Alex Merced

Artificial Intelligence (AI) has become the defining technology of the decade. From chatbots to code generators, from self-driving cars to predictive text—AI systems are everywhere. But before we dive into the cutting-edge world of large language models (LLMs), let’s rewind and understand where this all began. This post kicks off our 10-part series exploring how […]

If you’ve ever wished you could ask an AI model like Claude to interact with your local files or run custom code—good news: you can. That’s exactly what the Model Context Protocol (MCP) makes possible. In this tutorial, we’ll walk you through building a beginner-friendly MCP server that acts as a simple template for future projects. You don’t need to be […]

The story probably sounds familiar if you’ve been building data systems for a while. Your company starts with an operational database—SQL Server, PostgreSQL, MySQL, maybe even MongoDB. It serves its purpose well. Transactions are fast, applications are happy, and reports run—well, they used to run—just fine. But as the business grows, so do the data […]

Disaster recovery isn’t just for databases and monolithic data warehouses anymore. In the world of modern data lakehouses—where Apache Iceberg is increasingly used to power large-scale, multi-engine analytics—knowing how to recover from failure is just as important as knowing how to scale. Imagine this: your data platform experiences a serious outage. Maybe a cloud region […]

Apache Iceberg has quickly emerged as one of the most critical technologies in the modern data lakehouse stack. Originally designed to solve the shortcomings of Hive table formats, Iceberg brings ACID transactions, schema evolution, time travel, and hidden partitioning to data lakes—features that were once the domain of traditional data warehouses. But as the project […]

Introduction Imagine if every time you bought a new device, you had to invent a new plug to power it on. That’s precisely what working with LLMs used to feel like — every app had to create its unique way of connecting models to data, tools, and other systems. Enter the Model Context Protocol (MCP) […]

Data architectures have undergone a significant transformation over the past few decades. Enterprises once relied on rigid, centralized data warehouses to store and process information. Then came the era of data lakes, which offered more flexibility but often lacked governance and structure. Now, as organizations grapple with vast amounts of diverse data across multiple platforms, […]

Apache Polaris rapidly evolves into a robust open-source data catalog that bridges the gap between governance policies and federated data management. With its growing adoption, it has become essential to align development efforts with the needs of its users, ensuring that Polaris remains scalable, secure, and feature-rich. Over the past months, the Polaris community has […]

Managing applications in Kubernetes can be complex, requiring multiple YAML files to define resources such as Deployments, Services, ConfigMaps, and Secrets. As applications scale, manually maintaining and updating these configurations becomes cumbersome and error-prone. This is where Helm comes in. Helm is a package manager for Kubernetes that simplifies deployment by bundling application configurations into reusable, version-controlled Helm charts. With […]

AI-powered applications transform industries by enabling data-driven decision-making, automated workflows, and intelligent chat interfaces. However, one of the biggest challenges in developing these AI applications is efficiently accessing, integrating, and querying enterprise data from multiple sources. Many AI models rely on structured data stored in databases, data lakes, and warehouses—but accessing this data in real […]

AI-driven applications have revolutionized the way businesses extract value from data. AI models thrive on structured, well-organized data, from virtual assistants to predictive analytics. However, one of the biggest challenges in building AI-powered solutions is efficiently accessing and integrating data from multiple sources—data lakes, data warehouses, relational databases, and streaming platforms. This is where Dremio shines. […]

Building high-quality data models is at the core of creating a robust analytics ecosystem. However, this task often becomes a daunting challenge as organizations grow and data environments become increasingly complex. Data Engineers frequently find their efforts derailed by the need to move data into centralized data warehouses, a process that introduces inefficiencies and complications. […]

Building data applications is an exciting but complex journey. As a developer, you’re often tasked with transforming raw data into actionable insights while balancing performance, scalability, and cost. Along the way, you face a series of challenges: databases that struggle to handle both transactional and analytical workloads, ETL pipelines that become costly to maintain, and […]

The evolution of data architectures has reshaped how organizations manage, access, and derive value from their data. Traditional systems like data warehouses and databases offered centralized control, making governance relatively straightforward but often at the expense of scalability and cost. The emergence of data lakehouses has transformed this paradigm, combining the scalability and flexibility of […]

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. […]

The Output Welcome to the 2024 Football Playoffs Hackathon powered by Dremio. Teams from across the globe will apply their analytics prowess to predict:  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, […]

Dremio is a cutting-edge Lakehouse Platform designed to make data more accessible and actionable. With Apache Iceberg tables as first-class citizen, Dremio offers a powerful combination of data virtualization and unification capabilities. This means you can seamlessly combine data from databases, data warehouses, data lakes, and lakehouses into a single, governed platform. Dremio’s built-in semantic […]

Organizations face a common challenge: ensuring consistent and reliable data insights across multiple departments, tools, and teams. As data becomes increasingly central to decision-making, the need for a unified view—one everyone in the organization can rely on—has never been more critical. This is where a universal semantic layer comes into play. By creating a standardized […]

Modern enterprises are increasingly adopting data mesh architecture to keep up with demand for accessible, consistent data. Unlike traditional, centralized data models, data mesh prioritizes a decentralized approach, allowing individual teams to own and manage their own data domains. This structure enables organizations to achieve greater agility, faster access to data, and enhanced scalability. For […]

Organizations need rapid access to insights from their data to stay competitive. However, the complexity of managing data from diverse sources often slows down this process. Traditional methods like ETL (Extract, Transform, Load) are effective but can create delays due to data replication and movement. To overcome these challenges, data virtualization tools provide a robust […]

An Iceberg Data Lakehouse—a unified system that combines the scalability of data lakes with the analytical power of data warehouses—has emerged as a powerful solution to modern data requirements for performance, accessibility and costs. However, what makes this architecture effective is the strategic use of metadata to optimize performance, ensure data consistency, and enhance governance. […]

The demand for quick, actionable insights is higher than ever. Businesses are moving beyond traditional data warehouses to adopt lakehouses and other flexible data architectures that better support real-time analytics, BI, and AI applications. Dremio is at the forefront of this shift, providing a robust, high-performance hybrid lakehouse platform that enables fast, scalable analytics in […]

The rise of data lakehouses is transforming the way organizations manage, analyze, and leverage their data. Lakehouse architecture offers a flexible, scalable solution that bridges the gap between traditional data warehouses and data lakes. Apache Iceberg, an open table format designed to deliver reliable, high-performance analytics on large datasets, is at the heart of this […]