Global bicycle leader accelerates retail analytics
Acceleration in runtime of retail analytics solution globally
Increase in daily data refreshes on Databricks
Reduction in ERP data replication, which now happens in near real-time
Trek Bicycle started in a small Wisconsin barn in 1976, but their founders always saw something bigger. Decades later, the company is on a mission to make the world a better place to live and ride. Trek only builds products they love and provides incredible hospitality to customers as they aim to change the world for the better by getting more people on bikes. Frustrated by the rising costs and slow performance of their data warehouse, Trek migrated to Databricks Data Intelligence Platform. The company now uses Qlik to replicate their ERP data to Databricks in near real-time and stores data in Delta Lake tables. With Databricks and Qlik, Trek has dramatically accelerated their retail analytics to provide a better experience for their customers with a unified view of the global business to their data consumers, including business and IT users.
Slow data processing hinders retail analytics
As Trek Bicycle works to make the world better by encouraging more people to ride bikes, the company keeps a close eye on what’s happening in their hundreds of retail stores. But until recently, running analytics on their retail data proved challenging because Trek relied on a data warehouse that couldn’t scale cost-effectively.
“The more stores we added, the more information we added to our processes and solutions,” explained Garrett Baltzer, Software Architect, Data Engineering at Trek Bicycle. “Although our data warehouse did scale to support greater data volumes, our processing costs were skyrocketing, and processes were taking far too long. Some of our solutions were taking over 30 hours to produce analytics, which is unacceptable from a business perspective.”
Adding to the challenge, Trek’s data infrastructure hindered the company’s efforts to achieve a global view of their business performance. Slow processing speeds meant Trek could only process data once per day for one region at a time.
“We were processing retail data separately for our North American, European and Asia-Pacific stores, which meant everyone downstream had to wait for actionable insights for different use cases,” recalled Advait Raje, Team Lead, Data Engineering at Trek Bicycle. “We soon made it a priority to migrate to a unified data platform that would produce analytics more quickly and at a lower cost.”
Delta Lake unifies retail data from around the globe
Seeking to modernize their data infrastructure to speed up data processing and unify all their data from global sources, Trek started migrating to Databricks Data Intelligence Platform in 2019. The company’s processing speeds immediately increased. Qlik’s integration with Databricks Data Intelligence Platform helps feed Trek’s lakehouse. This replication allows Trek to build a wide range of valuable data products for their sales and customer service teams.
“Qlik enabled us to move relevant ERP data into Databricks where we don’t have to worry about scaling vertically because it automatically scales parallel. Since 70 to 80 percent of our operational data comes from our ERP system, Qlik has made it possible to get far more out of our ERP data without increasing our costs,” Baltzer explained.
Trek is now running all their retail analytics workloads in Databricks Data Intelligence Platform. Today, Trek uses the Databricks Data Intelligence Platform to collect point-of-sale data from nearly 450 stores around the globe. All computation happens on top of Trek’s lakehouse. The company runs a semantic layer on top of this lakehouse to power everything from strategic high-level reporting for C-level executives to daily sales and operations reports for individual store employees.
“Databricks Data Intelligence Platform has been a game changer for Trek,” said Raje. “With Qlik Cloud Data Integration on Databricks, it became possible to replicate relevant ERP data to our Databricks in real time, which made it far more accessible for downstream retail analytics. Suddenly, all our data from multiple repositories was available in one place, enabling us to reduce costs and deliver on business needs much more quickly.”
Trek’s BI and data analysts leverage Databricks SQL, their serverless data warehouse, for ad hoc analysis to answer business questions much more quickly. Internal customers can leverage Power BI connecting directly to Databricks to consume retail analytics data from Gold tables. This ease of analysis helps the company monitor and enhance their Net Promoter Scores. Trek uses Structured Streaming and Auto Loader functionality within Delta Live Tables to transform the data from Bronze to Silver or Gold, according to the medallion architecture.
“Delta Live Tables have greatly accelerated our development velocity,” Raje reported. “In the past, we had to use complicated ETL processes to take data from raw to parsed. Today, we just have one simple notebook that does it, and then we use Delta Live Tables to transform the data to Silver or Gold as needed.”
Data Intelligence Platform accelerates analytics by 80 to 90 percent
By moving their data processing to Databricks Data Intelligence Platform and integrating data with Qlik, Trek has dramatically increased the speed of their processing and overall availability of data. Prior to implementing Qlik, they had a custom program that, once a week, on a Sunday, replicated Trek’s ERP data from on-premises servers to data lake using bulk copies. Using Qlik, Trek now replicates relevant data from their ERP system as Delta tables directly in their lakehouse.
“We used to work with stale ERP data all week because replication only happened on Sundays,” Raje remarked. “Now we have a nearly up-to-the-minute view of what’s going on in our business. That’s because Qlik lets us keep replicating through the day, streaming data from ERP into our lakehouse.”
Trek’s retail analytics solution used to take 48+ hours to produce meaningful results. Today, Trek runs the solution on Databricks Data Intelligence Platform to get results in six to eight hours — an 80 to 90 percent improvement, thus allowing daily runs. A complementary retail analytics solution went from 12–14 hours down to under 4–5 hours, thereby enabling the lakehouse to refresh three times per day, compared to only once a day previously.
“Before Databricks, we had to run our retail analytics once a day on North American time, which meant our other regions got their data late,” said Raje. “Now, we refresh the lakehouse three times per day, one for each region, and stakeholders receive fresh data in time to drive their decisions. Based on the results we’ve achieved in the lakehouse, we’re taking a Databricks-first approach to all our new projects. We’re even migrating many of our on-premise BI solutions to Databricks because we’re all-in on the lakehouse.”
“Databricks Data Intelligence Platform, along with data replication to Databricks using Qlik, aligns perfectly with our broader cloud-first strategy,” said Steve Novoselac, Vice President, IT and Digital at Trek Bicycle. “This demonstrates confidence in the adoption of this platform at Trek.”
