DuckDB - The Ultimate Guide

Explore DuckDB, a lightweight embedded analytics database with columnar storage and vectorized execution, available locally or in the cloud, featuring a rich SQL dialect and Python and R integration.

Uncover four reasons to use DuckDB for analytics: lighter setup than Hadoop or Spark, faster than SQLite and Pandas, and on-device deployment via Wasm.

Identify who benefits from DuckDB across data scientists, data analysts, ML engineers, data engineers, DevOps specialists, IoT developers, and software engineers, from edge computing to rapid prototyping.

Discover DuckDB's sql basics, switch between duckbox, line, and box views with .mode, and perform blazing fast aggregations, including group by all on external data sources.

Explore dag db's cli and sql innovations using the Chinook database. Navigate 11 tables and study the provided readme and pdf while using an interactive terminal.

Learn how to integrate GDB with Python environments to streamline data work, and leverage dqdb.org documentation and client APIs blog for guidance.

Download the supplemental materials, including the hourly emissions parquet file from Kaggle, unzip it, and load it in a Jupyter notebook to explore Python client and Python integration features.

Explore a real-world analytics workflow with DuckDB and Python, analyzing a five gigabyte parquet dataset to compute average and maximum CO2 emissions, filter by Columbus, and compare performance to pandas.

Explore how the python-based Streamlit framework enables creation and deployment of analytical apps, sharing ML models, with deployment to GCP, AWS, or Streamlit Cloud, and integration with gdb.

Launch a Streamlit app powered by DuckDB to query tennis head-to-head statistics from GitHub CSV data using a search box.

Explore a practical case of integrating DuckDB with DBT to transform raw CSV transactional data into normalized fact and dimension tables, with tests and documentation.

Sign up for the Motherduck platform, download supplemental materials, and explore notebooks, databases, and S3 assets while querying the NYC taxi data and exporting results as CSV, Parquet, or JSON.

Discover how the Motherduck analytics environment executes and formats SQL queries, generates column-based insights like complaint type distributions, and accesses S3 parquet data for quick exploratory data analysis.

Enable automated authentication to cloud-based motherduck from locally running duckdb. Create and export a read-write token, connect via duckdb, and list tables.

Learn to migrate data from S3 to Motherduck using Dldt and set up auto updating shares. Manage and share datasets via SQL or UI, with live updates and access controls.

Explore how DuckDB powers real, a modern bi tool turning data lakes into dashboards. Learn bi as code and last-mile etl in two deployment modes, locally or in the cloud.

Riel is an open source platform that enables real-time data processing and analytics through streaming data, transformations, and seamless integration with sources like Elasticsearch and Kafka.

Utilize DuckDB native sql to normalize diverse data sources, rename and lowercase fields, union multiple datasets into one unified table, then compute start and end event duration in hours.

Outline a dashboard configuration in yaml, defining a data model, time series axes, measures, and dimensions to filter by city and summarize tickets for interactive charts.

Set up a local duckdb-backed real server to explore the 311 service data, dashboards, and automated profiling, including sources, models, and the real.yaml configuration.

Shows deploying dashboards to real cloud via infrastructure as code, and sets up GitHub version control by creating a public repo, initializing git, committing, renaming to main, and pushing.

Create and activate a Python 3.9 virtual environment, install Dexter, Dexter Duckdb, and Dexter Web Server with required packages, then configure a Mother Duck token in an env file.

Explore the data pipeline walkthrough by inspecting the ATP matches and players assets, cron-based scheduling, environment variables, and Duckdb/Mother Duck integration for loading tennis data.

Alter players table to add a name_foo column that concatenates first and last names. Create the ATP rounds and levels datasets and orchestrate the pipeline with dag db and gdb.