Travel & HospitalityMachine LearningPublic Cloud

NOL UNIVERSE builds unified data lakehouse with Databricks, cutting batch computing costs by 77%

NOL UNIVERSE· South KoreaDatabricks SQL · Delta Sharing · Unity Catalog

NOL UNIVERSE, a Korean travel, leisure, and culture platform operating the NOL, NOL Interpark Tours, and NOL Tickets brands, migrated approximately 2,500 Hive-based queries and its Apache Airflow/NiFi pipelines to the Databricks Platform, consolidating disparate BI tools into a single environment and giving each business domain a self-service analytics environment governed by Unity Catalog. The customer service team also used Databricks machine learning to automate categorization of CS consultation history. The migration was completed in two months, reducing time to complete batch aggregation by 66%, decreasing batch computing costs by 77%, and increasing data availability time by 27%.

Overview

NOL UNIVERSE, a Korean travel, leisure, and culture platform operating the NOL, NOL Interpark Tours, and NOL Tickets brands, migrated approximately 2,500 Hive-based queries and its Apache Airflow/NiFi pipelines to the Databricks Platform, consolidating disparate BI tools into a single environment and giving each business domain a self-service analytics environment governed by Unity Catalog. The customer service team also used Databricks machine learning to automate categorization of CS consultation history. The migration was completed in two months, reducing time to complete batch aggregation by 66%, decreasing batch computing costs by 77%, and increasing data availability time by 27%.

The challenge

NOL UNIVERSE's existing data environment relied on multiple databases (MySQL, SQL Server), Hive and Spark on Amazon EMR, and a combination of Apache Airflow and Apache NiFi for pipelines, with several separate BI tools (Redash, Tableau). As accumulated data and batch processing time grew, the separation of the data processing engine and scheduling system made operations inefficient, causing delays in tracing and responding to batch failures, and different cloud accounts and permission schemes for each service added difficulty. The data team's full responsibility for all data products created a bottleneck during peak workload.

The solution

NOL UNIVERSE adopted the Databricks Data + AI Platform, migrating approximately 2,500 existing Hive-based queries and its Apache Airflow/NiFi pipelines to Databricks, and consolidating disparate BI tools into one platform. Each business domain was given a self-service analytics environment governed by Unity Catalog, with SSO integration unifying user authentication. The customer service team used Databricks machine learning to automate categorization of CS consultation history. The migration ran as a proof of concept in August 2024, followed by an intensive transition and validation process from September through November, completing in two months.

Machine Learning

Reported business value

The migration reduced time to complete batch aggregation by 66%, decreased batch computing costs by 77%, and increased data availability time by 27%. The customer service categorization task that used to take over a day can now be completed in less than two hours.

Sources

Open any source and check the claim yourself — that is the point of the register.

This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)

Related entries

Other travel & hospitality entries in the register.

All entries
Travel & HospitalityPredictive AnalyticsPublic Cloud

Skyscanner Scales Smarter Travel Search with the Databricks Data + AI Platform

Skyscanner, which handles 35 million searches and tens of billions of events daily, built bronze, silver and gold data layers on the Databricks Data + AI Platform to create shared Cornerstone Data Products powering multiple use cases, including a flight ranking model serving 180 million predictions daily. Unity Catalog provides governance, and the company is adopting emerging agentic AI capabilities to experiment safely and surface more relevant search results for travelers.

96/100HighPrimary source
SkyscannerUnity Catalog
Travel & HospitalityGenerative AIPublic Cloud

JetBlue Accelerates Innovation With AI

JetBlue uses the Databricks Data + AI Platform to unify flight, aircraft, and customer data, powering its operational digital twin, Blue Sky. By streaming half a terabyte of data per flight, JetBlue gains real-time insights that enhance safety, efficiency, and customer experience, with generative AI allowing teams to query data in natural language.

96/100HighPrimary source
JetBlueDatabricks
Travel & HospitalityRecommendation & PersonalizationUnknown

Wyndham Hotels & Resorts builds Agentic Enterprise with Salesforce Data 360 and Agentforce

Wyndham Hotels & Resorts, which supports approximately 8,400 properties, used Salesforce Data 360, MuleSoft, Informatica, and Agentforce Service to create Wyndham Reservations Guest 360, a unified real-time guest record giving service reps access to reservation history, loyalty status, preferences, and sentiment plus AI-powered recommendations. Wyndham also launched agentic self-service support for franchise owners through the Wyndham Community online owner portal. Results included a 5% increase in Wyndham Rewards enrollment, 21% faster service conversations year-over-year, 20% autonomous resolution of franchisee inquiries, and a 25% reduction in contact center call volume.

92/100HighPrimary source
Wyndham Hotels & ResortsSalesforce Data 360 · MuleSoft · Informatica +1
Travel & HospitalityGenerative AIPublic Cloud

VisitBritain Unlocks Tourism Insights

VisitBritain leverages Databricks Data Intelligence and GenAI to unlock instant tourism sentiment insights and facilitate real-time data collaboration for thousands of responses, making decision-making simpler and more accessible for non-technical staff across the organization, with robust governance driving smarter strategies for Great Britain's travel sector.

96/100HighPrimary source
VisitBritainDatabricks

Was this helpful?

Your feedback helps us improve our use case database