The Dremio Blog

The Apache Polaris roadmap lays out an ambitious vision for the project, balancing core functionality, governance, security, and interoperability while staying true to its open-source roots. As Polaris evolves, its flexibility, community-driven approach, and commitment to quality will ensure it meets the growing demands of modern data ecosystems.

Helm is an essential tool for Kubernetes administrators and DevOps teams looking to optimize deployment workflows. Whether you are deploying simple microservices or complex cloud-native applications, Helm provides the flexibility, automation, and reliability needed to scale efficiently.

With Dremio + LangChain + Flask/FastAPI, you can build the next generation of AI-driven applications that provide real-time, data-powered insights.

Organizations can create intelligent, real-time, data-driven applications with minimal overhead by leveraging Dremio’s ability to unify data across sources, LangChain’s agent-based AI capabilities, and Iceberg’s scalable table format.

With Dremio, enterprises can achieve a unified approach to data modeling that simplifies workflows, reduces costs, and improves collaboration across teams. The result is a streamlined, high-performing data environment where analysts and scientists can focus on delivering insights rather than untangling complexity.

Dremio also shines in unifying data across multiple sources, allowing you to query seamlessly without managing dozens of backend connections. With its semantic layer, you can define business logic once and reuse it consistently across your applications, making your backend code leaner and your APIs more reliable.

By leveraging modern tools like dbt, Great Expectations, and Dremio, organizations can implement robust governance frameworks that ensure data is accurate, secure, and accessible. These tools empower teams to enforce quality checks, manage sensitive data in compliance with regulations, secure decentralized data at multiple layers, and provide a centralized semantic layer for consistent access. At the heart of governance is transparency and trust, achieved through data lineage, metadata management, and accountability, enabling stakeholders to confidently rely on their data.

To conclude, deploying Dremio Pipes within your organisation offers significant enhancements to your data workflows. Auto-ingest Pipes provide a robust and automated solution that overcomes the limitations of traditional data ingestion methods, ensuring reliability and minimising disruptions. By streamlining ingestion processes, guaranteeing data integrity, and eliminating errors, Dremio Pipes maximise data team productivity and free up valuable resources for revenue-generating activities. Furthermore, Dremio Pipes offer effortless scalability, enabling seamless adaption to the ever-growing volumes and complexity of your data. This agility and responsiveness ensure your organisation can leverage valuable insights and make more informed decisions with unprecedented speed and efficiency.

Introduction The convergence of data and artificial intelligence (AI) continues to reshape the business landscape at an accelerated pace. Organizations are under increasing pressure to derive actionable insights from their data and leverage AI to drive competitive advantage. The 2025 State of the Data Lakehouse in the AI Era Report provides a comprehensive overview of […]

Dremio allows you to easily share and manage your data within your organisation, while dbt allows the teams working with that data to efficiently share and collaborate. Combining Dremio's powerful data lakehouse platform and dbt's robust data transformation capabilities allows your organisation to produce reliable and accessible data to drive decision making and power AI initiatives.

Hadoop modernization on AWS with Dremio represents a significant leap forward for organizations looking to leverage their data more effectively. By migrating to a cloud-native architecture, decoupling storage and compute, and enabling self-service data access, businesses can unlock the full potential of their data while minimizing costs and operational complexity.

A hybrid lakehouse strategy offers the best of both worlds—leveraging the scalability of the cloud and the control of on-premises infrastructures. By addressing the limitations of cloud-only solutions, hybrid lakehouses enable organizations to optimize costs, enhance performance, and ensure robust governance.

As 2024 comes to a close, it’s clear that this year has been remarkable for the data lakehouse and the growing momentum driving its adoption. In this blog, I’ll reflect on some of the most exciting developments in the data lakehouse space, focusing on the new possibilities unlocked by tools like Apache Iceberg and Dremio.

Business data needs are quickly evolving, and technology is adapting to keep pace. Cloud data warehouses now offer elastic storage and compute. Data lakes have evolved into lakehouses, combining lakes' flexibility with warehouses' reliability. Many organizations are utilizing a hybrid on-prem + cloud data storage strategy. Transformation tools have shifted from proprietary ETL platforms to open-source frameworks that enable software engineering practices on analytics. These technological advances are fundamentally changing how organizations work with data.

Welcome to the 2024 Football Playoffs Hackathon powered by Dremio. Teams from across the globe will apply their analytics prowess to predict:  American Champion National Champion Overall League Winner Each team must analyze current stats provided to support their selections with detailed insights.  Judging criteria will include the accuracy of predictions, the quality of analysis, the clarity of visual presentation, and the depth of insights shared.