This resource explains what Business Intelligence (BI) is, why it matters, and how modern teams turn raw data into actionable insight. You’ll learn core BI concepts, the end-to-end BI pipeline, common tools (including where Galaxy fits), and best practices. Real-world scenarios, SQL snippets, and guided exercises help you apply the ideas immediately.
Business Intelligence (BI) is the process, technology, and practice of collecting, transforming, and presenting data to support better business decisions. While analytics asks, “Why did X happen?” and data science predicts, “What might happen next?”, BI focuses on providing reliable, timely facts to answer, “What is happening now, and what should we do?”
A mature BI solution typically includes the layers below. Each layer can be provided by standalone tools or an end-to-end platform.
Operational databases (e.g., PostgreSQL), SaaS tools (e.g., Salesforce), logs, spreadsheets—anywhere data is generated.
Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) pipelines move data into a central warehouse. Tools include Fivetran, Airbyte, and custom jobs.
A scalable, analytics-optimized store (Snowflake, BigQuery, Redshift). Warehouses enable complex joins and aggregations at scale.
An abstraction that maps business concepts (e.g., Active Customer) to canonical SQL so everyone calculates metrics the same way. dbt models, LookML, and increasingly tools like Galaxy Collections + Endorsements serve this role.
Dashboards and self-service query tools (Tableau, Power BI, Looker) surface insights. Developers often create the underlying SQL in an IDE such as Galaxy, then pipe results into these front-end layers.
Turning insights into action—triggering alerts, sending data to CRM systems, or exposing metrics through APIs. Galaxy’s roadmap includes live API endpoints from approved queries to streamline this step.
subscriptions, plans). In Galaxy, hover metadata shows columns and relationships instantly.SELECT DATE_TRUNC('week', activated_at) AS week_start,
COUNT(*) AS new_subscriptions
FROM subscriptions
WHERE activated_at >= CURRENT_DATE - INTERVAL '14 days'
GROUP BY 1
ORDER BY 1 DESC;
Assume you have a SaaS product database with users, subscriptions, and events tables.
SELECT event_date,
COUNT(DISTINCT user_id) AS dau
FROM (
SELECT user_id,
DATE_TRUNC('day', occurred_at) AS event_date
FROM events
WHERE occurred_at >= CURRENT_DATE - INTERVAL '30 days'
) sub
GROUP BY event_date
ORDER BY event_date;
Create a bar chart of monthly recurring revenue (MRR) split by plan type.
WITH mrr AS (
SELECT DATE_TRUNC('month', s.activated_at) AS month,
p.plan_name,
SUM(p.price) AS mrr
FROM subscriptions s
JOIN plans p ON p.id = s.plan_id
WHERE s.status = 'active'
GROUP BY 1,2
)
SELECT * FROM mrr ORDER BY month;
ObstacleRoot CauseMitigationMetric DriftMultiple definitions of the same KPIAdopt a semantic layer; endorse queries in GalaxySlow QueriesPoor indexing, large scansUse EXPLAIN plans; let Copilot suggest optimizationsData SilosTeams hoard spreadsheetsCentral warehouse + shared workspaceStale DashboardsManual refresh processesAutomate schedules; leverage Galaxy’s upcoming workflows
The BI ecosystem is broad. Here’s a simplified map:
Galaxy differentiates itself by focusing on developer ergonomics, context-aware AI, and tight collaboration—bridging the gap between raw SQL and governed BI layers.
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