GCP Data Engineer

GCP Data Engineer



1.      Introduction to GCP, Python Basics, and Linux Fundamentals


·         Introduction to Google Cloud Platform (GCP)

·         Overview of GCP services

·         Creating a GCP account and setting up the project

·         Python Basics

·         Variables, data types, and operators

·         Control structures: if-else, loops, and functions

·         Working with Python libraries

·         Linux Fundamentals

·         Linux distributions and installation

·         Basic Linux commands

·         File system structure and navigation


2.      Advanced Python, Linux Concepts, and Introduction to DBMS


·         Python

·         Working with data structures: lists, tuples, dictionaries, and sets

·         Exception handling

·         File handling and operations

·         Linux

·         File permissions and ownership

·         Text editors: Vim and Nano

·         Basic shell scripting

·         Introduction to Database Management Systems (DBMS)

·         Types of DBMS: Relational, NoSQL, and NewSQL

·         Overview of SQL and NoSQL databases

·         Database design and normalization


3.      Python Libraries for Data Engineering and GCP Introduction


·         NumPy

·         Introduction to NumPy and arrays

·         Array operations and functions

·         pandas

·         Introduction to pandas and data manipulation

·         Series and DataFrame objects

·         Handling missing data and data cleaning

·         Introduction to GCP Data Engineering Services

·         Cloud Storage

·         BigQuery


4.      SQL and NoSQL Databases


·         SQL Databases


·         Indexes and performance optimization

·         Database transactions and ACID properties

·         NoSQL Databases

·         Overview of NoSQL database types: Document, Key-Value


5.      Data Processing with Dataflow, Apache Beam, and Dataproc


·         Introduction to Dataflow and Apache Beam

·         Understanding Dataflow and Apache Beam concepts

·         Building data pipelines with Apache Beam

·         Python SDK for Apache Beam

·         Installation and setup

·         Creating simple pipelines

·         Windowing and time-based processing

·         Introduction to Dataproc

·         Overview of Hadoop, Spark, and the Hadoop ecosystem

·         Creating and managing Dataproc clusters

·         Running Hadoop and Spark jobs on Dataproc

·         Submitting jobs using the Cloud SDK

·         Monitoring job progress


6.      Data Transformation, Integration with DBMS, and Final Project


·         Data Transformation with Cloud Data Fusion

·         Introduction to Cloud Data Fusion

·         Overview and use cases

·         Creating and managing instances

·         Building data pipelines with Cloud Data Fusion

·         Pipeline design and development

·         Deploying and monitoring pipelines

·         Integration with SQL and NoSQL Databases

·         Connecting to SQL databases (Cloud SQL) using Python

·         Connecting to NoSQL databases (Firestore, MongoDB, Cassandra) using Python

·         Data import and export between GCP services and external databases

·         Final Project

·         Project briefing and requirements

·         Project implementation

·         Project presentation and evaluation


7.      Data Ingestion, Real-Time Data Processing, and Machine Learning


·         Data Ingestion using Cloud Pub/Sub

·         Introduction to Cloud Pub/Sub

·         Creating topics and subscriptions

·         Publishing and consuming messages using Python

·         Real-Time Data Processing with Cloud Dataflow and Apache Beam

·         Real-time data processing concepts

·         Streaming vs. batch processing

·         Windowing and watermarks

·         Real-time data processing with Apache Beam

·         Developing streaming pipelines

·         Integrating with Cloud Pub/Sub

·         Machine Learning with BigQuery ML and AI Platform



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