[100% Off] Gcp Data Engineering Project: Mysql To Bigquery Via Dataflow
Build a real-world incremental pipeline with Cloud SQL, Dataflow, BigQuery, Cloud Composer, watermarks and MERGE logic.
What you’ll learn
- Build an end-to-end incremental pipeline from Cloud SQL MySQL to BigQuery using Dataflow,Configure Cloud SQL
- private networking
- Dataflow
- BigQuery
- and Cloud Composer.,Implement watermark-based ingestion and BigQuery MERGE logic for new and updated records.,Orchestrate and monitor repeatable pipeline runs using Cloud Composer and Apache Airflow.
Requirements
- Basic familiarity with SQL and Python will help you get the most out of this course.,A computer with Internet Access,No prior Google Cloud
- Dataflow
- or Cloud Composer experience is required.
Description
Build a realistic GCP data engineering project that ingests retail data from Cloud SQL for MySQL into BigQuery using Google Cloud Dataflow.
You will work through the project as a data engineer—from understanding the business context and source systems to defining the pipeline contract, designing incremental loads, setting up Google Cloud resources, implementing the solution, validating the output, and orchestrating repeatable runs.
In this project, you will:
Prepare the source: Set up retail source tables in Cloud SQL for MySQL
Configure networking: Connect Cloud SQL and Dataflow using private networking
Build the ingestion pipeline: Move data from MySQL into BigQuery using Dataflow
Load data incrementally: Use watermarks to process new and updated records
Create BigQuery tables: Build staging and raw tables with partitioning and clustering
Merge incremental data: Use BigQuery MERGE procedures to update target tables
Orchestrate the pipeline: Deploy and schedule workflows using Cloud Composer and Apache Airflow
Validate the results: Query the loaded data and verify initial and incremental runs in BigQuery
More than isolated service demonstrations
You will build the project in your own Google Cloud account using the provided source code, sample retail data, configuration files, SQL scripts, and infrastructure setup scripts.
The focus is not only on getting the pipeline to run. You will understand why each Google Cloud service is used, how Cloud SQL, Dataflow, BigQuery, and Cloud Composer work together, and how the pipeline handles new and updated source records during subsequent runs.
By the end of the course, you will have a complete GCP data engineering project that you can practice, adapt for your portfolio, and explain clearly in interviews.
This course contains a promotion.
