Alex Merced

Customer 360 is the practice of unifying all customer data into a single, governed view that spans every touchpoint and system. When customer information is scattered across CRM platforms, marketing tools, support tickets, billing systems, and social channels, teams make decisions based on incomplete pictures. A customer 360 strategy consolidates this data so every department […]

Agentic analytics tools are changing how enterprises build reports and extract value from data. These platforms use AI agents that plan, execute, and adapt analytical workflows autonomously. Instead of waiting for analysts to write queries or build dashboards, agentic analytics tools continuously monitor data, detect patterns, and deliver insights directly to stakeholders. The market for […]

AI agents for analytics are transforming how enterprises interact with data. These autonomous systems go beyond traditional dashboards and copilots by independently planning, executing, and adapting complex analytical tasks. They detect anomalies, reason about root causes, orchestrate multi-step queries, and deliver insights without waiting for a human to write SQL or build a report. The […]

Every time a user opens a dashboard connected to Redshift Serverless, each chart fires a query. Even if each query runs in 3 seconds, you’re billed for 60 seconds of RPU compute per query. Eight charts on a dashboard, 50 users across the day: that’s not a rounding error on your AWS bill, it’s the […]

Snowflake’s consumption model is designed to make it easy to start and expensive to scale. Virtual warehouses bill per second with a 60-second minimum, which means every dashboard refresh pays an idle tax whether the query needed a full minute of compute or not. Features like Dynamic Tables, Automatic Clustering, and Search Optimization add charges […]

Supply chain teams operate across ERP systems, warehouse management platforms, and IoT sensor networks. When a product manager asks “Which suppliers are causing the most delivery delays, and do we have enough safety stock to cover it?”, answering requires data from all three systems. Most organizations can’t answer that question without a week of manual […]

Healthcare organizations collect patient data across Electronic Health Record (EHR) systems, insurance claims platforms, and pharmacy databases. A care coordinator trying to identify patients at risk of readmission needs data from all three. Most organizations solve this with batch ETL jobs that run overnight, meaning clinicians are always working with yesterday’s data. This tutorial shows […]

Financial institutions deal with data spread across core banking systems, market data feeds, and compliance databases. A risk analyst checking whether an account shows suspicious activity needs data from all three. Building ETL pipelines to consolidate everything into one warehouse takes months and introduces data staleness that regulators won’t accept. This tutorial shows you how […]

A unified customer view is one of the most requested analytics projects in any organization. Customer data sits in the CRM. Purchase history lives in the e-commerce database. Support tickets are stored somewhere else entirely. No single team sees the full picture. This tutorial walks you through building a complete Customer 360 view on Dremio […]

Analytics platforms with native AI integration change how organizations extract value from data. Instead of building separate ML pipelines and connecting them to BI tools manually, these platforms embed machine learning, natural language processing, and automated insight generation directly into the analytics workflow. The result is faster time to answers, broader data access for non-technical […]

Choosing between Apache Iceberg and Delta Lake affects how open, flexible, and future-proof your lakehouse architecture will be. Both table formats bring ACID transactions, schema evolution, and time travel to data lakes, but they differ in metadata design, engine compatibility, governance structure, and long-term portability. The Apache Iceberg vs Delta Lake decision shapes how your […]

A semantic layer is the bridge between raw data and business meaning. It translates complex database schemas, join relationships, and column names into terms that analysts, executives, and AI systems can understand. Without a semantic layer, every BI tool, dashboard, and AI model must redefine what “revenue” or “active customer” means, leading to conflicting numbers […]

Unified data management is the strategy of integrating, governing, and centralizing enterprise data across systems and environments. Today’s organizations face growing pressure to make sense of data spread across cloud platforms, on-premises databases, SaaS applications, and streaming pipelines. A strong unified data management strategy helps teams break through this fragmentation and turn raw data into […]

Most data platforms force you to choose: move all your data into one place and pay the storage and pipeline costs, or leave it scattered and accept slow, disconnected analytics. Dremio eliminates that tradeoff. But getting the most from it requires knowing which features to use when, and which to skip. This guide covers five […]

For many data analysts, daily reality is defined by “architectural friction.” You have a critical business question, but the answer is buried under fragmented data silos, brittle ETL pipelines, and queries that take forever to run. This friction turns promising data lakes into inefficient “data swamps,” where the cycle time between a question and an […]

For years, the term “semantic layer” has described a straightforward concept: creating a consistent, shared dictionary for canonical business datasets and metrics. This has been undeniably useful for ensuring that everyone in an organization is speaking the same language when it comes to data. However, as valuable as this has been, it is becoming insufficient […]

Many businesses hit an analytics wall as they grow. Production databases slow down under the weight of complex queries. Marketing teams wait for campaign data while order systems struggle to keep up. The traditional fix is building a data warehouse. This path is often slow and expensive. It requires moving and copying data through brittle […]

Agentic analytics is here. AI agents don’t wait for instructions. They take a question, explore the data, and find the next question before you ask it. This changes everything. It changes how people interact with data. It changes how quickly teams get answers. And it changes what the platform needs to do. Traditional tools weren’t […]

Data warehouse cost varies widely. One team pays for a small cloud footprint, another funds always-on compute, strict compliance, and high concurrency. This article breaks down the pricing models behind that spread, then shows practical ways to cut waste without losing speed or reliability. Agentic data warehouse cost models How these cost models work Consumption-based […]

On Monday morning, the CFO asks for a revenue view that splits by region and channel. The raw data sits in object storage, the clean tables live in a warehouse, and a few “temporary” extracts are in spreadsheets. A pipeline fails, the dashboard goes stale, and the meeting starts anyway. The right data lakehouse tools […]

Apache Polaris 1.3.0-incubating shipped in January 2026 and represents a meaningful step forward for teams building open lakehouse architectures. This release focuses on three practical needs that data engineers and architects consistently raise: visibility into table usage, tighter and more flexible authorization, and the ability to manage more than one table format through a single […]

(This blog covers this example; you can just create a fresh Dremio account and run this SQL to see this example in action, also see the end result in the 1 minute video below) The Data Burden: Moving Beyond the “Stale Data” Paradigm The traditional portfolio rebalancing workflow is broken. We are currently witnessing a […]

For decades, the data catalog has been a passive repository, a “phone book” for data that simply recorded where files lived and what columns they contained. These traditional, static catalogs have become the ultimate bottleneck in the age of AI, reinforcing data silos and offering zero intelligence to the consumers who need it most. We […]

As organizations embrace the modern data lakehouse, managing metadata has become a critical challenge. Apache Iceberg is rapidly becoming the standard for organizing huge analytic datasets, but its rise has created a new problem: how do you manage Iceberg tables across a growing ecosystem of different query engines and tools? The answer is a universal, […]

The Apache Iceberg data lakehouse has captured the industry’s imagination, and for good reason. It promises an open, flexible future where you aren’t locked into a single vendor’s ecosystem. But there’s a significant gap between that promise and the reality of implementation. Building a production-grade lakehouse from scratch is a massive undertaking. Data teams often […]

As your company grows, a familiar problem emerges: your production databases, which run your day-to-day operations, start to slow down under the weight of analytical queries. Your marketing team wants to analyze campaign performance, but running the query grinds the order-entry system to a halt. You’re forced to tell them to wait until after business […]

Every modern business wants to unlock the power of its data through artificial intelligence, particularly by enabling teams to ask questions in natural language and get immediate, data-backed answers. The promise is transformative: democratized analytics, faster insights, and a truly data-driven culture. However, a significant and frustrating paradox stands in the way. The reality for […]

Every business leader shares a common dream: to ask simple questions about their company’s data in plain English and get instant, accurate answers. “What was our quarterly recurring revenue in the APAC region?” or “Show me the top 10 most active customers last month.” This is the holy grail of business intelligence, the promise of […]

Dremio is a powerful data lakehouse platform designed to facilitate high-performance, self-service analytics and power AI-driven applications. It provides a unified semantic layer and seamless data access across diverse sources, making it a central hub for modern data workflows. While Dremio’s user interface is excellent for exploration and management, data engineers, data scientists, and developers […]

It’s impossible to have a conversation about data platforms today without AI coming up. The hype is real, and the promise of generative AI is transforming how we think about data interaction. While much of the initial focus has been on natural language-to-SQL chatbots, this is only scratching the surface of what’s possible. The most […]