BigQuery and Data Warehousing on GCP

Understanding the Google Cloud ecosystem for data.

• Overview of GCP core services (Compute Engine, GCS, IAM).
• The GCP Data Lifecycle (Ingest, Store, Process, Analyze).
• Key Data Services: BigQuery, Dataflow, Pub/Sub, Dataproc.
• Setting up a GCP project and billing.

Understanding the architecture and core concepts of BigQuery.

• BigQuery serverless architecture (Dremel, Colossus).
• Projects, Datasets, and Tables.
• BigQuery pricing models (On-demand vs. Flat-rate).
• Querying public datasets with the BigQuery UI and CLI (`bq`).

Ingesting data from various sources.

• Batch loading from Google Cloud Storage (GCS) (CSV, JSON, Parquet).
• Streaming data using the Storage Write API.
• Federated Queries (querying data in GCS, Cloud SQL directly).
• Using the BigQuery Data Transfer Service (DTS).

Leveraging BigQuery's powerful SQL dialect.

• Working with Arrays and Structs (Nested/Repeated fields).
• Advanced JOINs and set operations.
• Analytic (Window) functions.
• User-Defined Functions (UDFs) in SQL and JavaScript.

Designing efficient tables for performance and cost.

• Star vs. Snowflake schemas.
• Denormalization using nested and repeated fields.
• Partitioned Tables (Time-unit, Ingestion-time, Integer-range).
• Clustered Tables for query optimization.

Managing your BigQuery environment effectively.

• Understanding the BigQuery query plan.
• Best practices for query optimization (filtering early, avoiding `SELECT *`).
• Managing costs with quotas, custom quotas, and monitoring.
• Caching and materialized views.

Building ML models using only SQL.

• The BQML process (CREATE MODEL, EVALUATE, PREDICT).
• Building regression and classification models.
• Forecasting with time-series models.
• Importing and using TensorFlow models in BigQuery.

Building scalable ETL pipelines for BigQuery.

• Dataflow (Apache Beam) for batch and stream processing.
• Dataproc (Managed Spark/Hadoop) for big data jobs.
• Reading from and writing to BigQuery with Dataflow/Dataproc.
• Orchestrating pipelines with Cloud Composer (Airflow).

Connecting your data warehouse to BI tools.

• Connecting BigQuery to Looker Studio (formerly Data Studio).
• Building interactive dashboards and reports.
• BigQuery BI Engine for in-memory acceleration.
• Connecting to other tools (e.g., Tableau).

Building a complete data warehouse and analytics solution.

• Design a data model for a chosen dataset (e.g., e-commerce).
• Ingest data into GCS and load into BigQuery.
• Create partitioned and clustered tables.
• Build a Looker Studio dashboard to analyze the data.