Mark Shainman is a Principal Product Marketing Manager for Dremio. He has spent more than 20-years working in both the analytics as well as privacy, governance, and security space. He has worked with numerous data products and on numerous initiatives, including database migrations, data warehousing, big data, SQL on Hadoop, data lakes, federated query access, data cataloging, privacy compliance and, now, the data lakehouse.
Data pipelines used to require a lot of infrastructure to keep running: separate compute for transformation, staging layers between systems, and a growing stack of tools to manage it all. Dremio changes the equation. With native ingestion, flexible transformation, and AI-assisted pipeline development, teams can build and operate end-to-end ELT workflows directly in the lakehouse, […]
Using Claude Code to Build an Iceberg Lakehouse For years, building a production-grade data lakehouse required a specialized team: data engineers to design pipelines and to tune queries, and platform architects to manage table maintenance. Apache Iceberg changed the storage and table format equation, giving teams an open, vendor-neutral foundation for any scale of data. […]
For years, the promise of the open lakehouse was simple: store your data once, query it with any tool, and never get locked into a single vendor’s ecosystem. Apache Iceberg made that promise real. It became the industry-standard table format because it worked, it was open, and it kept getting better. Iceberg version 3 (V3) […]
The data warehouse is not a product. It never was. It is an architectural intent, a set of goals that organizations have pursued for nearly four decades. And like every architectural intent, the technology that delivers on it must evolve as the demands of the era change. For a generation of data leaders, the data […]
Every time you move data between a lake, a warehouse, and a downstream application, you multiply your security risk. Leaving data in place is the safest approach, but historically, accessing it directly meant sacrificing performance and governance. The agentic lakehouse pattern changes that by allowing high-speed, governed analytics directly on your storage. Dremio Cloud is […]
Most AI assistants fail at data analysis because they lack business context. You can build the most complex retrieval pipelines imaginable, but without a semantic layer and direct access to your data, large language models guess metrics and hallucinate schemas. Today, we fix that. You can now connect Claude to Dremio as your dedicated data […]
Today we’re making it super simple to use Claude, Claude Cowork, and Claude Code with Dremio. The new Claude connector for Dremio Cloud lets anyone instantly connect Anthropic’s Claude to their lakehouse. Open Claude, connect to Dremio, and start asking questions in plain English. That’s it. No custom integrations, no middleware, no project setup. When […]
Most enterprise AI projects fail at the same place. Not the model. Not the infrastructure. The data. Specifically: the AI doesn’t understand the data well enough to act on it accurately, and it can’t reach most of the data anyway. These two problems compound each other. A model that can only see 30% of your […]
Apache Polaris provides the open foundation, but enterprises running enterprise-wide, AI-driven platforms need more than a standards-compliant catalog service. They need enterprise governance, automation, and business context that AI systems can reason over. As original co-creator of Apache Polaris and one of the project’s largest contributors, Dremio uses Polaris as the foundation of Dremio’s Open […]
Apache Polaris is now a Top-Level Project at the Apache Software Foundation. For anyone building on Apache Iceberg, this is one of the most important catalog milestones since the REST Catalog spec itself. Graduation means Polaris has cleared Apache’s bar for contributor diversity, governance maturity, and long-term sustainability. It’s no longer incubating. It’s production-grade open […]
Enabling Guided, Intelligent Lakehouse Construction and Management Overview Modern lakehouse platforms have historically required significant technical expertise to operate effectively. Connecting data sources, modeling clean layers, applying governance semantics, creating views, and troubleshooting query failures have been tasks reserved for skilled data engineers. The learning curve has been a meaningful adoption barrier, particularly for organizations […]
Dremio is a commercial platform, and we’re straightforward about that. But the standards and projects that power it are genuinely open, and Dremio has been an active contributor to building them, not just consuming them. Apache Arrow, Apache Iceberg, and Apache Polaris all have Dremio fingerprints on their design, specification, and governance. That work matters […]
The data lakehouse market has moved past the “is this real?” phase. According to Forrester’s The Data Lakehouses Landscape, Q1 2026, lakehouses are now the default architectural choice for modern analytics and AI workloads, driven by the need to simplify data architectures, control costs, and support modern analytics and AI workloads. For technology leaders navigating […]
Companies are racing to operationalize agentic AI, yet the final process of getting from data to decision is extremely difficult, requiring data integration, tuning, and governance management. With Dremio’s latest release, we remove these blockers by putting natural‑language intelligence, explainability, and self‑optimizing performance directly into the lakehouse experience. You get clarity and control without copies, […]
Key Takeaways Built for the Dremio Community Transform Studio for Dremio, is a free and open source pipeline builder and data quality tool built for the Dremio community. Whether you run Dremio Cloud or a self-hosted deployment, Transform Studio gives analysts and data engineers a visual workspace to build transformation pipelines, monitor data quality, and […]
The New Economics of Data Cloud data warehouses like Amazon Redshift were built for a world that no longer exists. In that earlier era, organizations focused primarily on structured business intelligence, static dashboards, and predictable workloads. Data was tightly controlled, compute resources were fixed, and dynamic scalability for rapidly changing workloads was not a concern. […]
Companies today are under constant pressure to deliver faster insights, support advanced analytics, and enable AI-driven innovation. Many organizations chose Amazon Redshift as their cloud data warehouse. However, as data volumes grow and workloads change, Redshift’s legacy warehouse architecture is not meeting their needs—driving many organizations to consider alternatives. Dremio’s intelligent lakehouse platform: a modern, […]
Enterprises today face increasing pressure to extract insights from data quickly while controlling spend. Yet, as data volumes explode across cloud and on-prem environments, traditional architectures often fall short—resulting in higher costs, rigid pipelines, and slower decision-making. The Dremio Intelligent Lakehouse Platform addresses these challenges by delivering faster insights and significant total cost of ownership […]
Artificial Intelligence (AI) has become essential for modern enterprises, driving innovation across industries by transforming data into actionable insights. However, AI’s success depends heavily on having consistent, high-quality data readily available for experimentation and model development. It is estimated that data scientists spend 80+% of their time on data acquisition and preparation, compared to model […]
As businesses generate increasing volumes of data, the need for efficient, flexible, and cost-effective data management solutions has never been greater. Legacy Hadoop environments, though groundbreaking when first introduced, often struggle to keep up with the demands of modern data and analytic workloads. From high costs associated with licensing to the complexity of managing Hadoop […]
Enterprises have revolutionized analytics by leveraging the cloud’s scalability and flexibility. Yet, despite the promise of the cloud, many organizations find that a cloud-only strategy doesn’t always meet their performance, cost, or governance expectations. As the complexities of multi-cloud and hybrid data environments grow, it’s time to consider a hybrid lakehouse strategy that combines the […]
Organizations are constantly pressured to unlock data insights quickly and efficiently while controlling costs. However, as businesses amass ever-increasing volumes of data across multiple environments—both on-premises and in the cloud—they encounter significant challenges with traditional data architectures. A Hybrid Iceberg Lakehouse, such as the one offered by Dremio, delivers substantial Total Cost of Ownership (TCO) […]
Artificial Intelligence (AI) has become essential for modern enterprises, driving innovation across industries by transforming data into actionable insights. However, AI’s success depends heavily on having consistent, high-quality data readily available for experimentation and model development. It is estimated that data scientists spend 80+% of their time on data acquisition and preparation, compared to model […]
Organizations are constantly seeking ways to optimize data management and analytics. The Dremio and NetApp Hybrid Iceberg Lakehouse Reference Architecture brings together Dremio’s Unified Lakehouse Platform and NetApp’s advanced data storage solutions to create a high-performance, scalable, and cost-efficient data lakehouse platform. With this solution combining NetApp’s advanced storage technologies with Dremio’s high-performance lakehouse platform, […]
Dremio’s latest version sets a new standard in the overall performance for lakehouse platforms. This release underscores Dremio’s commitment to providing the most high performance Iceberg lakehouse platform, positioning it as the market’s premier lakehouse analytics platform. Reflection Enhancements A Reflection In Dremio, is an optimized relational cache that takes advantage of the platform’s advanced […]
In today’s data-driven world, organizations are increasingly dealing with diverse data environments, encompassing cloud, multi-cloud, on-premises, and hybrid. Efficiently managing and querying data across these varied landscapes can be challenging, particularly when it comes to access control and workload management. Dremio has introduced significant improvements in query federation capabilities, simplifying data access and ensuring robust […]
Dremio continues to innovate and enhance the capabilities of Data Lakehouse environments with its latest feature, Auto Ingest Pipelines for Iceberg tables. This cutting-edge functionality for both Dremio Enterprise Software and Dremio Cloud changes the way organizations handle data ingestion from Amazon S3 into Iceberg tables in Lakehouse environments. What is Automatic Iceberg Data Ingestion? […]
