INTELLIGENCE
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How to Get Started with Mage AI
Mage AI is an open-source data pipeline orchestration tool that lets you build, run, and monitor ETL workflows through a visual notebook interface. Unlike heavier tools such as Apache Airflow, Mage runs locally via Docker with no configuration overhead, and pipelines are built from modular blocks in Python or SQL. Most teams can have a working pipeline loading, transforming, and exporting data within 30 minutes of installation.
How to Analyze Data with PandasAI
PandasAI is a Python library that lets you query your data in plain English instead of writing code. You point it at a CSV file, SQL database, or pandas DataFrame, ask a question like "what was the top product by revenue last quarter?", and it generates and runs the analysis for you, returning the result or a chart. Setup requires Python 3.8 or later and an OpenAI API key, and takes about 10 minutes.
How to Get Started with Quadratic AI Spreadsheet
Quadratic is an AI-powered spreadsheet that runs in the browser and lets you write Python, SQL, or plain English to analyze data without switching tools. You connect a CSV or database, type what you want to find, and Quadratic returns the result directly in the grid. It is built for analysts and ops teams who need more than formulas but do not want to manage a full data stack.
How to Get Started with Prefect 3
Prefect 3 is a Python-native workflow orchestration framework that turns decorated Python functions into scheduled, retryable, observable data pipelines. Installing it takes one command (pip install prefect), and a working scheduled flow requires fewer than 20 lines of code. Prefect Cloud's free tier supports up to 500 flow runs per month with no credit card required, making it the fastest path from a manual Python script to a production-grade pipeline.
How to Set Up Lightdash with dbt
Lightdash is an open-source BI tool that connects directly to a dbt project and turns your existing model YAML into an explorable analytics layer. To set it up, install Lightdash via Docker Compose, point it at your dbt project and data warehouse, and add field definitions to your model YAML files. Lightdash generates dimensions and metrics automatically. The full process takes under an hour and gives business users self-serve analytics without writing SQL.
How to Get Started with Marimo Notebook
Marimo is an open-source Python notebook that stores code as plain .py files and runs cells reactively. When one cell changes, all dependent cells update automatically. Unlike Jupyter, marimo notebooks are version-control friendly, executable as scripts, and deployable as interactive apps without a separate server. Install with pip install marimo, create a notebook with marimo edit, and use built-in SQL cells to query dataframes directly from the editor.
How to Use Agent Mode in Excel with Copilot
Agent Mode in Microsoft Excel Copilot, generally available since March 2026, lets users give outcome-based instructions in plain English and have Copilot autonomously plan steps, write formulas, clean data, and build outputs directly in the workbook. It requires a Microsoft 365 Personal, Family, Premium, or commercial Copilot license, and the workbook must be saved to OneDrive or SharePoint. The older Think Deeper and App Skills features were deprecated in February 2026 and are no longer available for most users.
How to Get Started with dbt Cloud
dbt Cloud is a managed web platform for running dbt (data build tool), the open-source framework used by data teams to write, test, and schedule SQL-based transformation models. Unlike dbt Core, which requires a local Python environment and command-line setup, dbt Cloud runs entirely in the browser and connects directly to a cloud data warehouse. A team with an existing warehouse can sign up and run their first transformation model in under 30 minutes.
How to Get Started with Amazon QuickSight
Amazon QuickSight is AWS's cloud-native business intelligence service. To get started, create an AWS account, sign up for QuickSight (Standard for basic dashboards, Enterprise for Amazon Q natural language features), connect a data source such as S3 via Athena or a direct CSV upload, build a dataset, and create an analysis. The full setup takes under 30 minutes for most data sources and requires no SQL knowledge for basic dashboards.