Last updated 12/2021MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 390.28 MB | Duration: 0h 45m

AWS DataBrew

What you'll learn

AWS Glue DataBrew

Transformations using DataBrew

Cleanup and Normalize data

Data Profiling

Scheduling DataBrew Jobs

Requirements

No programming experience required. Basic understanding of ETL/ELT and Cloud Architecture is an advantage, but not mandatory

Description

The course is about AWS Glue DataBrew.AWS Glue DataBrew is a new visual data preparation tool that makes it easy for data analysts and data scientists to clean and normalize data to prepare it for analytics and machine learning. You can choose from over 250 pre-built transformations to automate data preparation tasks, all without the need to write any code. You can automate filtering anomalies, converting data to standard formats, and correcting invalid values, and other tasks. After your data is ready, you can immediately use it for analytics and machine learning projects. You only pay for what you use - no upfront commitment.Using DataBrew, business analysts, data scientists, and data eeers can more easily collaborate to get insights from raw data. Because DataBrew is serverless, no matter what your technical level, you can explore and transform terabytes of raw data without needing to create clusters or manage any infrastructure.With the intuitive DataBrew interface, you can interactively discover, visualize, clean, and transform raw data. DataBrew makes smart suggestions to help you identify data quality issues that can be difficult to find and -consuming to fix. With DataBrew preparing your data, you can use your to act on the results and iterate more quickly. You can save transformation as steps in a recipe, which you can update or reuse later with other datasets, and deploy on a continuing basis.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Overview of AWS Glue DataBrew

Section 2: Creating Data Set

Lecture 3 Create RDS Data Set

Lecture 4 Create S3 Data Set

Section 3: Filter and Column

Lecture 5 Filter

Lecture 6 Column

Section 4: Format, Clean and Extract

Lecture 7 Format

Lecture 8 Clean

Lecture 9 Extract

Section 5: Missing, Invalid, Duplicates and Outliers

Lecture 10 Missing

Lecture 11 Invalid

Lecture 12 Duplicates

Lecture 13 Outliers

Section 6: Split, Merge and Create

Lecture 14 Split

Lecture 15 Merge

Lecture 16 Create

Section 7: Group, Join and Union

Lecture 17 Group

Lecture 18 Join

Lecture 19 Union

ETL Developers,Data Eeers,Data Architects,Business Analysts

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