Delta Lake
15 use cases using this technology
Operationalizing Analytics Data at Scale (DEICHMANN SE)
DEICHMANN SE
DEICHMANN SE, Europe's largest footwear retailer with roughly 4,700 stores in more than 30 countries and about 8.7 billion euros in 2024 revenue, adopted Databricks Lakebase, a managed PostgreSQL database integrated with its Databricks Lakehouse, to activate governed customer and product data for e-commerce marketing without building separate integration pipelines. Lakebase reduced a data-product publishing pipeline to 3 clicks and about 30 seconds, and is now in production across Europe feeding SAP Emarsys for e-commerce marketing.
Coop Reaches 30% Retention with "AskCap", a Databricks Genie Assistant Born From a Hackathon
Coop
Retail cooperative Coop turned an internal hackathon prototype into AskCap, an AI assistant built on Databricks Genie and governed through Unity Catalog, that lets employees query enterprise data in natural language directly inside Microsoft Teams. Category managers use it for point lookups such as checking market share of specific products, and it has reached a 30% retention rate among internal users interacting with it.
Plenitude builds machine learning models on Databricks to forecast energy demand and renewable production
Plenitude
Eni-owned energy company Plenitude, which serves 10 million households and businesses across Europe, uses statistical models and machine learning on the Databricks Data + AI Platform to forecast customer energy consumption at hourly and daily granularity, forecast wind and solar generation from its renewable asset portfolio, and run customer segmentation and propensity models across 60 implemented use cases.
Maya is simplifying banking for diverse populations
Maya
Maya, the Philippines' leading fintech company, partnered with Databricks to modernize its data architecture into a unified lakehouse on Delta Lake, centralizing 100% of its data for real-time access and AI scalability. Maya's ML-powered underwriting tools cumulatively disbursed $2.1 billion in loans by year-end 2024. AI-driven fraud detection, combining transactional sequence embedding, network analysis and predictive modeling, achieved up to a 98% reduction in fraud losses. Hyperpersonalized, AI-driven marketing campaigns increased average revenue per user (ARPU) by 45%. Using Unity Catalog for governance and automated ETL pipelines, Maya also achieved a 28% increase in storage capacity without a rise in costs.
GoGuardian: Safer schools, empowered teachers, thriving students
GoGuardian
GoGuardian, which powers safe, focused learning for half of U.S. K-12 students, migrated its ML infrastructure to Databricks to manage billions of daily inferences for web filtering, classroom management and harm prevention while maintaining a PII-free, COPPA/FERPA-compliant data environment. Using Delta Lake, Lakeflow, Unity Catalog, MLflow and Databricks Model Serving, GoGuardian achieved up to 50% reduction in machine learning operational costs, 90% operational cost savings with its Delphi website classification model, and a 62% reduction in inappropriate device use among students. AI-driven prioritization also cut the volume of records requiring human review for high-risk content by over 95%, from 1 million to 35,000-45,000.
Furniture.com Transforms Online Search with Databricks
Furniture.com
Furniture.com unifies over 60 retail partners and 1.5 million SKUs on the Databricks Data + AI Platform, using Delta Lake, MLflow and Unity Catalog to run its ML lifecycle. Its Find It AI product-discovery tool uses generative AI to create a synthetic image representing shopper intent, then matches it against the product catalog for image-based search. A Collections model uses LLMs to automatically group related products, finding more than 16,000 collections across 50+ partners with no human intervention. Users who interact with Find It AI show a click-through rate 8x higher than baseline and a return rate 3.2x higher than baseline.
Reinventing mobile banking with ML
HSBC
HSBC used Azure Databricks and Delta Lake to unify data analytics across data engineering, data science and analysts for its PayMe mobile payments app, applying NLP and machine learning for fraud detection, personalization and recommendations. Data processing for complex analytics dropped from 6 hours to 6 seconds, 14 read replica databases were consolidated into one Delta Lake, and app engagement improved 4.5x.
AT&T reduces fraud by up to 80% with the Databricks Data + AI Platform
AT&T
AT&T migrated from a legacy on-premises, rules-based fraud detection system to the Databricks Data + AI Platform, moving to a unified lakehouse architecture. AT&T now runs over 100 ML models in production that use real-time data, automatic alerts and recommendations to protect its 182 million wireless customers from fraud including robocalls, robotexts, illegal unlocks and identity theft, reducing fraud by up to 80%.
Hafnia modernizes maritime operations with Databricks Lakebase
Hafnia
Maritime tanker operator Hafnia built a 'data supermarket' on the Databricks Data + AI Platform and adopted Lakebase, a managed Postgres database natively integrated into Databricks, to run operational applications alongside analytics. This powers Hafnia's internal DNA Port operating portal and the Marvis AI copilot. Time to deliver production apps dropped from about two months to four or five days (90%+ reduction), and a 10-person data team now supports 10 departments, 200+ vessels and 4,500+ crew.
Building the foundation for the 5G revolution: Digital Nasional Berhad
Digital Nasional Berhad
Digital Nasional Berhad (DNB), Malaysia's government-established 5G network operator, used the Databricks Data + AI Platform to build a cost-effective, high-performance data platform to process 5G network data at scale, achieving 82%+ populated-area coverage in two years. Using Delta Lake with a medallion (Bronze/Silver/Gold) architecture, Databricks Workflows for pipeline orchestration, serverless compute, Notebooks for team collaboration, Unity Catalog for governance, and MLflow for ML lifecycle management, DNB achieved 70% cost optimization and a 60-70% increase in data pipeline performance, and has begun building AI-powered chatbots giving network engineers real-time access to performance insights with geospatial visualizations.
TK Elevator: Moving Beyond with Data + AI Driven Service Intelligence
TK Elevator
TK Elevator (TKE), one of the world's largest providers of elevators, escalators and vertical transportation systems, adopted the Databricks Platform to unify and process more than 500 million IoT telemetry events daily from connected elevators across 30+ countries, moving from reactive to predictive maintenance. TKE's MAX platform generates predictive insights that feed actionable guidance to field technicians via its Digital Operations Center (DOC), which generates 30,000-40,000 tickets annually and has reduced technician troubleshooting time on call-backs and unplanned shutdowns by up to 20%. The platform scaled from 3 to over 40 markets (10x expansion), cut data pipeline delivery from months to weeks, and reduced data sharing time with internal/external consumers from three months to three days (90% reduction). TKE uses Delta Lake, Delta Sharing, Lakeflow, Unity Catalog, Databricks Assistant, MLflow, AI Gateway, Vector Search and Model Serving, and is exploring agentic AI to combine technical, service and commercial context in real time.
Techcombank: Ushering Personalized Banking for Millions of Customers
Techcombank
Techcombank, Vietnam's largest private financial institution with 315 branches serving over 15.3 million customers, adopted the Databricks Data + AI Platform to unify data from disparate on-premises databases and a legacy data warehouse. Central to the initiative is 'Customer Brain,' a customer-360 tool centralizing customer data for targeted marketing, and the Lead Allocation Curated Engine (LACE), which uses AI insights to prioritize and assign sales leads. Techcombank built machine learning models for fraud detection and credit risk management, using an enterprise feature store with over 7,500 features and more than 100 ML risk models to manage a credit lifecycle handling a twentyfold increase in retail credit applications. MLOps on Databricks with MLflow cut model implementation from months to weeks. The bank is developing an internal RAG-based chatbot, Smartie, built on Databricks AI Search, and piloting AI/BI Genie for natural-language data queries. The platform has over 1,000 active users bank-wide.
From high costs to near real-time insights with Zerobus Ingest
Beusa Energy
Beusa Energy, a 30-year energy sector company spanning electric hydraulic fracturing, mobile power generation, electrical distribution, field gas processing and industrial manufacturing, migrated its high-frequency telemetry ingestion from a custom SQL Statement API pipeline to Databricks' Zerobus Ingest direct-write API. The migration required only swapping the write path (same .NET worker, same MQTT subscriptions), cutting ingestion cost from roughly 689 DBU per GB to about 0.29 DBU per GB, a 99% cost reduction, while eliminating the need for a separate streaming broker tier. Today more than 6,000 devices stream telemetry at 1 Hz across about 250 remote assets, ingesting ~22 million rows daily into a unified lakehouse with end-to-end latency of about three seconds, governed by Unity Catalog. Beusa is now using this high-frequency telemetry, combined with maintenance history, to train predictive-maintenance models forecasting remaining useful life of assets, moving from condition-based to predictive and eventually prescriptive maintenance.
Lippert Improves Customer Support with GenAI
Lippert
Lippert, a $3.8 billion global manufacturer of components for RV, marine and automotive brands, used Databricks and Agent Bricks to consolidate fragmented data into a single lakehouse and deploy AI agents for customer support. An AI assistant trained on product manuals, technical case history and field-expert videos surfaces troubleshooting guidance for call center agents in real time, cutting new-agent ramp time from six months to four weeks (an 85% reduction). Using Agent Bricks' evaluation framework and synthetic data, model accuracy for the support agent improved from 33% to 84% within weeks. AI also analyzes thousands of support calls daily for coaching and quality scoring (versus a prior manual sample of 100 calls/month) and automates call summarization into Salesforce. The initiative is expected to save millions per year and reclaim hundreds of thousands of hours, with new agents planned for HR, warranty and supply chain.
DXC Technology cuts platform TCO 30% and builds 11 AI agents using Databricks Data + AI Platform
DXC Technology
DXC Technology, a global IT services company with fragmented systems from years of mergers, used the Databricks Data + AI Platform (Delta Lake, Unity Catalog, MLflow 3.0, and the Genie conversational assistant) to unify data across sales, HR, finance and project management. DXC built 11 AI agents (3 in production, 8 in pilot or development), reduced time-to-insight from months to days, and validated a 30% reduction in platform total cost of ownership.