Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
- Sensors - Camera / Video Systems
Applicable Industries
- Buildings
- Construction & Infrastructure
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Real-Time Location System (RTLS)
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
About The Customer
Kaltura is a company that provides live, real-time and on-demand video SaaS solutions for over 1,000 customers who engage millions of viewers at home, work and school. Its virtual events products exploded in popularity during the COVID-19 pandemic. Kaltura's mission is to power any video experience for any organization, deploying a wide array of video solutions to help customers teach, learn, communicate, collaborate and entertain. The company's data engineering team supports all the company’s data needs, recently transitioning from supporting primarily the company’s cloud TV unit to serving the entire company as part of the platform division.
The Challenge
Kaltura, a company providing live, real-time and on-demand video SaaS solutions, faced the challenge of building a near real-time event pipeline. The data team was tasked with creating a new data product based on streaming events sent from users’ devices. This pipeline would need to capture events and write them directly into a data lake, detecting anomalies and notifying stakeholders of spikes in the number of events. The data engineering team, which had recently transitioned from supporting primarily the company’s cloud TV unit to serving the entire company, was also tasked with replacing the legacy infrastructure with a new data lake platform.
The Solution
Kaltura decided to deploy Databricks Lakehouse Platform and dbt to replace its legacy architecture. The company launched a proof of concept and, encouraged by its success, decided to incorporate dbt to help scale its fast-growing, cluttered data. With Databricks and dbt, Kaltura replaced its legacy data architecture, running dbt alongside Databricks to orchestrate its most complex workflows. This resulted in faster processing speeds with less need for human involvement. The transition to dbt also forced Kaltura to shift to SQL, a move that the team believed was worth it. As Kaltura’s computing needs continue to increase, the data team can easily scale up resources in Databricks.
Operational Impact
Quantitative Benefit
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