Basic data engineering : python, pandas, google cloud platform (GCP) bigquery, spark on dataproc, gcs, data warehouse

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 аbout: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.

Who this course is for:
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

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