2 Day Workshop - Data Engineering - End to End Project | Part 2

Greetings and casual conversations

Discussion on recent events including festival celebrations and budget updates

Debate on tax changes, budget updates, and implications on different income levels

Detailed discussion on tax brackets, exemptions, and implications on different income levels

Step-by-step guide on creating an Azure storage account, including configurations and options

Walkthrough on creating an Azure SQL database, including configurations and deployment

Explanation of Azure Data Factory with analogies to a garment factory and kitchen

The speaker discusses the process of data processing and the importance of different services like Azure Data Factory for data storage and processing.

The speaker explains the significance of a Business Requirement Document (BRD) as a source of truth for data analytics and engineering teams in a project.

Details about the project architecture including various data storage options like Azure SQL, Azure Data Lake Storage (ADLS), and Block Storage are discussed.

Explanation of the workflow involving cleaning raw data, storing it in different layers (bronze, silver, gold), and applying business logic using PowerBI and Data Engineering roles.

Discussion on Medan architecture used to avoid data corruption and enhance data processing efficiency through bronze, silver, and gold layers with examples of storing and processing raw data.

The speaker talks about extracting and transforming data from different sources (JSON, CSV, Excel) to create a unified dataset in the Silver layer. Various steps such as data cleaning, fixing spelling errors, converting JSON data to table format, and creating new columns are demonstrated.

The focus is on processing encounter data, including encounter IDs, patient information, visit provider IDs, and encounter diagnoses. The speaker explains the concept of encounters in a hospital setting, tracking patient visits, and managing provider information.

The creation of a dataset specific to pregnancy diagnoses is discussed. The speaker demonstrates transforming and loading data related to pregnant patients, including diagnosis codes, anesthesia complications, and spinal issues during pregnancy.

The speaker deals with JSON formatted provider data, explaining the process of converting it into a table format for analysis. Steps like flattening JSON data, importing new columns, and generating new IDs are shown.

The final steps involve creating datasets for PCP data, pregnancy data, and location data. The speaker showcases the conversion of these datasets into CSV format and storing them in the Silver layer for further analysis.

The speaker demonstrates data cleaning and aggregation processes, including grouping by columns and aggregating with functions like count to identify and remove duplicates.

The speaker sets file patterns, like naming conventions, and publishes the cleaned data to new storage locations for further processing.

The speaker discusses cloning pipelines and testing mechanisms, emphasizing the importance of basic pipeline setups and data flow handling.

The speaker explains the concept of incremental loading and limitations in data delegation, highlighting the need to manage data sources and connections effectively.

The speaker delves into integration runtimes, their types, and the significance of using the right runtime for seamless data movement across systems.

The speaker showcases data transformation techniques with SQL queries, such as deriving new columns like age from existing data points like birth date.

The speaker orchestrates multiple pipelines, ensures data flows correctly, performs final data joining operations, and prepares a centralized table for consolidated data.

The speaker discusses the process of data mapping and joins between different tables, focusing on patient data, location data, provider details, and diseases. Various join types like inner join, left join, and right join are explained in detail.

The speaker demonstrates how to transform data by excluding unnecessary columns like IDs, and how to perform left joins to retain all patient records. The focus is on joining patient data with location and provider details while excluding irrelevant columns.

The process of creating a final data set for analysis is explained, including joining patient data with provider details and diseases. The speaker mentions the importance of double-checking the joins and creating a final data set for analysis in CSV format.

The speaker optimizes the data flow by pushing the final data set to the gold layer and showcases the flow from silver to gold. The steps for deployment, including syncing to a SQL database and creating copies for PowerBI developers, are detailed.

The speaker discusses resource management by creating Resource Groups for testing and production. Cost considerations for different services and practices, such as synapse and data bricks, are highlighted, emphasizing the importance of efficient learning and cost-effective practices.