Alex Merced is Head of DevRel for Dremio, a developer, and a seasoned instructor with a rich professional background. Having worked with companies like GenEd Systems, Crossfield Digital, CampusGuard, and General Assembly.
Alex is a co-author of the O’Reilly Book “Apache Iceberg: The Definitive Guide.” With a deep understanding of the subject matter, Alex has shared his insights as a speaker at events including Data Day Texas, OSA Con, P99Conf and Data Council.
Driven by a profound passion for technology, Alex has been instrumental in disseminating his knowledge through various platforms. His tech content can be found in blogs, videos, and his podcasts, Datanation and Web Dev 101.
Moreover, Alex Merced has made contributions to the JavaScript and Python communities by developing a range of libraries. Notable examples include SencilloDB, CoquitoJS, and dremio-simple-query, among others.
A BI analyst runs the same sales dashboard every Monday morning. A data engineer can look at that query, understand the access pattern, and build a materialized view to make it fast. That model works because the query patterns are predictable and stable. An AI agent doesn’t work that way. When a business analyst asks […]
Most companies still run a separate data warehouse and a data lake. They pay twice for storage, run duplicate pipelines, and spend weeks reconciling numbers that don’t match between the two systems. The data lakehouse pattern exists to collapse that complexity into one open architecture. This guide covers the full picture: how we got to […]
This is Part 2 of a 15-part Apache Iceberg Masterclass. Part 1 covered why table formats exist. This article breaks down exactly how each format organizes its metadata. The metadata structure of a table format determines everything: how fast queries start planning, how efficiently concurrent writes are handled, how schema changes propagate, and how much overhead accumulates over […]
AI integration platforms have become a critical piece of enterprise architecture. Organizations building AI agents, automation workflows, and AI-ready data pipelines need platforms that connect data sources, enforce governance, and support the high-throughput, low-latency access patterns that AI systems demand. This guide covers 17 of the best AI integration platforms available in 2026, selection criteria […]
Apache Hadoop was the default platform for big data processing for much of the 2010s. By 2026, most organizations have moved on. The architecture that made Hadoop groundbreaking — distributed storage with MapReduce computation — has been replaced by faster, more flexible, and less operationally demanding systems. This guide covers the 11 best Hadoop alternatives […]
Enterprise data fabrics have become a central topic for data and technology leaders working to support AI, real-time analytics, and cross-cloud operations. As organizations accumulate data across cloud providers, on-premises systems, SaaS applications, and partner environments, the challenge of maintaining consistent, governed, and accessible data grows with each new source added. This guide explains what […]
The amount of data enterprises generate has grown beyond what traditional storage and processing systems can handle. Enterprise data platforms have emerged as the infrastructure layer that brings order to this complexity, enabling analytics teams and AI systems to work from a single, governed foundation. This guide covers what enterprise data platforms are, how their […]
This is Part 1 of a 15-part Apache Iceberg Masterclass. This article covers the fundamental question: what problem do table formats solve, and why does the choice between them matter? A data lake without a table format is a collection of files. It has no concept of a transaction, no mechanism to prevent two writers from […]
If you manage a modern data stack, you likely spend the majority of your time and compute budget moving data around. You pull data from an operational database, stage it in object storage, transform it, load it into a data warehouse, and finally extract it into BI extracts. This DIY approach creates fragile pipelines, delayed […]
Databricks built its reputation as the go-to platform for data engineering, machine learning and lakehouse analytics. But its complex pricing model, steep learning curve and heavy Spark dependency have pushed many organizations to explore Databricks competitors that offer simpler operations, lower costs, or stronger SQL analytics. This guide covers 19 Databricks alternatives across data lakehouses, […]
Snowflake changed cloud data warehousing with its separate compute-and-storage architecture and multi-cloud support. But rising costs, vendor lock-in concerns and the shift toward open data formats have pushed many organizations to look at Snowflake competitors that offer better pricing, open source foundations, or both. This guide covers 19 alternatives to Snowflake across cloud data warehouses, […]
The term “semantic layer” has been part of the data industry’s vocabulary for over 35 years. It first appeared in a 1991 patent filing by Business Objects, and it has since been reinvented, abandoned, and reinvented again across three distinct eras of data architecture. Today, it sits at the center of one of the most […]
The conventional wisdom for data platform modernization goes like this: pick a target system, build ETL pipelines for every source, migrate everything, validate the data, retrain your users, and then start getting value. That process takes six to eighteen months. During that time, analysts are waiting and leadership is asking why the investment has not […]
Every major data platform now lists Apache Iceberg somewhere on its feature page. Snowflake has Iceberg Tables. Databricks has UniForm. BigQuery has BigLake. This is a genuinely good thing for the ecosystem because it gives users more choice and more portability. But “supports Iceberg” and “Iceberg native” are not the same thing. The distinction matters […]
Most data teams spend months assembling a lakehouse from separate components: a catalog here, an ETL pipeline there, a query engine bolted on top. Then they repeat the process when leadership asks for “AI-powered analytics.” The result is a stack of loosely connected tools, each with its own governance model, its own failure modes, and […]
Every data warehouse, every database, every analytics platform is built from the same four components: storage, a table format, a catalog, and a query engine. Traditional systems bundle all four into a single proprietary product. You get convenience, but you also get lock-in, data silos, and a compounding infrastructure bill. The data lakehouse takes a […]
This is the final post in a four-part series on building a true agentic analytics platform. Part 1 introduced the three pillars; Part 2 covered data unification; Part 3 covered data meaning and the semantic layer. This post focuses on the third pillar: governed agentic access. You’ve unified your data across sources, and you’ve built a semantic layer that gives agents […]
This is Part 3 of a four-part series on building a true agentic analytics platform. Part 1 introduced the three pillars; Part 2 covered data unification through federation and the lakehouse. This post focuses on the second pillar: data meaning. The agent has access to all your data. It can query across your data lake, your warehouse, and a […]
This is Part 2 of a four-part series on building a true agentic analytics platform. Part 1 introduced the three pillars: data unification, data meaning, and governed agentic access. This post goes deep on the first one. Here’s a problem that sounds mundane but is actually the root cause of most agentic analytics failures: AI agents can […]
Most discussions about AI in the enterprise focus on the models themselves, which LLM is fastest, which one is cheapest, which one writes the best SQL. That focus misses the harder problem. Models are getting capable fast. The bottleneck isn’t intelligence. It’s access. Agentic analytics is the discipline of connecting AI agents to enterprise data […]
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 […]
Databricks bills you twice for every workload: Databricks Units for compute, plus the cloud VM costs underneath. Interactive compute runs $0.40/DBU. SQL Warehouses run $0.22/DBU. And the cloud infrastructure bill on top can match or exceed the Databricks charges. Most teams don’t realize how much of that spend is coming from a single category: analysts […]
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 […]
