Aravo Solutions Case Studies Kaltura's Transformation: Powering Limitless Video Experiences with Databricks and dbt

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	Kaltura's Transformation: Powering Limitless Video Experiences with Databricks and dbt Aravo Solutions

Kaltura's Transformation: Powering Limitless Video Experiences with Databricks and dbt

Aravo Solutions

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The migration to a lakehouse architecture has brought significant benefits to Kaltura. The company has streamlined and enhanced its workflows, resulting in faster processing speeds and fewer man-hours spent. The new architecture has also improved transparency about the processes it is running and has empowered users to investigate and debug these processes themselves. Dozens of employees now access the data platform regularly. The company is also using its lakehouse architecture to support a major new product feature — deeper segmentation of the company’s users and their activities for future learning and feature development. Kaltura plans to deploy Databricks and dbt for additional efficiency and enhancement use cases as well.

Impact #1

The migration to a lakehouse architecture has brought significant benefits to Kaltura. The company has streamlined and enhanced its workflows, resulting in faster processing speeds and fewer man-hours spent. The new architecture has also improved transparency about the processes it is running and has empowered users to investigate and debug these processes themselves. Dozens of employees now access the data platform regularly. The company is also using its lakehouse architecture to support a major new product feature — deeper segmentation of the company’s users and their activities for future learning and feature development. Kaltura plans to deploy Databricks and dbt for additional efficiency and enhancement use cases as well.

Benefit #1	20% reduction in infrastructure costs
Benefit #2	92% reduction in latency for support engineers to get data logs
Benefit #3	Fewer man-hours spent maintaining data architecture

Benefit #1

20% reduction in infrastructure costs

Benefit #2

92% reduction in latency for support engineers to get data logs

Benefit #3

Fewer man-hours spent maintaining data architecture

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Overview

Kaltura's Transformation: Powering Limitless Video Experiences with Databricks and dbt

Operational Impact

Quantitative Benefit