Latest news with #Lakebase
Time of India
2 days ago
- Business
- Time of India
India's digital businesses are innovating faster with data & AI, says Databricks founder
India's digital-native businesses are artificial intelligence (AI)-hungry and ahead of the curve from global peers when it comes to innovation with data and AI , said Ali Ghodsi, founder and chief executive of Databricks . 'India's great because when the rest of the world is talking about recession, India is on the upswing. And in the last decade, they've built a lot of digital infrastructure in India, which is a game-changer. India's ahead on digital infrastructure compared to most other countries in the world,' Ghodsi said while addressing the media at the Databricks Data + AI Summit in San Francisco on Wednesday. The Silicon Valley's data and AI company Databricks recently committed a $250 million investment in India over the next three years towards local R&D, talent development, and enterprise adoption of AI. "We're doubling down on Bangalore. We hired a huge engineering team. We target the IITs," he said, mentioning an instance where the company received 700 applications from IIT graduates for just four open positions in Bangalore. Ghodsi said that the company is extremely bullish on Asian markets, including India, South Korea, Australia and New Zealand, which are moving faster than the rest of the world on AI because of the relaxed regulatory environment. 'We're investing ahead of the game there. We're not just looking at how much revenue we get? Is the ROI there? Instead. We're saying, let's put even more there than the numbers justify, because we're so bullish on what's happening in Asia,' he said. At the annual conference on Wednesday, Databricks made a slew of bold announcements challenging traditional players in database management, AI apps and agents. Here's a rundown of key announcements: Agent bricks Taking a fresh approach to agentic AI, Databricks is focusing on the quality and cost of productising agents with 'Agent Bricks', an offering that directly challenges Salesforce's Agent Force and Google's Agent Space. 'There are a lot of challenges in the industry around building agents. We can't evaluate the quality of the agents. We don't know how these agents are doing in production,' Ghodsi said, adding that there are no evaluations or benchmarks for judging the performance of agents. Hence, Databricks is introducing LLM judges for automated evaluations. Agent Bricks' auto optimisation techniques, such as knowledge extraction and multi-agent supervisor can refine the agent for the best quality output, sometimes at 10 times lower cost. Lakebase Challenging the traditional database platforms like Oracle Database, MySQL, Microsoft SQL Server, and PostgreSQL, Databricks announced Lakebase, a first-of-its-kind fully-managed Postgres database built for AI. 'We think that's going to disrupt the existing database market, which has really not changed much in 40 years. But I think now is the time where it's actually under a lot of pressure with agents coming in,' Ghodsi said, adding that the company is targeting a $100 billion total addressable market with Lakebase. Databricks, last month, announced the acquisition of Neon, a leading serverless Postgres company, which showed that over 30% of the databases at Neon were actually created by agents, not by database administrators. 'So next year, it's probably 99% plus.' Therefore, in the new AI era, enterprises need different types of databases where compute and storage are completely separated, he explained. 'You just store the database on very cheap cloud storage in an open format so you're not locked into anyone (single vendor).' Over 300 Databricks customers are already using Lakebase, and this transition is going to be the most important marathon for the next five years, he said. Databricks free edition To close the AI talent gap, Databricks also announced the free edition of its platform, along with a $100 million global investment in data and AI education. This initiative gives students, professionals, and institutions free access to Databricks tools and training. Among other notable announcements made was the Lakeflow Designer, a new no-code capability that lets non-technical users create data pipelines using a visual drag-and-drop interface and a natural language GenAI assistant. (The reporter was in San Francisco at the invitation of Databricks)
Yahoo
2 days ago
- Business
- Yahoo
Striim Announces Neon Serverless Postgres Support to Broaden Agentic AI Use Cases with Databricks
PALO ALTO, Calif., June 12, 2025 (GLOBE NEWSWIRE) -- Applications in the AI era depend on real-time data, but data ingestion and integration from legacy architectures often hold them back. Traditional ETL pipelines introduce latency, complexity, and stale intelligence, limiting the effectiveness of LLM-driven applications and Retrieval-Augmented Generation (RAG). For enterprises building on the Postgres stack, bridging that gap between operational data and real-time AI is critical. Open-source Postgres is widely deployed as the back-end database by developers to address operational requirements. Neon builds on this foundation with a new paradigm for the creation of databases by AI agents. Most recently, Databricks announced Lakebase, based on its acquisition of Neon—a fully managed Postgres database that is a popular choice to build AI Applications on. Now, Striim is excited to announce that it is expanding its Postgres offerings with high-throughput ingestion from Neon into Databricks for real-time analytics, as well as high-speed data delivery from legacy systems into Neon for platform and data modernization. Striim's unified platform further allows vector embeddings to be built within the data pipeline while delivering real-time data into Neon and into Databricks for building Agentic AI use cases. Using Striim, developers can seamlessly migrate, integrate, or replicate transactional and event data along with in-flight vector embeddings, enriched context, and cleansed high-quality data from multiple operational stores into Neon. This modern integration allows modern agentic applications to be rapidly built with Neon as the transactional backend. With this added capability, organizations can: Seamlessly replicate operational data in real-time from traditional systems like Oracle, PostgreSQL, MySQL, SQL Server, and hundreds of other sources to Neon, with zero downtime and automated schema evolution. Enable real-time ingestion and Change Data Capture (CDC) from Neon into Databricks, ensuring AI models and analytics workloads always operate on fresh data. Fuel Retrieval-Augmented Generation (RAG) and generative AI use cases natively within Neon or Databricks with inline data enrichment and vector embeddings. Stream event data from Apache Kafka into Neon in real time, eliminating the need for brittle batch-based integrations. Maintain end-to-end data governance with in-flight AI-driven PII detection and resolution, encryption, and support for customer-managed keys. "By extending our platform to support Neon and Databricks, we're giving Postgres-native teams the tools to build real-time, AI-native architectures without rethinking their stack,' said Alok Pareek, co-founder and Executive Vice President of Engineering and Products at Striim. 'Our mission is to help customers modernize from legacy platforms and legacy ETL to real-time agent-incorporated intelligence—and Striim's Vector Agent and Neon CDC and delivery capabilities bring us one step closer to that future.' This expansion builds on Striim's momentum with Databricks, following the support for Databricks Delta Lake with open Delta table formats, and the launch of SQL2Fabric-X, which unlocks real-time SQL Server data for both Microsoft Fabric and Azure Databricks. With Neon now part of the Striim ecosystem, Postgres users can join this wave of modernization: streaming operational data to fuel AI and analytics without sacrificing performance or reliability. To learn more about Striim's support for Neon and Databricks, visit or contact our team at sales@ ABOUT STRIIM, INC. Striim pioneers real-time intelligence for AI by unifying data across clouds, applications, and databases via a fully managed, SaaS-based platform. Striim's platform, optimized for modern cloud data warehouses, transforms relational and unstructured data into AI-ready insights instantly with advanced analytics and ML frameworks, enabling swift business action. Striim leverages its expertise in real-time data integration, streaming analytics, and database replication, including industry-leading Oracle, PostgreSQL, MongoDB CDC technology, to achieve sub-second latency in processing over 100 billion daily events for ML analytics and proactive decision-making. To learn more, visit Media Contact: Dianna Spring, Vice President of Marketing at StriimPhone: (650) 241-0680 ext. 354Email: press@ Source: Striim, in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Techday NZ
3 days ago
- Business
- Techday NZ
Databricks launches Lakebase Postgres database for AI era
Databricks has launched Lakebase, a fully managed Postgres database designed specifically for artificial intelligence (AI) applications, and made it available in Public Preview. Lakebase integrates an operational database layer into Databricks' Data Intelligence Platform, with the goal of enabling developers and enterprises to build data applications and AI agents more efficiently on a single multi-cloud environment. Purpose-built for AI workloads Operational databases, commonly known as Online Transaction Processing (OLTP) systems, are fundamental to application development across industries. The market for these databases is estimated at over USD $100 billion. However, many OLTP systems are based on architectures developed decades ago, which makes them challenging to manage, inflexible, and expensive. The current shift towards AI-driven applications has introduced new technical requirements, including the need for real-time data handling and scalable architecture that supports AI workloads at speed and scale. Lakebase, which leverages Neon technology, delivers operational data to the lakehouse architecture — combining low-cost data storage with computing resources that automatically scale to meet workload requirements. This design allows for the convergence of operational and analytical systems, reducing latency for AI processes and offering enterprises current data for real-time decision-making. "We've spent the past few years helping enterprises build AI apps and agents that can reason on their proprietary data with the Databricks Data Intelligence Platform," said Ali Ghodsi, Co-founder and CEO of Databricks. "Now, with Lakebase, we're creating a new category in the database market: a modern Postgres database, deeply integrated with the lakehouse and today's development stacks. As AI agents reshape how businesses operate, Fortune 500 companies are ready to replace outdated systems. With Lakebase, we're giving them a database built for the demands of the AI era." Key features Lakebase separates compute and storage, supporting independent scaling for diverse workloads. Its cloud-native architecture offers low latency (under 10 milliseconds), high concurrency (over 10,000 queries per second), and is designed for high-availability transactional operations. The service is built on Postgres, an open source database engine widely used by developers and supported by a rich ecosystem. For AI workloads, Lakebase launches in under a second and operates on a consumption-based payment model, so users only pay for the resources they use. Branching capabilities allow developers to create copy-on-write database clones, supporting safe testing and experimentation by both humans and AI agents. Lakebase automatically syncs data with lakehouse tables and provides an online feature store for machine learning model serving. It also integrates with other Databricks services, including Databricks Apps and Unity Catalog. The database is managed entirely by Databricks, with features such as encrypted data at rest, high availability, point-in-time recovery, and enterprise-grade compliance and security. Market adoption and customer perspectives According to the company, hundreds of enterprises participated in the Private Preview stage of Lakebase. Potential applications for the technology span sectors, from personalised product recommendations in retail to clinical trial workflow management in healthcare. Jelle Van Etten, Head of Global Data Platform at Heineken, commented: "At Heineken, our goal is to become the best-connected brewer. To do that, we needed a way to unify all of our datasets to accelerate the path from data to value. Databricks has long been our foundation for analytics, creating insights such as product recommendations and supply chain enhancements. Our analytical data platform is now evolving to be an operational AI data platform and needs to deliver those insights to applications at low latency." Anjan Kundavaram, Chief Product Officer at Fivetran, said: "Lakebase removes the operational burden of managing transactional databases. Our customers can focus on building applications instead of worrying about provisioning, tuning and scaling." David Menninger, Executive Director at ISG Software Research, said: "Our research shows that the data and insights from analytical processes are the most critical data to enterprises' success. In order to act on that information, they must be able to incorporate it into operational processes via their business applications. These two worlds are no longer separate. By offering a Postgres-compatible, lakehouse-integrated system designed specifically for AI-native and analytical workloads, Databricks is giving customers a unified, developer-friendly stack that reduces complexity and accelerates innovation. This combination will help enterprises maximise the value they derive across their entire data estate — from storage to AI-enabled application deployment." Integration and partner network Lakebase is launching with support from a network of partners, including technology vendors and system integrators such as Accenture, Deloitte, Cloudflare, Informatica, Qlik, and Redis, among others. These partnerships are designed to ease data integration, enhance business intelligence, and support governance for customers as they adopt Lakebase as part of their operational infrastructure. Lakebase is now available in Public Preview with further enhancements planned in the coming months. Customers can access the preview directly through their Databricks workspace.
Cision Canada
4 days ago
- Business
- Cision Canada
Databricks Launches Lakebase: a New Class of Operational Database for AI Apps and Agents
Fully-managed, Postgres database built to unify analytics and operations for intelligent apps SAN FRANCISCO, June 11, 2025 /CNW/ -- Data + AI Summit-- Databricks, the Data and AI company, today announced the launch of Lakebase, a first-of-its-kind fully-managed Postgres database built for AI. With Lakebase, Databricks adds an operational database layer to the company's Data Intelligence Platform. Now, developers and enterprises can build data applications and AI agents faster and more easily on a single multi-cloud platform. Lakebase is now available in Public Preview. Operational databases (OLTP) are a $100-billion-plus market that underpin every application. However, they are based on decades-old architecture designed for slowly changing apps, making them difficult to manage, expensive and prone to vendor lock-in. AI is introducing a new set of requirements. Now, every data application, agent, recommendation and automated workflow needs fast, reliable data at the speed and scale of AI agents. This also requires that operational and analytical systems converge to reduce latency between AI systems and to provide enterprises with current information to make real-time decisions. The new Lakebase, powered by Neon technology, brings operational data to the lakehouse (storing data in low-cost lakes) with continuous autoscaling of compute to support agent workloads and unifies operational and analytical data. Now developers can build faster, scale effortlessly and deliver the next generation of intelligent applications. "We've spent the past few years helping enterprises build AI apps and agents that can reason on their proprietary data with the Databricks Data Intelligence Platform," said Ali Ghodsi, Co-founder and CEO of Databricks. "Now, with Lakebase, we're creating a new category in the database market: a modern Postgres database, deeply integrated with the lakehouse and today's development stacks. As AI agents reshape how businesses operate, Fortune 500 companies are ready to replace outdated systems. With Lakebase, we're giving them a database built for the demands of the AI era." Lakebase: The Operational Database for the AI Era Lakebase is designed for the next era of application development and is intended to enable developers and AI agents. Key benefits of Lakebase include: Separated compute and storage: Lakebase is built on Neon technology, with separated compute and storage for independent scaling. Its cloud-native architecture supports low latency (<10 ms), high concurrency (>10K QPS) and high availability transactional needs. Built on open source: Postgres is widely used by developers and has seen rapid adoption over the last few years. The familiar, open source engine has a rich ecosystem of community extensions and partners and is ideal for workflows built on agents, as all frontier LLMs have been trained on the vast amount of information on the database system. Built for AI: Launch in under a second, and only pay for what you use. Additionally, Lakebase's unique branching capability enables low-risk development by creating copy-on-write database clones to assist both developer testing and agent-based development. Integrated with the lakehouse. Automatically sync data to and from lakehouse tables. Lakebase also provides an online feature store for model serving and is integrated with Databricks Apps and Unity Catalog. Enterprise ready. Fully managed by Databricks, Lakebase is based on hardened compute infrastructure and encrypted data at rest, supports high availability, point-in-time recovery and integrates with Databricks enterprise features from network security to compliance. Lakebase Momentum Digital leaders are already seeing the value of unifying operational and analytical workloads on a single platform, with hundreds of enterprises already in the Private Preview. Lakebase can be used across industries to serve personalized product recommendations, create shopping experiences powered by agents, manage clinical trial workflows and more. "At Heineken, our goal is to become the best-connected brewer. To do that, we needed a way to unify all of our datasets to accelerate the path from data to value. Databricks has long been our foundation for analytics, creating insights such as product recommendations and supply chain enhancements. Our analytical data platform is now evolving to be an operational AI data platform and needs to deliver those insights to applications at low latency." — Jelle Van Etten, Head of Global Data Platform at Heineken. "Lakebase removes the operational burden of managing transactional databases. Our customers can focus on building applications instead of worrying about provisioning, tuning and scaling." — Anjan Kundavaram, Chief Product Officer at Fivetran. "Our research shows that the data and insights from analytical processes are the most critical data to enterprises' success. In order to act on that information, they must be able to incorporate it into operational processes via their business applications. These two worlds are no longer separate. By offering a Postgres-compatible, lakehouse-integrated system designed specifically for AI-native and analytical workloads, Databricks is giving customers a unified, developer-friendly stack that reduces complexity and accelerates innovation. This combination will help enterprises maximize the value they derive across their entire data estate — from storage to AI-enabled application deployment." — David Menninger, Executive Director, ISG Software Research. Partner Ecosystem A strong partner network helps Lakebase customers work with their System Integrators and use existing enterprise tools for data integration, business intelligence, and governance. We're excited to have the following launch partners on board: Accenture, Airbyte, Alation, Anomalo, Atlan, Boomi, Cdata, Celebal Technologies, Cloudflare, Collibra, Confluent, Dataiku, dbt Labs, Deloitte, EPAM, Fivetran, Hightouch, Immuta, Informatica, Lovable, Monte Carlo, Omni, Posit, Qlik, Redis, Retool, Sigma, Snowplow, Spotfire, Striim, Superblocks, ThoughtSpot and Tredence. Availability Lakebase is available in Public Preview starting today, with additional, significant improvements planned over the coming months. Customers can enable the preview directly from their Databricks workspace or visit this page to learn more. About Databricks Databricks is the Data and AI company. More than 15,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, MLflow, and Unity Catalog. To learn more, follow Databricks on X, LinkedIn and Facebook.
