Great question. Here’s a breakdown of top use cases for a Data Analyst working with:
- BigQuery Sandbox, and
- Raw Layer of a Data Lake (e.g., DataLake, GCS, or raw zone in a data lakehouse)
1. BigQuery Sandbox – Use Cases for Data Analysts
The BigQuery Sandbox is a free, no-credit-card-required environment, ideal for prototyping and learning. It has usage limits but supports real SQL capabilities.
Top Use Cases:
- Ad-hoc SQL Analysis
- Run quick queries against public datasets or connected sources for exploratory analysis.
- Data Cleaning and Transformation
- Use SQL to apply filters, remove duplicates, standardize formats (e.g., dates, currency).
- Data Joins Across Tables
- Combine datasets using JOIN to enrich or correlate data.
- Custom Metric Calculation
- Create derived metrics like conversion rates, retention, churn, etc.
- Visualization Prototyping
- Connect BigQuery Sandbox to tools like Looker Studio (free) for dashboard mockups.
- Query Optimization Practice
- Analyze execution plans and learn how to optimize SQL using partitioning, clustering, and caching.
- Public Dataset Exploration
- Leverage Google’s public datasets (e.g., COVID, Census, StackOverflow) for practice and insights.
2. Raw Layer of Data Lake – Use Cases for Data Analysts
The raw layer stores unprocessed, large-volume data — often in formats like JSON, Parquet, or CSV — usually on cloud storage (like GCS, S3, or Azure Data Lake).
Top Use Cases:
- Schema Discovery & Data Profiling
- Use tools like bq load, bq show, or data catalog to inspect structure, nulls, ranges, outliers.
- Data Ingestion Validation
- Check if all expected files landed, and validate record counts, file sizes, or timestamps.
- Raw-to-Curated Transformation
- Write SQL or Spark queries (depending on infra) to shape raw data into usable curated/clean tables.
- Change Detection
- Compare raw file drops over time for changes in volume, structure, or anomalies.
- Data Lineage & Traceability
- Understand source system behavior by inspecting raw logs or events (e.g., API payloads, user events).
- Staging for Snapshotting
- Pull in the raw data to BigQuery staging tables for snapshotting/archival analytics.
Combined Workflow Example:
- Analyst pulls raw JSON logs from GCS into BigQuery temporary table.
- Cleans and joins with dimension data in curated layer.
- Creates KPIs and dashboards in Looker Studio using the sandbox or scheduled queries.
Would you like a visual diagram or cheat sheet to map these layers and use cases?