What's changed: Created Associate Data Practitioner Chapter 2 (Domain 2 "Analysis and presentation": analysis with BigQuery and notebooks = SQL/Colab Enterprise; dashboards with Looker = Looker/Looker Studio/LookML; using ML models = BigQuery ML/AutoML/LLM via remote connection/standard ML project/Model Registry).
2.1Analysis with BigQuery and notebooks
Understand running SQL queries in BigQuery to produce reports and key insights, analyzing and visualizing data with Jupyter notebooks (Colab Enterprise), and conducting data analysis to answer business questions.
Extracting value from ingested data centers on analysis. On Google Cloud, SQL queries and notebook-based exploratory analysis are the two pillars.
2.1.1SQL analysis in BigQuery
BigQuery is a serverless data warehouse where you run SQL queries over petabyte-scale data to quickly produce reports and key insights. Its benefit is aggregating, joining, and analyzing data in place without moving it; the basic use is "answer business questions with SQL." For complex analysis or visualization, combine it with notebooks below.
2.1.2Jupyter notebooks (Colab Enterprise)
For more exploratory, iterative analysis and visualization, use Jupyter notebooks. On Google Cloud, Colab Enterprise provides a managed notebook environment combining Python and SQL to wrangle and visualize data and even prototype machine learning. Map "fast aggregation with SQL = BigQuery" and "exploratory analysis/visualization in code = notebook (Colab Enterprise)."
Common: goal → tool. E.g., "reports/insights with SQL" = BigQuery; "analyze without moving data" = BigQuery; "exploratory analysis/visualization in Python and SQL" = Jupyter notebooks (Colab Enterprise); "answer business questions" = analysis (BigQuery + notebook).
Watch the mix-ups: (1) BigQuery (SQL, aggregation) and notebook (exploratory, code) are complementary. (2) BigQuery analyzes without moving data. (3) Visualization is via dashboards (Looker, next section) or notebooks.
2.1.3Section summary
- BigQuery = fast SQL analysis on large data (no movement); reports/insights
- Jupyter notebooks (Colab Enterprise) = exploratory analysis/visualization in Python + SQL
- Answer business questions using SQL (BigQuery) and notebooks as appropriate
Sign in to track progress — Log in.
Quick check
(just a quick review)Q1. To run SQL queries over petabyte-scale data for reports and insights, which is best?
Q2. To exploratorily analyze/visualize data combining Python and SQL, which Google Cloud managed notebook fits?
Q3. Which best describes a key benefit of analyzing in BigQuery?
Q4. Which correctly describes the relationship of BigQuery and notebooks?
Q5. To answer a business question (e.g., which product is growing), what is the basic first step?
Q6. Which best describes the main use of notebooks (Colab Enterprise)?