With the General Availability of Lakebase, Databricks introduces a production-ready operational database built for the AI era. Lakebase brings fully managed, serverless Postgres directly into the Databricks Data Intelligence Platform, unifying transactional, analytical, and AI workloads on one governed foundation.
Alongside GA, we are announcing our Lakebase launch partners. These partners worked closely with Databricks during development, validated Lakebase in real production environments, and are ready to help customers move from architecture to execution.
What this blog covers
- Why partners matter for Lakebase adoption
- How launch partners are using Lakebase
- Who is ready today to support Lakebase projects
Why partners matter for Lakebase
Lakebase changes how teams build applications and AI systems on Databricks. It eliminates the need to move data between OLTP databases and the lakehouse, simplifying the architecture and shortening delivery cycles.
Partners play a critical role in making that shift practical. They help customers:
- Build real-time applications and agentic AI workflows
- Operationalize analytics and AI without breaking governance
- Migrate legacy PostgreSQL and operational databases
These partners are not learning Lakebase. They are already using it.
How launch partners are using Lakebase
Database modernization and migrations
Partners are using Lakebase to replace external OLTP systems and eliminate fragile ETL pipelines. Operational data stays in sync with Delta tables by design, which reduces complexity and speeds up migrations.
Agentic AI and intelligent applications
Lakebase is being used as the system of record for agent state, memory, configuration, and real-time decisioning. This enables stateful, resumable AI agents and production applications without managing separate databases.
Industry accelerators and platforms
Several partners have built Lakebase-powered accelerators for healthcare, retail, financial services, and public sector use cases. These solutions combine low-latency transactions, analytics, and AI on a single governed platform.
Get started with a Lakebase partner
Lakebase is ready for production use today. Our launch partners are ready to help you modernize databases, build real-time applications, and deploy agentic AI on Databricks.
- Explore the Lakebase GA announcement
- Connect with a Lakebase launch partner
- Start building intelligent applications on the Databricks Data Intelligence Platform
Featured launch partners
Our launch partners include global consulting firms, systems integrators, and specialist data and AI companies. They have validated Lakebase across modernization, applications, and AI-driven workloads, and are actively supporting customer engagements.
A full list of global launch partners is available below.
Accenture
Databricks Lakebase is exciting because it bridges the gap between systems of record and systems of intelligence. It makes it possible to power real-time applications, AI agents, and analytics directly from the lakehouse, all on a single platform. That’s a big unlock for companies in retail, healthcare and manufacturing who are seeking real-time personalization, intelligent automation, and AI-powered applications.— Teresa Tung, Global Data Lead, Accenture
As organizations move from experimenting to running AI at scale, the challenge is now turning intelligence into action. By leveraging Databricks Lakebase, Accenture is helping clients build a new generation of AI-powered systems that operate at the decision layer—where insights directly inform actions across operations, finance, risk, and strategy.
Aimpoint Digital
Lakebase has fundamentally changed how we approach driving ROI from data while enabling analytics and functional AI for our clients. We are now able to completely leverage Databricks as an end-to-end platform. Combining OLAP and OLTP unlocks a huge amount of untapped potential. As I said in an interview at DAIS 2025, Lakebase is one of the most exciting products Databricks has launched to date.—Dylan Ford, Databricks Practice Lead, Aimpoint Digital
Aimpoint has enhanced its LLMOps framework with Lakebase to provide both short term and long-term scalable knowledge storage for GenAI applications for its clients. In addition, Lakebase and Databricks Apps are powering several of their internal applications being leveraged daily.
Atlan
Lakebase powers real-time transactional applications and AI agents at machine speed - but they need context to operate intelligently. Atlan transforms Lakebase into a context management system, unifying operational and analytical metadata so every AI agent can discover, trust, and act on governed data without friction.— Prukalpa Sankar, Founder & Co-CEO, Atlan
Atlan + Lakebase deliver context-driven intelligence for AI applications. By unifying operational, analytical, and business metadata, enterprises build governed transactional systems where AI agents operate at scale with full context - enabling faster decisions, reduced risk, and trustworthy real-time applications.
Blueprint
Lakebase gives us a governed, scalable foundation for executing complex Databricks programs. We built the Blueprint Workbench on top of Lakebase to standardize how data is accessed and managed, enabling guided, repeatable workflows that help teams move faster, reduce delivery risk, and maintain consistent quality across migrations, AI Factory builds, and governance initiatives.—Gary Nakanelua, Managing Director of Product and Innovation, Databricks MVP, Blueprint
Blueprint uses Lakebase to power the Blueprint Accelerated Data Migration accelerator, a unified execution platform that transforms Databricks migrations, AI Factory builds, and governance initiatives into guided workflows. Lakebase enables consistent delivery, faster execution, and reduced risk across enterprise-scale data and AI programs.
Capgemini
While Lakehouses already help our clients unlock value of their data, the technological silos between OLAP and OLTP have always stood in the way of a truly unified data estate vision – until now. We see Databricks Lakebase not only as an exciting platform capability, but also a potential disruption to the broader applications and agents ecosystem.—Kevin Campbell, CEO Capgemini Insights & Data
Capgemini offers Lakebase to customers within their core Data & AI portfolio - from specific scenarios in Data & AI Estate Migration & Modernization, through its role in their Agentic-ready AI & Data Platforms architecture, to industry-specific use-cases.
Celebal Technologies
Lakebase has accelerated our ability to innovate by removing friction between transactional applications and analytical insights via a serverless, compliant environment where operational data is immediately available for AI and analytics. Our Brickbuilder Accelerator Eagle Eye observability platform now delivers millisecond-latency KPI reads through a serverless OLTP engine that scales seamlessly with our analytics workloads. Similarly for CT Visa - our DW Brickbuilder Migration Solution, the native integration with Databricks Apps handles authentication and execution complexities out of the box, significantly reducing our operational overhead. This shift has empowered our teams to focus on delivering value, resulting in faster feature expansion and massive cost savings across our solutions.—Tushar Mittal, Databricks Practice Lead, Celebal Technologies
Celebal Technologies modernized their Eagle Eye and CT Visa Brickbuilder Accelerators by adopting Databricks Lakebase. This transition unified OLTP and analytical workloads, eliminating external database silos to simplify architecture. The result delivers centralized metadata management and end-to-end governance natively through Unity Catalog.
CitiusTech
Lakebase represents a major step forward in unifying operational and analytical data. With our deep Databricks expertise and purpose‑built accelerators, CitiusTech is committed to helping healthcare organizations modernize faster and more intelligently. We’re excited to partner with Databricks in shaping the next era of data‑driven care.—Sridhar Turaga, Sr Vice President, Data & AI Business, CitiusTech
CitiusTech is accelerating Lakebase adoption for interested customers with purpose‑built solutions like our Gen AI enabled OLTP Database Migration solution and FHIR Data Framework—designed to simplify conversion, standardize healthcare data models, and enable interoperable, Lakebase‑ready architectures. CitiusTech sees Lakebase as an ideal solution for replacing legacy, siloed OLTP databases eliminating complex ETL.
Cognizant
Cognizant’s Agentic Data Unification Framework, powered by Lakebase, delivers accurate, scalable, and cost-effective Data Mastering natively within the Lakehouse. Our Agentic stewards shatter silos, merging disparate sources into "Golden Records." in real time. This zero-dependency architecture fuels high-concurrency operational apps and real-time AI intent models with unparalleled precision and speed.—Diptesh Singh, Global Leader, Data & AI Management, Cognizant
Lakebase elevates the Lakehouse from insight to action. Cognizant’s Agentic Data Unification Framework leverages it to deliver real‑time, trusted Golden Records - powering AI and operations with unmatched speed, scale, and simplicity.
Collibra
As enterprises leverage Databricks to build AI agents and migrate custom applications to Lakebase, Collibra provides the foundation for AI leaders and CDOs to address governance for data, AI and business semantics. By unifying both technical and business needs while addressing compliance and policy controls across your Databricks Apps and Agent Bricks models, we help assure your data and AI is trusted, audit-ready, and secure from day one.—Mike Robertson, VP of Tech Partnerships, Collibra
Datapao
Lakebase helped us put a production-ready datastore in place extremely quickly for agent state, history, and configuration. It made real-time state persistence straightforward: every user message, agent response, and orchestration step can be saved immediately, allowing the platform to resume workflows, audit decisions, and maintain a consistent user experience without added delays. By staying fully on Databricks, we also kept the architecture simple, with a single, unified governance and security layer.—Adam Litter, Data Scientist, Datapao
Datapao built a complex multi-agent solution for Redbow Consulting Group’s pharma and healthcare strategy application on Databricks, GEM-A®. The system orchestrates dozens of specialized agents, persists agent state and conversation history, and stores reusable configurations and prompts. Databricks Lakebase served as the central operational datastore for agent memory and configuration, alongside the broader lakehouse components. Check out this blog from Datapao on how to leverage Databricks Lakebase in Generative AI Applications.
Datasentics
Lakebase has been instrumental in bridging the gap between our batch-processed Spark tables in Unity Catalog and the low-latency requirements of our production applications. It allowed us to deliver a seamless, real-time data experience without the operational overhead of traditional data movement.—Martin Gendiar, Agentic Use Case Architect, DataSentics
Datasentics leveraged Lakebase to power a real-time service desk analytics platform. By connecting Databricks Unity Catalog to interactive dashboards and GenAI assistants, they eliminated the lag between batch-processed pipelines and application layers. This ensured users access the most current, AI-enriched insights for critical operational decision-making in real-time.
Delaware Consulting
Organizations need an open platform that unifies all data capabilities required to make fast, confident decisions. Lakebase bridges a common gap between the lakehouse and operational platforms. Previously, we relied on numerous external components and bespoke data-exchange logic to support operational flows. Today, we offer a simplified yet equally flexible architecture that exposes lakehouse data to any consumer or producer. It allows us to deliver without compromise, significantly reducing both complexity and time-to-value.—Maarten Herthoge, Technical Alliance Manager, Data & AI and Databricks Champion, Delaware Consulting
Delaware’s in-house accelerators enable delivery of pre-packaged data products, API's and AI solutions focused around specific industry vertical needs. By integrating Lakebase with the Lakehouse, Delaware bridges the operational and analytical worlds. This enables end-to-end deployments that previously took months to be delivered in weeks or even days.
DXC
Databricks Lakebase enables our customers to run operational workloads, analytics, and AI on the Databricks Data Intelligence Platform. Its transactional performance, native Lakehouse integration, and unified governance simplify architecture, reduce data movement, and support scalable, AI-native and agent-driven applications with enterprise-grade security and control.—Paul Hewitt, Senior Director and Global Head of Data & AI Practice, DXC Technology
DXC modernized its data landscape by moving operational workloads from a traditional operational database to Databricks Lakebase, delivering transactional performance with native Lakehouse integration. This enabled unified OLTP and analytics, streamlined data movement, and strengthened DXC’s ability to guide customers with proven architectures for AI-ready, Lakebase-driven platforms.
Elitmind
The GA of Lakebase shows that Databricks is becoming an end-to-end platform: from analytical data to transactional workloads. For customers, this means simpler architecture, shorter time-to-value, and less ‘gluing’ between systems - while maintaining consistent governance and control.—Adrian Kukiełka, Data Platform & BI Domain Lead, Elitmind
In Elitmind projects, Lakebase will strengthen two key areas: (1) online Feature Store for financial institutions, where milliseconds and high query volumes matter, (2) operational ODS for reporting and integration processes (e.g., consolidation), where transactions, fast updates, and reliable APIs for applications are essential.
EY
Fast, reliable data access is essential for EY’s platforms supporting business solutions including EY’s managed services. With Lakebase, EY eliminates the need for custom data pipelines and separate operational databases, facilitating a more agile platform that provides faster query performance.—Raghu Jakkampudi, Managing Director, Financial Services Products & Solutions, Ernst & Young, LLP
EY's Data Fusion platform, a Databricks Brickbuilder Accelerators, provides low‑latency, transactional data services to meet the demands of EY’s client’s applications. Traditionally, this required costly data pipelines moving data to relational databases at scheduled intervals. With Lakebase, data syncs automatically on arrival, facilitating concurrent transactional reads and low‑latency processing of user‑submitted financial adjustments smoothly.
IBM / Neudesic
As organizations push toward real‑time intelligence, the convergence of analytical and operational workloads is no longer optional - it’s essential. Traditional databases weren’t built for today’s AI‑driven demands, leaving operational data siloed and underutilized. Lakebase changes the equation by running true OLTP directly on the cloud object store, delivering a unified, modern foundation for data and application innovation.—Sai Nageshwaran, Vice President, Data & AI, Neudesic, an IBM company.
Neudesic’s Payment Intelligence Platform is a Lakebase‑powered accelerator that unifies real‑time payment operations and analytics on the Databricks Data Intelligence Platform. By combining low‑latency transactional processing with Lakehouse‑scale analytics and AI, it enables end‑to‑end visibility, faster issue resolution, and intelligent insights across the payment lifecycle.
Impetus
Databricks Lakebase accelerates enterprise AI adoption by removing friction between operational data and analytics and AI initiatives. We are committed to leveraging Lakebase in conjunction with our Context Engineering assets and domain-focused Business AI solutions to help organizations bypass complex integration cycles and move directly to measurable business outcomes. This approach enables Agentic AI applications that not only deliver insight, but drive confident, intelligent action across the enterprise.—Deepak Khosla, Chief Growth Officer & Head of AI Business, Impetus Technologies
Infosys
Databricks Lakebase serves as the critical “working memory and real‑time data store” for Infosys’ Agentic AI Data Plane solution built on Databricks Data Intelligence Platform, functioning as the high-velocity operational core that closes the gap between static analysis and real-time action. Lakebase helps sustain agent’s active reasoning state, evolving hypotheses, and contextual signals with sub-10ms latency; it transforms data from static storage into an active reasoning engine—enabling fast, state‑aware, autonomous decisions essential for a high‑velocity agentic ecosystem.
Koantek
Lakebase is the missing link for true Databricks-native application development. We validated this by re-architecting our own X2D Migrations™ platform on Lakebase, and immediately codified those learnings into our Ascend AI accelerators. Now, we can deploy turnkey Lakebase templates, integrated with Databricks Apps, Databricks Asset Bundles (DABs), and Unity Catalog, allowing our clients to unify transactional and analytical workloads without leaving the platform.—Eddie Edgeworth, Chief Technology Officer, Koantek
Koantek accelerates Lakebase adoption through two vectors: their X2D Migrations™ platform, which uses Lakebase for operational telemetry, and their Ascend AI Brickbuilder accelerators. Ascend AI provides customers with deployable, governed Lakebase templates—incorporating Databricks Apps and DABs—to rapidly build modern data applications and agentic workflows on the Data Intelligence Platform.
LatentView Analytics
Managing separate stacks for analytics and application layer was always a governance nightmare. The moment you egress data to an external app database, you lose native lineage and have to rebuild security from scratch. By using Lakebase for our analytical solutions, we’ve closed that gap. Insights stay governed within the Databricks ecosystem, inheriting permissions directly from Unity Catalog. It kills the fragile syncs and keeps our audit trails unbroken.—Sunil Kalra, Head of Databricks CoE, LatentView Analytics
LatentView Analytics is implementing a Demand Forecasting and Revenue Growth Management (RGM) solution for a major CPG client where Lakebase serves as the operational engine for the business app. Traditionally, they would have to ship forecast results from their Gold tables to an external SQL database just to power the UI. In this modern architecture, the app reads directly from Lakebase, which stays natively synced with their Lakehouse. It eliminates the data movement entirely and ensures their RGM insights are always live and governed in one place.
Lingaro
Lakebase eliminates data silos by unifying OLTP and Analytical workloads in a single Lakehouse, removing the need for costly ETL pipelines. The feature that most closely resonated with our clients is that Lakebase enables near real-time synchronization between Postgres and Delta tables to make data instantly available for analytics, and this is what Lingaro enabled.—Ajay Parasuraman, Partner, Consumer Goods, Lingaro
Lingaro helped a Fortune 100 Consumer Goods company unify DC, Warehouse, and Store Inventory data on Lakebase. With AI/BI Dashboards, the teams were able to provide low-latency tracking and analytics of movement of finished goods. The team then created an interactive layer using Databricks Apps to enable seamless real-time decision-making for several use cases - including Dynamic Order Fulfilment, better Demand Forecasting, and optimizing logistics.
Lovelytics
Lakebase's transactional processing has given us the ability to provide more powerful and interactive AI-driven experiences for our clients, moving beyond just analyzing and visualizing data and allowing users to take action within their organization.—Alex Wiss, Head of Innovation Labs, Lovelytics
Lovelytics’ AI solutions, including Gridlytics Brickbuilder, demonstrate how we drive real business transformation using Lakebase and Databricks Apps. Lovelytics has also innovated across areas like metadata enrichment and data quality, delivering enhanced, AI-driven platform capabilities that improve operational efficiency and rapidly establish an AI-first foundation.
LTIMindtree
With Lakebase, we are able to bring operational and analytical planes closer than ever, eliminating friction between creating data products and activating them. Our clients can now deliver modern, AI‑driven experiences far faster - without compromising the governance, trust, or reliability required at enterprise scale.—Sriram Narasimhan, SVP & Global Head - Data & Analytics, LTIMindtree
LTIMindtree Scintilla, an approved Databricks Brickbuilder Accelerator, leverages Lakebase as the transactional backbone to deliver predictable migrations for customers. Lakebase powers job orchestration states, rule base & feature flags, and telemetry data with higher quality, lower risk, and smoother modernization at scale—thus accelerating time to value and strengthening ROI for enterprise transformation programs.
Omni
Omni customers can use Databricks Lakebase as a low-latency operational layer to power their data products. Combined with Omni’s AI and Excel functionality, end users can get both snappy response times on pre-built reports and a fast UX as they create their own analysis or ask questions in natural language. Combined with Omni’s governed semantic model, which syncs with Unity Catalog metrics, customers can build and scale apps with less engineering maintenance and performance tuning.—Arielle Strong, VP of Product, Omni
Perficient
We see tremendous opportunity in leveraging Lakebase as part of the Databricks Data Intelligence Platform. It allows application development teams to simplify their transactional database needs while enabling data teams to streamline integration for analytic and AI use cases. We are already seeing strong adoption across our internal teams, and more importantly, customers are gravitating toward Lakebase for their critical business applications and production workloads.—Michael Patterson, Vice President, AI Data & Analytics, Perficient
Perficient’s Base-Is-Loaded Accelerator bridges the OLTP/OLAP gap. This PySpark-based solution connects Lakebase directly to Delta Lake, enabling secure CRUD operations and turning Lakebase tables into micro-batch streaming sources for Lakeflow Spark Declarative Pipelines. Seamlessly ingest transactional data using Databricks-native tooling, eliminating the need for external CDC tools and maintaining an AI-friendly Lakehouse. Check out this blog on how to bridge OLTP and OLAP with Lakebase and PySpark.
Persistent
With the addition of Lakebase, Databricks has evolved into a truly complete data platform bringing together transactional workloads, analytics, AI, and applications on a single, scalable foundation with built-in security and governance. For our customers, this accelerates time-to-value and enables trusted, real-time AI and operational applications built directly on Databricks.—Sameer Dixit, Corporate Vice President - Data & AI, Persistent Systems
Persistent’s iAURA suite augments data engineering and data management on the Databricks Platform, supporting migrations, modeling, mapping, observability, and agent-driven use cases. iAURA is natively deployed on Databricks, leveraging Lakebase for operational and metadata storage, the Lakehouse for analytics, Native Apps for deployment, and Agent Bricks for agentic AI capabilities.
Polestar Analytics
Enterprises still struggle to bridge the gap between operational systems and analytics - traditional architectures force a trade-off between real-time responsiveness and analytical depth. Lakebase collapses that false choice by unifying transactional and analytical workloads on a single Databricks platform. In the same spirit, our 1Platform storage is natively built on Databricks utilizing Lakebase for master data management, storing metadata, and building small apps via App Builder. This means our entire stack benefits from Lakebase’s performance, consistency, and governance, while customers get unified data for analytics and AI without duplication or complex pipelines.—Ankit Rana, Chief Innovation Officer, Polestar Analytics
Polestar Analytics 1Platform transforms how enterprises act on data. Built natively on Databricks Lakebase, it converges operational, master, & metadata into one consistent foundation. Polestar's Agenthood AI agents execute real-time decisions on live data. Customers deploy AI-driven dashboards and automations weeks faster, without the infrastructure complexity or data fragmentation of traditional architectures.
Qubika
Lakebase was the missing piece for building production-grade AI agents on Databricks. Our Databricks Setup Accelerator needs persistent memory across sessions and workflows, and Lakebase gives us PostgreSQL OLTP with zero operational overhead.—Facundo Sentena, Senior AI Engineer, Qubika
Qubika’s Databricks Setup Accelerator uses Lakebase to store LangGraph checkpoints, conversation history, and user/workflow state for long-running, multi-step setup tasks. This enables resumable, stateful AI agents with strong multi-tenant isolation and native OAuth authentication, eliminating external databases, secrets rotation, and added DevOps overhead. Check out this video from Qubika Databricks Champion, Marco Luquer that explains how Databricks Lakebase marks a major shift in how organizations use data.
Replit
Replit Agents use Databricks Lakebase to provision databases, tune resource limits, and safely validate complex changes before they go live. This lets our AI agents work directly against real production data while keeping customer workloads safe and reliable.—Luis Héctor Chávez, Chief Technology Officer, Replit
Reply
With Lakebase, we no longer sacrifice relational rigor for lakehouse scalability. Native support for primary key & foreign key relationships will ensure that our mission-critical configuration tables remain accurate, preventing pipeline failures and streamlining our enterprise deployments while eliminating significant connection overhead.—Darshan Patel, Data Architect, Reply
Reply has migrated a large amount of mission-critical orchestration metadata from external SQL databases to Lakebase for their customer. By leveraging native primary key & foreign key constraints, this will ensure 100% referential integrity while eliminating the infrastructure overhead of cross-platform connections, achieving a true, unified Databricks Lakehouse implementation.
Retool
Our long-standing partnership with Databricks continues to deepen, and together we're reshaping who can build with AI securely. More teams across the enterprise are expanding their builder bench and turning data into production-ready, AI-powered applications and automations in minutes with Retool and Databricks Lakebase.—Abhishek Gupta, Chief Product Officer, Retool
Databricks Lakebase and Retool enable customers to democratize AI securely by eliminating the infrastructure complexity that traditionally slows teams down. Retool's integration with Lakebase means enterprises can build production-ready applications directly on their own data in minutes without the fragmented pipelines, separate databases, or compromises on governance and security.
Slalom
Lakebase was our critical integration layer connecting Databricks to our REACT app, powering real-time emergency response dashboards, AI-driven situation reports, mission prioritization, demobilization recommendations, transaction auditing, and our natural language chat agent for querying structured and unstructured disaster data.—Ramin Ostad, Principal AI Architect, Slalom
One of Slalom’s public sector customers is modernizing emergency response by using AI-powered tools like LakeSpeak, Slalom’s MCP powered Brickbuilder accelerator, to create dynamic, real-time Situation Reports. This enhances decision-making, reduces manual reporting, and offers an AI assistant for targeted data queries during disasters. LakeSpeak delivers a secure, standardized gateway to expose Databricks Genie and Lakebase to external apps, agents, and enterprise users - without duplicating logic, breaking governance, or rewriting integration patterns.
Solita
The combination of Databricks lakehouse with OLTP database by using Lakebase allows us to solve architectural problems in a fundamentally different way. We finally see a clear path away from fragile ETL pipelines and the forced separation of analytical and operational layers. The ability to serve millisecond queries to frontline workloads without maintaining separate, decoupled operational databases simplifies everything, which is exactly what is needed to scale our customers' digital services and reuse the analytical data.—Sami Lehtola, Senior Consultant in Industrial Data & Databricks Partner Manager, Solita
Solita’s Databricks-enabled accelerator unifies asset and service data into a single Lakehouse-Lakebase architecture. By bridging the gap between installed base data management, operational analytics and low-latency services - governed by Unity Catalog, OEMs and asset-heavy industries can consolidate installed base data for optimizing operations and unlocking value of new digital services.
Systech Solutions
Lakebase eliminates the need for complex modeling by enabling direct analytical queries on transactional data. It is the perfect backend for WizarD™, our agentic AI platform, allowing our AI agents to perform deep, investigative analysis instantly - right at the source and take instant business actions.—Sunil Kumar, VP, Products, Systech Solutions Inc.
Systech’s WizarD™ redefines real-time analytics by leveraging Lakebase to unify transactional and analytical workloads. By enabling AI agents to generate millisecond-level KPIs directly from raw, live data, it eliminates ETL latency - significantly accelerating time-to-value for mission-critical business insights. Systech has also integrated Lakebase into DBShift™, its migration to Databricks accelerator, creating a seamless 'lift-and-shift' path for legacy PostgreSQL workloads to move directly onto the Databricks Data Intelligence Platform.
Tata Consultancy Services
For decades, we accepted a compromise: we optimized one system for writing data and a completely different one for reading it. We built complexities just to bridge that gap. Lakebase renders that compromise obsolete. It demonstrates that the divide between operational speed and analytical depth wasn’t a law of physics- it was just a legacy constraint. We have finally unified the lifecycle of our data.—Anoop Choozhikunnathu, Global Head, Databricks Practice, TCS
TCS developed an AI/ML ROI observability solution using a React app with Lakebase as the backend. Lakebase enables real-time, conversational insights and drill-downs on ROI metrics through natural language queries and embedded dashboards to drive faster, informed decision making.
Tiger Analytics
Transitioning to Lakebase within Databricks was a strategic game-changer. By unifying metadata and analytics, we eliminated external infrastructure, driving immediate cost optimization and architectural simplicity. Tight synchronization with Delta tables has boosted performance and governance, delivering a scalable, easily maintainable foundation.—Abhishek Patel, Director, Tiger Analytics
Tiger unified their Data Marketplace by replacing Azure SQL with Lakebase, managing metadata directly within Databricks. This eliminated external infrastructure dependencies, significantly reducing costs and operational complexity. By keeping metadata tightly synchronized with Silver and Gold Delta tables, Tiger Analytics achieved superior performance and seamless governance. This native approach streamlined their architecture, delivering a scalable, efficient foundation for future growth.
Tredence
With the tight integration between Lakebase and Lakehouse, maintaining a real-time feature store is a single line of code away.—Jason Yip, Director, Databricks MVP, Tredence
At a leading network provider, there was a need to analyze customer churn in real time when customers call the support hotline. The existing solutions were batch-based. Using Databricks model serving, along with Lakebase, Tredence developed real-time feature lookup for churn detection. With the features already in the Lakehouse, syncing them to Lakebase bridges the performance gap that is required for real-time inference.
Valcon
At Valcon, we explored Lakebase with a large retail client, using Databricks pipelines to write directly to it for faster write times while gaining relational database benefits like unique keys and data model enforcement. We then used Unity Catalog sync to present these tables as catalog objects for our reporting layer. I’m genuinely impressed by the maturity, flexibility, and practical usability of Lakebase. It fits extremely well with how modern data platforms should be designed and operated.—Ivan Medrano, Senior Principal Engineer, Valcon
Wipro
Lakebase, built on open‑source Postgres and a serverless architecture, removes fragile ETL by unifying OLTP and the Lakehouse - so transactional data, analytics, and low‑latency AI agents all run from the same real‑time source.—Sandip Roy, Director & Databricks Practice Leader, Wipro
Lakebase delivers low‑latency access to client 360, portfolio, household, goals, and risk‑related data for Wipro’s Wealth AI solution—enabling real‑time, advisor‑ready insights. With this OLTP layer, AI outputs become instantaneous, supporting API-driven interactions, rapid CRUD-like operations, and event‑driven responses to client updates, transactions, and market changes. With Lakebase unified with the Lakehouse platform, WeGA for Data builds on top of it to provide a responsible, enterprise‑grade, secure, and governed data platform that ensures compliant, scalable, and reliable AI adoption.
Zeb
With Lakebase and Databricks Apps we're finally able to create a containerized interface for AI agents, marrying the viewing layer via Databricks Apps and the persistence provided via Lakebase into one unified solution.—Sid Vivek, Head of AI, zeb
Zeb’s ‘Building Data Products using Databricks Apps and Lakebase’ Brickbuilder accelerator unifies transactional and analytical workloads, embeds AI agents into business applications, and delivers governed data products quickly. Customers gain real-time decisioning, faster product delivery, and stronger governance on a single, scalable platform built on Lakebase and Databricks Apps. Check out this blog to learn more about zeb's accelerator.
Get started with a Databricks Lakebase Launch Partner
The future of data and AI is unified. By integrating operational and analytical data onto a single, governed platform with Lakebase, our customers can move beyond complex, multi-system architectures to a world where intelligence seamlessly translates into instant action. Our launch partners are ready to help you capitalize on this shift, with proven solutions and accelerators designed to leverage Lakebase’s unique advantages - including transactional performance, native Lakehouse integration, and unified governance via Unity Catalog. Connect with one of our launch partners today to begin building your next generation of real-time, AI-native applications on the Databricks Data Intelligence Platform.
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