Google Cloud Professional Data Engineer

Introduction to Data Engineering & GCP covers building, processing, and managing data pipelines using Google Cloud Platform services for scalable analytics.

Data Engineering Concepts cover data ingestion, transformation, storage, and pipeline orchestration to enable reliable and scalable analytics systems.

Cloud Storage & Data Lake Design focus on organizing, storing, and managing large volumes of structured and unstructured data for scalable analytics.

BigQuery is Google Cloud’s fully managed data warehouse that enables fast SQL-based analytics on large-scale datasets.

Data Ingestion Methods involve collecting data from multiple sources using batch, streaming, or real-time pipelines for processing and analytics.

Data Integration & ETL involve extracting data from sources, transforming it into usable formats, and loading it into target systems for analysis.

Workflow Orchestration manages and automates the execution, scheduling, and monitoring of data pipelines and tasks across systems.

Dataproc & Spark on GCP provide managed, scalable big data processing using Apache Spark for fast data analytics and transformations.

Batch Data Processing is a method of processing large volumes of data together at scheduled intervals rather than in real time.

Streaming Data Processing is a method of processing data continuously in real time as it is generated from sources like applications, sensors, or logs.

Data Governance & Metadata Management ensures data quality, security, compliance, and proper documentation by defining policies and managing data definitions, ownership, and lineage.

Monitoring, Logging & Troubleshooting involves tracking system performance, recording events, and quickly identifying and fixing issues to ensure reliable operations.