable time and resources for its engineering team.
"Working with Imply support has saved us 20-30 hours a month of scaling our workload, which is pretty significant."The versatility of Druid with Imply enabled Orb to extend its use beyond invoicing to encompass financial reporting, accounting, and revenue recognition—ultimately evolving to meet their customer needs.
Orb's strategic adoption of Druid with Imply not only fulfilled several core requirements, but also positioned it for continued growth and success in the competitive landscape, reinforcing confidence in its technology choice and alignment with its long-term objectives.
Highlights
- Able to scale and handle event volume that doubles or triples every few months
- Able to support customers with hundreds of thousands to millions of events per second
- Saved up to 30 hours a month on scaling their workload
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