Fivertran ETL from Scratch

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Jim Macaulay

1:07:36

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1 - Introduction

1 - Introduction.mp4

00:44

2 - Designing ConnectorPostgreSql and DestinationRedshift

2 - PostgreSql Scenario.mp4

01:40

3 - Connector.mp4

05:17

4 - Destination.mp4

04:27

3 - ETL Sync up from PostgreSql to Redshift

5 - Sync Up.mp4

02:35

6 - Redshift Synced Objects.mp4

03:09

7 - Resync.mp4

02:45

8 - Metadata of Fivetran.mp4

02:43

9 - Connector Properties.mp4

04:05

4 - AWS S3 Buckets and Fivetran

10 - S3 Connector Configuration.mp4

10:35

11 - S3 to Redshift.mp4

03:34

5 - Working with the data

12 - Insert a record.mp4

08:08

13 - Delete a record in Source.mp4

03:38

6 - Transformations

14 - Transformations Overview.mp4

01:01

15 - Data Transformation using Sql.mp4

06:55

16 - Views using Transformation.mp4

06:20

Description

Learn Fivetran from basic to advanced | PostgreSql | Redshift | AWS S3 buckets | Sql Transformations

What You'll Learn?

ETL using Fivetran
Data Integration
Data Sync
Data Migration

Who is this for?

ETL Architects

ETL Developers

Data Integration Developers

Data Migration Specialists

Data Architects

Data Engineers

More details

Description
The building blocks of data organization are tables and schemas. You can think of a table as a file organized by rows and columns and of a schema as a folder that contains multiple tables. Each Fivetran connector creates and manages its own schema. Fivetran connector reaches out to your source, receives data from it, and writes it to your destination. Depending on the type of connector, Fivetran either collects data that the source pushes to us or sends a request to the source and then grabs the data that the source sends in response.

Fivetranâ€™s responsibility
It is Fivetranâ€™s responsibility to deliver up-to-date, accurate information in a cleaned and normalized schema - the canonical schema - at the lowest level of aggregation. It is our responsibility to regularly maintain the connector and evolve the canonical schema to reflect operational and product changes in the source systems. It is our responsibility to respond to any unknown operational breaking change in the extract and load from the source system to the destination schema.

Fivetran connects to all of your supported data sources and loads the data from them into your destination. Each data source has one or more connectors that run as independent processes that persist for the duration of one update. A single Fivetran account, made up of multiple connectors, loads data from multiple data sources into one or more destinations.
Who this course is for:
ETL Architects
ETL Developers
Data Integration Developers
Data Migration Specialists
Data Architects
Data Engineers

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My self Jim, am an experienced Software and Robotics Engineer with a demonstrated history of working in the financial services industry. Passionate about teaching and blogging. Am also a YouTuber and an instructor. Skilled in Data Engineering with various ETL, Database and Cloud Technologies, and a Robotics Engineer. Strong consulting professional with a Master’s degree focused in Software Systems (Data Analytics) from Birla Institute of Technology and Science, Pilani.

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