Agents That Build Tables, Not Just Query ThemLivestream March 17
Explore the key concepts of OLAP, DuckDB, databases, and their pivotal role in data-driven applications.
Discover DuckDB, the high-performance in-process SQL OLAP database. Learn its unique columnar-vectorized architecture, see how it compares to Pandas, and understand its role next to SQLite and PostgreSQL. Get started in seconds.
What is an OLAP database? Explore clear examples and see how modern serverless tools like MotherDuck, built on DuckDB, deliver fast OLAP analytics without the complexity of traditional data warehouses.
A data application is software that collects, processes, and analyzes data to support decision-making. Learn the key components of data applications, their benefits, and real-world examples, showing how they drive efficiency and insights for businesses.
Explore and compare five popular dataframe libraries—Pandas, Polars, Dask, PySpark, and Ibis—based on performance, scalability, and ease of use. Find the best tool for tasks ranging from quick analysis to big data processing and SQL integration.
Data application architecture defines how systems collect, process, and analyze data. This guide outlines key components, patterns, and best practices for building scalable, efficient data-driven applications, along with emerging trends in the field.
Explore the core types of data warehouses, from enterprise and on-premise to modern cloud and serverless solutions. This guide compares architectures to help you choose the right design.
DuckDB vs SQLite — when to use each. SQLite is best for apps with simple reads/writes. DuckDB is built for analytics and complex queries. See benchmarks and use cases.
WebAssembly brings near-native performance to web browsers, enabling high-performance applications that were previously impossible on the web. Learn how this binary format works and when to use it in your projects.
Explore big data processing techniques, from traditional distributed systems to modern, efficient analytics. Learn how to handle large-scale data without the complexity.
DuckDB supports all common analytical functions, including the necessary GROUP BY clause in SQL. This article gives examples of using GROUP BY effectively.
What is Parquet and why use it over CSV? This guide covers columnar storage, compression, schema evolution, and when to choose Parquet vs Avro, ORC, or Delta Lake.
This post dives into the practical art of exporting data from DuckDB with structure, security, and speed in mind. You’ll get a crash course in Hive-style partitioning, learn how to use COPY TO ... PARTITION_BY effectively, and explore how DuckLake builds on these concepts with catalog-aware writes and encryption support. Packed with hands-on code examples, file format tips, and performance advice, this guide helps you turn raw data into a streamlined, query-friendly lakehouse—no quackery involved.
DuckLake: Open table format using SQL databases for metadata. Get ACID compliance, faster queries & simplified lakehouse management. MIT licensed.
Learn about ACID transactions and what it means for a database to be ACID compliant. We explain Atomicity, Consistency, Isolation, and Durability with SQL examples.
Star schema explained with SQL examples. Learn how fact and dimension tables work, when to use star vs snowflake schema, and why it's faster in columnar databases like DuckDB.
Confused about data lakehouse vs data warehouse vs data lake? Get a clear, side-by-side comparison of schema, cost, and use cases to choose the right architecture in 2026.
Columnar vs row storage: when to use each and why it matters for analytics. Covers vectorized execution, compression, Parquet format, and how DuckDB and Snowflake use columnar architecture.
Tired of expensive Snowflake and BigQuery bills? Learn how to cut cloud data warehouse costs by 70% or more using DuckDB for local work & MotherDuck's serverless platform.
Looking for the best data warehouse for your startup? See our 2026 guide comparing Snowflake, Databricks, and MotherDuck to find the right fit for your needs.
Tired of high Snowflake & BigQuery costs? Learn the principles of a lean, modern data warehouse and build a startup data stack that's 10x cheaper and faster.
Don't be surprised by a 5-figure bill. Learn the hidden costs of data warehouse TCO, including compute minimums, egress fees, and admin overhead for Snowflake, BigQuery, and Redshift.
Searching for a BigQuery alternative? See how MotherDuck's serverless platform, built on DuckDB, offers a faster, more cost-effective solution for medium data workloads.
Frustrated by slow BI dashboards? Learn the causes of dashboard latency, from architectural bottlenecks to lakehouse issues. See how MotherDuck helps startups scale.
Explore top data warehouse use cases beyond BI. Learn to build fast data warehouse dashboards, internal tools, and live apps with MotherDuck and DuckDB.
Skip complex ETL. Learn the No-ETL method for startups to query multiple raw CSV, JSON, & Parquet files directly with SQL. Get insights in minutes, not months.
Diagnose and fix slow queries by targeting the true bottlenecks: I/O, Network, & CPU. This developer's guide helps you optimize data layout & joins for faster apps.
End slow queries & high cloud costs with hybrid analytics. Learn to analyze huge local files & join them with cloud data using serverless SQL. No clusters needed.
Unlock growth with our guide to self-service analytics for startups. Learn to empower your team, use collaborative tools, and embed real-time dashboards affordably.
Your comprehensive guide to building a secure and scalable data warehouse. Master the essentials of access control, disaster recovery, cost management, and avoiding vendor lock-in.
Get a practical guide to working with a DataFrame in Pandas. Discover how to create, filter, and transform tabular data in Python, with code examples and best practices for when your data exceeds local memory.
Your Snowflake bill is high due to the 60s idle-compute tax. Learn how a hybrid analytics model with DuckDB & MotherDuck can cut costs by 70-90%. Read the guide.
DuckDB + Python quickstart: install, connect, and query CSV or Parquet files in minutes. No server required—just fast SQL in your Python environment.
Take your DuckDB and Python skills further by learning how to query Pandas DataFrames directly with SQL. This guide shows you how to integrate with Arrow and Polars and extend DuckDB by writing your own custom Python UDFs.
Dive into product analytics with MotherDuck & DuckDB. This technical guide provides ready-to-use SQL queries to calculate key metrics like MRR, retention, churn, and LTV. Turn your user data into actionable insights today.
Working with JSON or nested data in DuckDB? Learn how to use STRUCT to create, query, and flatten complex data—with practical SQL examples you can copy-paste.
Learn what a data lakehouse is, how it compares to data lakes and warehouses, and explore architecture patterns like medallion. Covers open table formats (Iceberg, Delta Lake, DuckLake) and how to build your own lakehouse.
Build customer-facing analytics into your SaaS product with MotherDuck. Learn about multi-tenant architecture, fast query performance, and when to use 3-tier vs browser-based execution.
DuckDB vs Postgres for embedded analytics: compare performance, cost, and hybrid architecture with MotherDuck. Get a clear decision framework—read now.
Outgrowing Postgres for analytics? Learn the 4 warning signs, key metrics, and best architecture paths forward. Diagnose fast and scale smarter today.
Learn what an enterprise data warehouse is, how it differs from data marts and data lakes, key architecture patterns, and practical steps to build one.
Data warehouse as a service (DWaaS) lets you run a fully managed cloud data warehouse without provisioning infrastructure. Compare providers, pricing, and features.
Outgrowing Postgres? Use this OLAP solution decision guide to compare scale-up, scale-out, and streaming options—choose faster analytics with less cost.
Choosing a database for customer-facing analytics in 2026? Compare warehouses, real-time OLAP, and hybrid scale-up to cut latency and TCO—read now.