Data Engineering for Beginner using Google Cloud Python
What you’ll learn
Basic data engineering, what is data engineering, why needed, how to do it from zero
Relational database model, database modelling for normalization design & hands-on using postgresql & python / pandas
NoSQL database model, denormalization design & hands-on using elasticsearch & python / pandas
Introduction to spark & spark cluster using google cloud platform
Requirements
Understanding basic sql statements (select, insert, update, delete is sufficient)
Understanding basic python / pandas
The course uses google cloud platform. If you wants to do hands-on, you need to provide credit card detail for payment on google cloud. If you don’t, you can still watch the course video
Description
“Data is the new oil”. You might have heard the quote before. Data in digital era is as valuable as oil in industrial era. However, just like oil, raw data itself is not usable. Rather, the value is created when it is gathered completely and accurately, connected to other relevant data, and done so in a timely manner.Data engineers design and build pipelines that transform and transport data into a usable format. A different role, like data scientist or machine learning engineer then able to use the data into valuable business insight. Just like raw oil transformed into petrol to be used through complex process.To be a data engineer requires a lot of data literacy and practice. This course is the first step for you who want to know about data engineering. In this course, we will see theories and hands-on to introduce you to data engineering. As data field is very wide, this course will show you the basic, entry level knowledge about data engineering process and tools.This course is very suitable to build foundation for you to go to data field. In this course, we will learn about:Introduction to data engineeringRelational & non relational databaseRelational & non relational data modelTable normalizationFact & dimension tablesTable denormalization for data warehouseETL (Extract Transform Load) & data staging using pyhton pandasElasticsearch basicData warehouseNumbers every engineers should know & how it is related to big dataHadoopSpark cluster on google cloud dataprocData lakeImportant NotesData field is HUGE! This course will be continuously updated, but for time being, this contains introduction to concept, and sample hands-on for data engineering. For now, this course is intended for beginner on data engineering. If you have some experience on programming and wonder about data engineering, this course is for you.If you have experience in data engineering field, this course might be too basic for you (although I’m very happy if you still purchase the course)If you never write python or SQL before, this course is not for you. To understand the course, you must have basic knowledge on SQL and pyhton.
Overview
Section 1: Introduction
Lecture 1 Welcome to This Course
Lecture 2 Course Structure & Coverage
Lecture 3 How To Get Maximum Value From This Course
Section 2: Introduction to Data Engineering
Lecture 4 What is Data Engineering?
Lecture 5 Data Engineering Example
Lecture 6 What is Data Modelling?
Section 3: Database
Lecture 7 What is Database
Lecture 8 Relational Database
Lecture 9 When Not To Use Relational Database?
Lecture 10 NoSQL Database
Lecture 11 Demo : Postgresql
Lecture 12 Demo : Python for Postgresql
Lecture 13 Demo : Elasticsearch
Lecture 14 Demo : Python for Elasticsearch
Section 4: Relational Database Model
Lecture 15 The Importance of Relational Data Model
Lecture 16 OLTP vs OLAP
Lecture 17 Database Normalization
Lecture 18 First Normal Form (1NF)
Lecture 19 Second Normal Form (2NF)
Lecture 20 Third Normal Form (3NF)
Lecture 21 Normalization Python Demo
Lecture 22 Normalization Tips
Lecture 23 Database Denormalization
Lecture 24 Denormalization Python Demo
Lecture 25 Fact & Dimension Tables
Lecture 26 Star Schema
Lecture 27 Star Schema Python Demo
Lecture 28 Snowflake Schema
Lecture 29 Galaxy Schema
Lecture 30 Extract Transform Load (ETL) & Staging Tables
Lecture 31 ETL & Staging Tables – Demo Overview
Lecture 32 ETL & Staging Tables – Python Demo 1
Lecture 33 ETL & Staging Tables – Python Demo 2
Lecture 34 To Insert or To Update?
Lecture 35 ETL & Staging Tables – Python Demo 3
Lecture 36 ETL & Staging Tables – Python Demo 4
Lecture 37 ETL & Staging Tables – Tips
Section 5: NoSQL Database Model
Lecture 38 Basic NoSQL Concept
Lecture 39 CAP Theorem
Lecture 40 Denormalization on Elasticsearch
Lecture 41 Elasticsearch Basic Usage
Lecture 42 Elasticsearch Index & Document
Lecture 43 Elasticsearch ETL – Overview
Lecture 44 Elasticsearch Query DSL
Lecture 45 Elasticsearch ETL – Python Demo
Section 6: Data Warehouse
Lecture 46 Business Perspective
Lecture 47 Technical Perspective
Lecture 48 More Fact & Dimension Table
Lecture 49 OLAP Cube
Lecture 50 On-Premise or Cloud?
Lecture 51 Various Techniques
Lecture 52 Demo Overview
Lecture 53 Demo 1 – PostgreSQL Data Warehouse
Lecture 54 Demo 2 – BigQuery Data Warehouse
Lecture 55 Demo 3 – Data Warehouse Operations
Section 7: Numbes Every Engineer Should Know
Lecture 56 Numbers Every Engineer Should Know
Lecture 57 Small Numbers
Lecture 58 Big Numbers
Section 8: Hadoop & Spark
Lecture 59 Hadoop Ecosystem
Lecture 60 Introducing Spark
Lecture 61 Spark Programming
Lecture 62 Data Formats
Lecture 63 Hello Spark
Lecture 64 Spark Demo – Dataframe
Lecture 65 Spark Demo – Spark SQL
Lecture 66 Spark & BigQuery – Setting Environment
Lecture 67 Spark & BigQuery – ETL Movies
Lecture 68 Spark & BigQuery – Lesson Learned
Section 9: Spark Cluster on Google Cloud (Dataproc)
Lecture 69 Spark Cluster – Overview
Lecture 70 Demo : Big Data
Lecture 71 Google Dataproc
Section 10: Data Lake
Lecture 72 Data Lake Overview
Lecture 73 Schema On Read
Lecture 74 Lake, not Swamp
Lecture 75 Google Data Catalog
Section 11: Resources & References
Lecture 76 Download Source Code & Datasets
Lecture 77 Bonus & Discount Codes
Beginner python developer curious about data engineering,Software engineer who wants to take the path of becoming data engineer,Technical architect, engineering manager, who wants to know overview of data engineering
Course Information:
Udemy | English | 8h 3m | 3.30 GB
Created by: Timotius Pamungkas
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