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Data Tools

This document provides an overview of the main data platforms we use for analytics and data engineering, focusing on BigQuery, Snowflake, and other commonly used tools.

1. Google BigQuery

Overview

BigQuery is a fully managed, serverless data warehouse by Google Cloud. It enables fast SQL queries using the processing power of Google’s infrastructure.

Key Features

  • Serverless: No infrastructure management.
  • Scalability: Handles petabytes of data seamlessly.
  • Performance: Distributed query engine for fast analytics.
  • Integration: Works well with Google Cloud ecosystem (Dataflow, Pub/Sub, Looker, etc.).
  • Pricing: Pay per query (on-demand) or flat-rate.

Typical Use Cases

  • Large-scale data analytics
  • Real-time streaming data analysis
  • Machine learning integration via BigQuery ML
  • Building dashboards with Looker Studio / BI tools

Example SQL

SELECT user_id,
    COUNT(*) AS total_actions
FROM `project.dataset.events`
WHERE event_date >= '2025-01-01'
GROUP BY user_id
ORDER BY total_actions DESC;

2. Snowflake

Overview

Snowflake is a cloud-native data warehouse that separates storage and compute for flexibility and cost efficiency. It runs on AWS, Azure, and Google Cloud.

Key Features

  • Separation of Compute & Storage: Scale independently.
  • Multi-Cloud: Deploy across different cloud providers.
  • Data Sharing: Share data securely across organizations.
  • Semi-Structured Data: Supports JSON, Avro, Parquet.
  • Security: Strong governance, role-based access control.

Typical Use Cases

  • Enterprise data warehousing
  • Data lake + data warehouse hybrid (lakehouse)
  • Secure data collaboration
  • ELT/ETL workloads with dbt or custom pipelines

Example SQL

SELECT customer_id,
    SUM(order_amount) AS total_spent
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY customer_id
ORDER BY total_spent DESC;

3. Other Common Tools

dbt (Data Build Tool)

  • Transformation layer for analytics engineering.
  • Manages SQL models, testing, and documentation.
  • Works with both BigQuery and Snowflake.

Airflow

  • Workflow orchestration for ETL/ELT pipelines.
  • Schedule and monitor jobs.
  • Integrates with GCP, AWS, Snowflake, and more.

Looker / Looker Studio

  • Business intelligence and data visualization tools.
  • Connect directly to BigQuery and Snowflake.
  • Used for dashboards, reporting, and self-service analytics.

Fivetran / Stitch

  • Managed data integration tools (ELT).
  • Automates ingestion from SaaS apps, databases, and APIs.

4. Best Practices

  • Cost Management

    • BigQuery: Use partitioned and clustered tables to optimize queries.
    • Snowflake: Scale warehouses appropriately, suspend unused ones.

  • Data Governance

    • Define access roles and permissions carefully.
    • Keep sensitive data encrypted and masked where required.

  • Performance Optimization

    • Avoid SELECT * in queries.
    • Use caching and materialized views where possible.

  • Documentation & Testing

    • Maintain clear documentation of datasets and models.
    • Use dbt tests to validate data quality.

Summary

  • BigQuery: Best for serverless analytics and GCP-native projects.
  • Snowflake: Flexible, enterprise-grade, multi-cloud data warehouse.
  • Supporting Tools: dbt, Airflow, Looker, and Fivetran complete the data stack.

By combining these tools, we achieve a modern, scalable, and maintainable data ecosystem.