Microsoft Data Engineering on Microsoft Azure (DP - 203) Exam Prep

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Free Download Microsoft Data Engineering on Microsoft Azure (DP-203) Exam Prep: 300 Practice Questions and Answers Across All 3 Domains by James Howlett
English | October 18, 2024 | ISBN: N/A | ASIN: B0DKB5Y6X1 | 93 pages | EPUB | 0.16 Mb
Are you ready to master the DP-203 exam and accelerate your career as a Microsoft Azure Data Engineer?

This comprehensive exam prep guide is your ultimate resource for gaining the knowledge and confidence needed to pass the Microsoft Data Engineering on Microsoft Azure (DP-203) certification exam.
Inside, you'll find:
* 300 Expertly Crafted Practice Questions: Test your skills with a wide range of questions that mirror the actual DP-203 exam format.
* Coverage of All 3 Domains: From designing and implementing data storage to securing and monitoring data platforms, this book thoroughly covers all aspects of the DP-203 syllabus.
* Insights from Azure Data Experts: Learn from experienced data professionals who have contributed their expertise to ensure the questions and explanations are aligned with real-world applications.
* Comprehensive Learning Experience: Each topic is broken down with clear, concise explanations that make even complex subjects easy to understand. Perfect for hands-on professionals and newcomers alike.
Whether you're an experienced data engineer or starting your journey in data engineering, this guide will help you confidently prepare for the DP-203 exam and advance your skills in Microsoft Azure.

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Data Engineering On Microsoft Azure
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.44 GB | Duration: 6h 24m​

DP 203 - Azure Data Engineer Associate

What you'll learn

Implement a partition strategy

Design and implement the data exploration layer

Ingest and transform data

Develop a batch processing solution

Develop a stream processing solution

Manage batches and pipelines

Implement data security

Monitor data storage and data processing

Optimize and troubleshoot data storage and data processing

Requirements

Foundational Knowledge of Azure

Description

In this course, you will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage. You can become a data professional, a data architect, or a business intelligence professional by learning about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. This course will give you a flavor of end-to-end processing of big data in Azure.As a candidate for this certification, you should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions.As an Azure data engineer, you help stakeholders understand the data through exploration, and build and maintain secure and compliant data processing pipelines by using different tools and techniques. You use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis.

Overview

Section 1: Microsoft Azure Data Engineering Introduction

Lecture 1 Introduction

Section 2: Get started with data engineering on Azure

Lecture 2 Introduction to data engineering on Azure

Lecture 3 Introduction to Azure Data Lake Storage Gen2

Lecture 4 Introduction to Azure Synapse Analytics

Lecture 5 Lab - Explore Azure Synapse Analytics

Section 3: Build data analytics solutions using Azure Synapse Analytics serverless SQL pool

Lecture 6 Use a serverless SQL pool to query files in a data lake

Lecture 7 Use a serverless SQL pool to transform data

Lecture 8 Lab - Transform files using a serverless SQL pool

Lecture 9 Create a lake database

Section 4: Perform data engineering with Azure Synapse Apache Spark Pools

Lecture 10 Analyze data with Apache Spark in Azure Synapse Analytics

Lecture 11 Transform data with Apache Spark in Azure Synapse Analytics

Lecture 12 Lab - Transform data using Spark in Synapse Analytics

Lecture 13 Use Delta Lake in Azure Synapse Analytics

Lecture 14 Lab - Use Delta Lake with Spark in Azure Synapse Analytics

Section 5: Work with data warehouses using Azure Synapse Analytics

Lecture 15 Analyze data in a relational data warehouse

Lecture 16 Load data into a relational data warehouse

Lecture 17 Lab - Load Data into a Relational Data Warehouse

Section 6: Transfer and transform data with Azure Synapse Analytics Pipelines

Lecture 18 Build a data pipeline in Azure Synapse Analytics

Lecture 19 Lab - Build a data pipeline in Azure Synapse Analytics

Lecture 20 Use Spark Notebooks in an Azure Synapse Pipeline

Lecture 21 Lab - Use an Apache Spark notebook in a pipeline

Section 7: Hybrid transactional and analytical processing Solutions using Synapse Analytics

Lecture 22 Plan hybrid transactional and analytical processing

Lecture 23 Implement Azure Synapse Link with Azure Cosmos DB

Lecture 24 Lab - Use Azure Synapse Link for Azure Cosmos DB

Lecture 25 Implement Azure Synapse Link for SQL

Section 8: Implement a data streaming solution with Azure Stream Analytics

Lecture 26 Get started with Azure Stream Analytics

Lecture 27 Streaming data using Azure Stream Analytics and Azure Synapse Analytics

Lecture 28 Lab - Ingest data with Azure Stream Analytics and Azure Synapse Analytics

Lecture 29 Visualize real-time data with Azure Stream Analytics and Power BI

Section 9: Govern data across an enterprise

Lecture 30 Introduction to Microsoft Purview

Lecture 31 Integrate Microsoft Purview and Azure Synapse Analytics

Lecture 32 Lab - Use Microsoft Purview with Azure Synapse Analytics

Section 10: Data engineering with Azure Databricks

Lecture 33 Explore Azure Databricks

Lecture 34 Use Apache Spark in Azure Databricks

Lecture 35 Lab - Use Spark in Azure Databricks

Lecture 36 Run Azure Databricks notebooks in Azure Data Factory

Section 11: Conclusion

Lecture 37 Redemption of Badges

Data Engineers: Professionals who focus on preparing "big data" for analytical or operational uses. These individuals are responsible for designing, building, and maintaining the architecture (such as databases and large-scale processing systems) for data ingestion, processing, and analytics.,Data Architects: These are professionals who design the blueprint for managing data across the organization. They work on designing data solutions that utilize Azure services effectively to meet both the technical and business requirements.,Data Professionals: This broad category includes anyone working with data in a technical capacity and looking to leverage Azure's data services for their data solutions. This could include database administrators, data analysts, and software developers with a focus on data.,IT Professionals: IT professionals who are not necessarily data specialists but are looking to expand their skills into the data engineering space can benefit from this course. This includes system administrators, software developers, and IT managers who need to understand how data solutions are designed and implemented on Azure.,Students and Recent Graduates: Those who are studying in fields related to computer science, data science, information technology, or similar areas and are looking to enter the workforce with a strong set of skills in cloud-based data solutions.

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Data Engineering On Microsoft Azure
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.44 GB | Duration: 6h 24m​

DP 203 - Azure Data Engineer Associate

What you'll learn

Implement a partition strategy

Design and implement the data exploration layer

Ingest and transform data

Develop a batch processing solution

Develop a stream processing solution

Manage batches and pipelines

Implement data security

Monitor data storage and data processing

Optimize and troubleshoot data storage and data processing

Requirements

Foundational Knowledge of Azure

Description

In this course, you will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage. You can become a data professional, a data architect, or a business intelligence professional by learning about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. This course will give you a flavor of end-to-end processing of big data in Azure.As a candidate for this certification, you should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions.As an Azure data engineer, you help stakeholders understand the data through exploration, and build and maintain secure and compliant data processing pipelines by using different tools and techniques. You use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis.

Overview

Section 1: Microsoft Azure Data Engineering Introduction

Lecture 1 Introduction

Section 2: Get started with data engineering on Azure

Lecture 2 Introduction to data engineering on Azure

Lecture 3 Introduction to Azure Data Lake Storage Gen2

Lecture 4 Introduction to Azure Synapse Analytics

Lecture 5 Lab - Explore Azure Synapse Analytics

Section 3: Build data analytics solutions using Azure Synapse Analytics serverless SQL pool

Lecture 6 Use a serverless SQL pool to query files in a data lake

Lecture 7 Use a serverless SQL pool to transform data

Lecture 8 Lab - Transform files using a serverless SQL pool

Lecture 9 Create a lake database

Section 4: Perform data engineering with Azure Synapse Apache Spark Pools

Lecture 10 Analyze data with Apache Spark in Azure Synapse Analytics

Lecture 11 Transform data with Apache Spark in Azure Synapse Analytics

Lecture 12 Lab - Transform data using Spark in Synapse Analytics

Lecture 13 Use Delta Lake in Azure Synapse Analytics

Lecture 14 Lab - Use Delta Lake with Spark in Azure Synapse Analytics

Section 5: Work with data warehouses using Azure Synapse Analytics

Lecture 15 Analyze data in a relational data warehouse

Lecture 16 Load data into a relational data warehouse

Lecture 17 Lab - Load Data into a Relational Data Warehouse

Section 6: Transfer and transform data with Azure Synapse Analytics Pipelines

Lecture 18 Build a data pipeline in Azure Synapse Analytics

Lecture 19 Lab - Build a data pipeline in Azure Synapse Analytics

Lecture 20 Use Spark Notebooks in an Azure Synapse Pipeline

Lecture 21 Lab - Use an Apache Spark notebook in a pipeline

Section 7: Hybrid transactional and analytical processing Solutions using Synapse Analytics

Lecture 22 Plan hybrid transactional and analytical processing

Lecture 23 Implement Azure Synapse Link with Azure Cosmos DB

Lecture 24 Lab - Use Azure Synapse Link for Azure Cosmos DB

Lecture 25 Implement Azure Synapse Link for SQL

Section 8: Implement a data streaming solution with Azure Stream Analytics

Lecture 26 Get started with Azure Stream Analytics

Lecture 27 Streaming data using Azure Stream Analytics and Azure Synapse Analytics

Lecture 28 Lab - Ingest data with Azure Stream Analytics and Azure Synapse Analytics

Lecture 29 Visualize real-time data with Azure Stream Analytics and Power BI

Section 9: Govern data across an enterprise

Lecture 30 Introduction to Microsoft Purview

Lecture 31 Integrate Microsoft Purview and Azure Synapse Analytics

Lecture 32 Lab - Use Microsoft Purview with Azure Synapse Analytics

Section 10: Data engineering with Azure Databricks

Lecture 33 Explore Azure Databricks

Lecture 34 Use Apache Spark in Azure Databricks

Lecture 35 Lab - Use Spark in Azure Databricks

Lecture 36 Run Azure Databricks notebooks in Azure Data Factory

Section 11: Conclusion

Lecture 37 Redemption of Badges

Data Engineers: Professionals who focus on preparing "big data" for analytical or operational uses. These individuals are responsible for designing, building, and maintaining the architecture (such as databases and large-scale processing systems) for data ingestion, processing, and analytics.,Data Architects: These are professionals who design the blueprint for managing data across the organization. They work on designing data solutions that utilize Azure services effectively to meet both the technical and business requirements.,Data Professionals: This broad category includes anyone working with data in a technical capacity and looking to leverage Azure's data services for their data solutions. This could include database administrators, data analysts, and software developers with a focus on data.,IT Professionals: IT professionals who are not necessarily data specialists but are looking to expand their skills into the data engineering space can benefit from this course. This includes system administrators, software developers, and IT managers who need to understand how data solutions are designed and implemented on Azure.,Students and Recent Graduates: Those who are studying in fields related to computer science, data science, information technology, or similar areas and are looking to enter the workforce with a strong set of skills in cloud-based data solutions.

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8.14 GB | 17min 37s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

FileName :01 IMPORTANT - How we are going to approach the exam objectives.mp4 | Size: (6.37 MB)
FileName :02 Cloud Computing.mp4 | Size: (18.53 MB)
FileName :03 Introduction to Cloud Computing.mp4 | Size: (4.64 MB)
FileName :04 Introduction to Azure.mp4 | Size: (7.96 MB)
FileName :05 The Azure Free Account.mp4 | Size: (40.27 MB)
FileName :06 Creating an Azure Free Account.mp4 | Size: (56.11 MB)
FileName :07 Tour around the Azure Portal.mp4 | Size: (9.95 MB)
FileName :08 Disabling security defaults.mp4 | Size: (6.87 MB)
FileName :01 Understanding Data.mp4 | Size: (40.89 MB)
FileName :02 The different data formats.mp4 | Size: (34.61 MB)
FileName :03 We need different types of storage services.mp4 | Size: (11.95 MB)
FileName :04 Lab - Creating an Azure Storage Account.mp4 | Size: (18.02 MB)
FileName :05 Lab - Working with the Azure Storage account.mp4 | Size: (40.8 MB)
FileName :06 Example of an application accessing data.mp4 | Size: (22.35 MB)
FileName :08 Thoughts on Udemy storing data.mp4 | Size: (19.71 MB)
FileName :09 The Azure Data Lake Gen2 storage account.mp4 | Size: (20.71 MB)
FileName :10 Lab - Azure Data Lake Gen-2 storage accounts.mp4 | Size: (6.26 MB)
FileName :11 Lab - Uploading data to Azure Data Lake Gen2.mp4 | Size: (20.11 MB)
FileName :12 Getting PowerBI.mp4 | Size: (14.04 MB)
FileName :13 Using PowerBI to view your data.mp4 | Size: (16.06 MB)
FileName :02 Section Introduction.mp4 | Size: (6.46 MB)
FileName :03 The Azure SQL database service.mp4 | Size: (6.72 MB)
FileName :04 Lab - Setting up a new Azure SQL database.mp4 | Size: (23.2 MB)
FileName :05 Lab - Installing Azure Data Studio.mp4 | Size: (8.98 MB)
FileName :06 Lab - Using Azure Data Studio on macOS.mp4 | Size: (4.94 MB)
FileName :07 Lab - T-SQL - SELECT clause.mp4 | Size: (22.62 MB)
FileName :08 Lab - T-SQL - WHERE clause.mp4 | Size: (10.48 MB)
FileName :09 Lab - T-SQL - ORDER BY clause.mp4 | Size: (3.72 MB)
FileName :10 Lab - T-SQL - Aggregate Functions.mp4 | Size: (7.8 MB)
FileName :11 Lab - T-SQL - GROUP BY clause.mp4 | Size: (5.78 MB)
FileName :12 Lab - Using PARTITION BY.mp4 | Size: (29.97 MB)
FileName :13 Lab - LEAD and LAG functions.mp4 | Size: (22.68 MB)
FileName :14 Lab - WITH Clause.mp4 | Size: (10.35 MB)
FileName :15 Lab - T-SQL - Create Table command.mp4 | Size: (18.48 MB)
FileName :16 Lab - T-SQL - Foreign Key constraints.mp4 | Size: (59.73 MB)
FileName :02 Section Introduction.mp4 | Size: (15.25 MB)
FileName :03 A quick view on the costing aspect when using Azure Synapse.mp4 | Size: (9.45 MB)
FileName :04 What have we seen so far.mp4 | Size: (17.94 MB)
FileName :05 A data warehouse.mp4 | Size: (18.22 MB)
FileName :06 Welcome to Azure Synapse Analytics.mp4 | Size: (3.64 MB)
FileName :07 Lab - Let's create a Azure Synapse workspace.mp4 | Size: (14.34 MB)
FileName :08 About the serverless SQL pool.mp4 | Size: (9.27 MB)
FileName :09 Let's open up some data.mp4 | Size: (80.16 MB)
FileName :10 Quick note on Microsoft Entra ID and permissions.mp4 | Size: (26.72 MB)
FileName :11 Lab - Using External tables - CSV - Part 1.mp4 | Size: (57.67 MB)
FileName :12 Lab - Using External tables - CSV - Part 2.mp4 | Size: (32.67 MB)
FileName :13 Lab - External Tables - Parquet file.mp4 | Size: (45.15 MB)
FileName :14 Lab - External Tables - Multiple Parquet files.mp4 | Size: (36.01 MB)
FileName :15 Lab - OPENROWSET - JSON files.mp4 | Size: (53.16 MB)
FileName :16 The dedicated SQL pool.mp4 | Size: (5.58 MB)
FileName :17 Lab - Creating a SQL pool.mp4 | Size: (9.29 MB)
FileName :18 Lab - SQL Pool - External Tables - CSV.mp4 | Size: (69.18 MB)
FileName :19 Lab - SQL Pool - External Tables - Parquet.mp4 | Size: (13.25 MB)
FileName :20 Lab - External table - Hidden files and folders.mp4 | Size: (23.45 MB)
FileName :21 Pausing the SQL Pool.mp4 | Size: (6.59 MB)
FileName :22 Lab - Loading data into a SQL pool using Polybase.mp4 | Size: (18.22 MB)
FileName :23 Lab - Loading data into a table - COPY Command - CSV.mp4 | Size: (62.21 MB)
FileName :24 Lab - Loading data into a table - COPY Command - Parquet.mp4 | Size: (10.33 MB)
FileName :25 Lab - Loading data - Pipelines - Storage accounts.mp4 | Size: (45.74 MB)
FileName :26 Lab - Loading data - Pipelines - Azure SQL database.mp4 | Size: (56.8 MB)
FileName :27 Designing a data warehouse.mp4 | Size: (12.05 MB)
FileName :28 Fact and Dimension Tables.mp4 | Size: (12.38 MB)
FileName :29 Lab - Building a Fact Table.mp4 | Size: (73.11 MB)
FileName :30 Lab - Building a dimension table.mp4 | Size: (29.05 MB)
FileName :31 Lab - Transfer data to our SQL Pool.mp4 | Size: (36.96 MB)
FileName :32 Lab - Using Power BI for Star Schema.mp4 | Size: (27.86 MB)
FileName :33 Understanding Azure Synapse Architecture.mp4 | Size: (28.06 MB)
FileName :34 Understanding table types.mp4 | Size: (25.69 MB)
FileName :35 Lab - Creating Hash-distributed Tables.mp4 | Size: (49.76 MB)
FileName :36 Lab - Creating Replicated Tables.mp4 | Size: (7.52 MB)
FileName :37 Fact as Hash and Dimensions as Replicate.mp4 | Size: (46.77 MB)
FileName :38 More on designing tables.mp4 | Size: (33.25 MB)
FileName :39 Lab - Surrogate keys for dimension tables.mp4 | Size: (47.75 MB)
FileName :40 Slowly Changing dimensions.mp4 | Size: (9.95 MB)
FileName :41 Indexes in Azure Synapse.mp4 | Size: (34.79 MB)
FileName :42 Which Load Method to use.mp4 | Size: (6.71 MB)
FileName :43 Partitions in Azure Synapse.mp4 | Size: (17.51 MB)
FileName :44 Lab - Creating a table with partitions.mp4 | Size: (30.29 MB)
FileName :45 Lab - Switching partitions.mp4 | Size: (28.16 MB)
FileName :46 Lab - CASE statement.mp4 | Size: (9.23 MB)
FileName :47 What about the Spark Pool.mp4 | Size: (26.85 MB)
FileName :48 Using the dedicated SQL pool.mp4 | Size: (23.45 MB)
FileName :01 Section Introduction.mp4 | Size: (3.73 MB)
FileName :03 Costing aspect when it comes to Azure Data Factory.mp4 | Size: (6.15 MB)
FileName :04 Extract, Transform and Load.mp4 | Size: (11.83 MB)
FileName :05 What is Azure Data Factory.mp4 | Size: (25.35 MB)
FileName :06 Starting with Azure Data Factory.mp4 | Size: (5.84 MB)
FileName :07 Lab - Azure Data Lake to Azure Synapse.mp4 | Size: (64.09 MB)
FileName :08 Lab - Generating a Parquet file.mp4 | Size: (12.55 MB)
FileName :09 Review on what has been done so far.mp4 | Size: (7.42 MB)
FileName :10 Lab - Creating a pipeline from scratch - CSV to Parquet.mp4 | Size: (42.33 MB)
FileName :11 Lab - Creating a pipeline from scratch - Parquet to Synapse.mp4 | Size: (66.75 MB)
FileName :12 So if you want to change the source data type.mp4 | Size: (34.84 MB)
FileName :13 About the Copy data activity.mp4 | Size: (4.59 MB)
FileName :14 Lab - Transfering data from our Azure SQL database.mp4 | Size: (31.76 MB)
FileName :15 Mapping Data Flow.mp4 | Size: (25.85 MB)
FileName :16 Lab - Mapping Data Flow - Fact Table.mp4 | Size: (84.44 MB)
FileName :17 Lab - Pipeline - Mapping data flows.mp4 | Size: (63.07 MB)
FileName :18 Lab - Mapping Data Flow - Dimension Table - DimCustomer.mp4 | Size: (71.14 MB)
FileName :19 Lab - Mapping Data Flow - Dimension Table - DimProduct.mp4 | Size: (41.32 MB)
FileName :20 If you want to drop the table data.mp4 | Size: (20.15 MB)
FileName :21 Lab - Derived Column.mp4 | Size: (18.18 MB)
FileName :22 Lab - Surrogate Keys - Dimension tables.mp4 | Size: (23.05 MB)
FileName :23 Data Flow Debug feature.mp4 | Size: (21.19 MB)
FileName :24 Lab - Azure Data Factory - Cache Sink - Setup.mp4 | Size: (75.6 MB)
FileName :25 Lab - Azure Data Factory - Cache Sink - Implementation.mp4 | Size: (157.77 MB)
FileName :26 Lab - Generating JSON data.mp4 | Size: (51.08 MB)
FileName :27 Lab - Loading JSON into SQL Pool.mp4 | Size: (56.23 MB)
FileName :28 Lab - Processing JSON Arrays.mp4 | Size: (35.56 MB)
FileName :29 Lab - Processing JSON objects.mp4 | Size: (24.75 MB)
FileName :30 Self-Hosted Integration Runtime.mp4 | Size: (7.29 MB)
FileName :31 Lab - Self-Hosted runtime - Building the machine.mp4 | Size: (26.62 MB)
FileName :32 Lab - Self-Hosted runtime - Setting up a web server.mp4 | Size: (17.71 MB)
FileName :33 Lab - Self-Hosted runtime - Installing the runtime.mp4 | Size: (53.2 MB)
FileName :34 Lab - Self-Hosted Runtime - Copy Activity.mp4 | Size: (36.15 MB)
FileName :35 Lab - Self-Hosted Runtime - Mapping Data Flow.mp4 | Size: (167.52 MB)
FileName :36 Lab - Conditional Split.mp4 | Size: (35.58 MB)
FileName :37 Using Aggregate Transformation.mp4 | Size: (4.7 MB)
FileName :38 About Schema Drift.mp4 | Size: (17.37 MB)
FileName :39 Lab - Get Metadata Activity.mp4 | Size: (12.6 MB)
FileName :40 Lab - For Each Activity.mp4 | Size: (75.16 MB)
FileName :41 Quick view on the code part.mp4 | Size: (3.18 MB)
FileName :42 Lab - Using the Stored Procedure Activity.mp4 | Size: (17.74 MB)
FileName :43 Lab - Using the Lookup Activity.mp4 | Size: (49.31 MB)
FileName :44 Lab - Running a pipeline based on a storage event.mp4 | Size: (45.17 MB)
FileName :45 Lab - Running a pipeline based on a schedule.mp4 | Size: (31.9 MB)
FileName :46 Lab - Running a pipeline based on a tumbling window.mp4 | Size: (72.85 MB)
FileName :47 Azure Data Factory and Git.mp4 | Size: (2.95 MB)
FileName :48 Integrating Azure Data Factory and GitHub.mp4 | Size: (17.68 MB)
FileName :49 Azure Data Factory - Git - Creating a pipeline.mp4 | Size: (69.09 MB)
FileName :50 Azure Data Factory - Git - Pull request.mp4 | Size: (50.15 MB)
FileName :02 Costing aspect when it comes Streaming services.mp4 | Size: (4.43 MB)
FileName :03 Batch and Real-Time Processing.mp4 | Size: (22.11 MB)
FileName :04 What are Azure Event Hubs.mp4 | Size: (17.07 MB)
FileName :05 Lab - Creating an instance of Event hub.mp4 | Size: (15.24 MB)
FileName :06 Lab - Azure Event Hubs - Sending data.mp4 | Size: (41.2 MB)
FileName :07 What is Azure Stream Analytics.mp4 | Size: (4.77 MB)
FileName :08 Lab - Creating a Stream Analytics job.mp4 | Size: (5.94 MB)
FileName :09 Lab - Azure Stream Analytics - Defining the input.mp4 | Size: (28.48 MB)
FileName :10 Lab - Azure Stream Analytics - Defining the output.mp4 | Size: (28.62 MB)
FileName :11 Lab - Azure Stream Analytics - Defining the query.mp4 | Size: (54.94 MB)
FileName :12 Review on what we have seen so far.mp4 | Size: (21.54 MB)
FileName :13 Lab - Reading Blob data.mp4 | Size: (100.06 MB)
FileName :14 Lab - Sending Diagnostic log data.mp4 | Size: (74.93 MB)
FileName :15 Lab - Formulating our query.mp4 | Size: (203.91 MB)
FileName :16 Lab - Azure Event Hubs Capture.mp4 | Size: (82.32 MB)
FileName :17 Debugging your jobs.mp4 | Size: (19.08 MB)
FileName :18 About Windowing functions.mp4 | Size: (9.38 MB)
FileName :19 Lab - Tumbling Window - Setup.mp4 | Size: (46.56 MB)
FileName :20 Lab - Tumbling Window - Implementation.mp4 | Size: (180.01 MB)
FileName :21 Having multiple queries in the job.mp4 | Size: (16.41 MB)
FileName :22 Quick note on other windowing functions.mp4 | Size: (42.76 MB)
FileName :23 Lab - Reference data.mp4 | Size: (65.96 MB)
FileName :24 Lab - Reading Network Security Group Logs - Server Setup.mp4 | Size: (23.63 MB)
FileName :25 Lab - Reading Network Security Group Logs - Enabling NSG Flow Logs.mp4 | Size: (26.83 MB)
FileName :26 Understanding the NSG Flow Log structure.mp4 | Size: (44.98 MB)
FileName :27 Starting with the query.mp4 | Size: (44.56 MB)
FileName :28 Formulating our query.mp4 | Size: (77.54 MB)
FileName :29 Finalizing our query.mp4 | Size: (137.53 MB)
FileName :30 Signing up for Power BI service.mp4 | Size: (71.71 MB)
FileName :31 Lab - Power BI Output.mp4 | Size: (12.77 MB)
FileName :32 Using Azure Data Factory - Mapping Data Flow.mp4 | Size: (114.14 MB)
FileName :33 Using Azure Data Factory - Pipelines.mp4 | Size: (64 MB)
FileName :34 Common query patterns.mp4 | Size: (15.7 MB)
FileName :02 Why Spark.mp4 | Size: (12.57 MB)
FileName :03 Spark Architecture.mp4 | Size: (12.39 MB)
FileName :04 Installing Spark - Virtual Machine setup.mp4 | Size: (9.82 MB)
FileName :05 Installing Spark - Implementation.mp4 | Size: (43.46 MB)
FileName :07 Running Python on Spark.mp4 | Size: (15.99 MB)
FileName :09 Using Notebooks.mp4 | Size: (23.08 MB)
FileName :11 Python - Data types.mp4 | Size: (5.63 MB)
FileName :12 Python - List collection.mp4 | Size: (6.62 MB)
FileName :13 Spark - Python - Data Frames.mp4 | Size: (20.28 MB)
FileName :14 Spark - Python - Data Frames from file.mp4 | Size: (7 MB)
FileName :15 Using Scala in notebooks.mp4 | Size: (17.61 MB)
FileName :17 Scala - Using Variables.mp4 | Size: (3.02 MB)
FileName :18 Spark - Scala - Data Frames.mp4 | Size: (9.56 MB)
FileName :19 Spark - Scala - Data Frame from file.mp4 | Size: (16.01 MB)
FileName :02 Azure Synapse - Spark Pool - Concepts.mp4 | Size: (6.25 MB)
FileName :03 Lab - Azure Synapse - Creating a Spark pool.mp4 | Size: (9.55 MB)
FileName :04 Spark Pool - Starting out with Notebooks - Scala.mp4 | Size: (20.29 MB)
FileName :05 Spark Pool - Starting out with Notebooks - Python.mp4 | Size: (7.23 MB)
FileName :06 Lab - Spark Pool - Load data.mp4 | Size: (79.89 MB)
FileName :07 Lab - Spark Pool - Working with data.mp4 | Size: (49.56 MB)
FileName :08 Lab - Spark Pool - Grouping data.mp4 | Size: (6.38 MB)
FileName :09 Lab - Spark Pool - Saving to a delta table.mp4 | Size: (37.41 MB)
FileName :10 Lab - Spark Pool - Write data to Azure Synapse.mp4 | Size: (141.4 MB)
FileName :11 Lab - Spark Pool - Shared tables.mp4 | Size: (34.79 MB)
FileName :12 Azure Synapse - Database templates.mp4 | Size: (16.9 MB)
FileName :14 What is Azure Databricks.mp4 | Size: (7.62 MB)
FileName :15 Concepts with Azure Databricks.mp4 | Size: (5.17 MB)
FileName :16 Lab - Creating a workspace.mp4 | Size: (4.93 MB)
FileName :17 Lab - Creating a cluster.mp4 | Size: (22.08 MB)
FileName :18 Note on Compute for Azure Databricks.mp4 | Size: (7.99 MB)
FileName :19 Lab - Loading data from a file.mp4 | Size: (25.98 MB)
FileName :20 Lab - Reading from Azure Data Lake.mp4 | Size: (21.59 MB)
FileName :21 Lab - Working with data.mp4 | Size: (13.11 MB)
FileName :22 Lab - Group By and Visualizations.mp4 | Size: (11.94 MB)
FileName :23 Lab - Few functions on dates.mp4 | Size: (45.01 MB)
FileName :24 Lab - JSON-based files.mp4 | Size: (45.91 MB)
FileName :25 Lab - Saving to a table.mp4 | Size: (16.74 MB)
FileName :26 Lab - Using the COPY INTO command.mp4 | Size: (29.47 MB)
FileName :27 Lab - Streaming data from datalake.mp4 | Size: (44.73 MB)
FileName :28 Lab - Performing transformation on the streaming data.mp4 | Size: (42.13 MB)
FileName :29 Lab - Specifying the schema.mp4 | Size: (28.85 MB)
FileName :30 Lab - Writing data to Azure Synapse SQL Dedicated Pool.mp4 | Size: (28.61 MB)
FileName :31 Lab - Reading data from Azure Synapse.mp4 | Size: (55.36 MB)
FileName :32 Lab - Versioning of tables.mp4 | Size: (54.84 MB)
FileName :33 Lab - Running an automated job.mp4 | Size: (20.44 MB)
FileName :34 Lab - Azure Data Factory - Running a notebook.mp4 | Size: (32 MB)
FileName :35 Lab - Streaming from Azure Event Hub - Setup.mp4 | Size: (87.56 MB)
FileName :36 Lab - Streaming from Azure Event Hub - Implementation.mp4 | Size: (66.78 MB)
FileName :37 Deleting the Azure Databricks resource.mp4 | Size: (5.39 MB)
FileName :02 Authorization for Azure Data Lake Gen2.mp4 | Size: (7.05 MB)
FileName :03 Lab - Using the Azure Storage Explorer.mp4 | Size: (16.92 MB)
FileName :04 Azure Data Lake Gen 2 Security - Shared Access Signature.mp4 | Size: (18.44 MB)
FileName :05 Microsoft Entra ID.mp4 | Size: (13.75 MB)
FileName :06 Lab - Using Microsoft Entra ID - Creating a user.mp4 | Size: (27.22 MB)
FileName :07 Lab - Using Microsoft Entra ID - Using RBAC - Storage Blob Data Reader.mp4 | Size: (22.98 MB)
FileName :08 Lab - Using Microsoft Entra ID - Using RBAC - Using Reader role.mp4 | Size: (22.04 MB)
FileName :09 Lab - Using Access Control Lists.mp4 | Size: (102.25 MB)
FileName :10 Lab - Azure Synapse - Column-Level Security.mp4 | Size: (37.14 MB)
FileName :11 Lab - Azure Synapse - Row-Level Security.mp4 | Size: (44.74 MB)
FileName :12 Lab - Azure Synapse - Data Masking.mp4 | Size: (66.13 MB)
FileName :13 Azure Synapse - Dedicated Pool Encryption.mp4 | Size: (10.59 MB)
FileName :14 Azure Synapse Workspace Encryption.mp4 | Size: (35.06 MB)
FileName :15 Azure Synapse - Microsoft Entra ID.mp4 | Size: (8.06 MB)
FileName :16 Lab - Azure Synapse - Microsoft Entra ID - Setting the admin.mp4 | Size: (26.34 MB)
FileName :17 Lab - Azure Synapse - Microsoft Entra ID - Logging as the admin.mp4 | Size: (55.29 MB)
FileName :18 Lab - Azure Synapse - Microsoft Entra ID - Creating a user.mp4 | Size: (56.43 MB)
FileName :19 New - Lab - Azure Synapse - External tables - Microsoft Entra ID.mp4 | Size: (30.98 MB)
FileName :20 Azure Storage Accounts - Network and Firewall.mp4 | Size: (6.48 MB)
FileName :21 Azure Storage Accounts - Network & Firewall settings -Creating a virtual machine.mp4 | Size: (103.01 MB)
FileName :22 Azure Storage Accounts - Network and Firewall settings - Service endpoint.mp4 | Size: (10.95 MB)
FileName :23 About Managed Identities.mp4 | Size: (3.38 MB)
FileName :24 Azure Synapse - Managed Identity connectivity.mp4 | Size: (42.02 MB)
FileName :25 Azure Synapse - Data Discovery and Classification.mp4 | Size: (26.55 MB)
FileName :26 Azure Data Factory - Encryption.mp4 | Size: (17.44 MB)
FileName :27 Lab - Azure Databricks - Using Secret Scope - Setup.mp4 | Size: (42.83 MB)
FileName :28 Lab - Azure Databricks - Secret Scope - Implementation.mp4 | Size: (93.49 MB)
FileName :01 Microsoft Purview.mp4 | Size: (7.82 MB)
FileName :02 Creating a Microsoft Purview account.mp4 | Size: (4.86 MB)
FileName :03 Microsoft Purview - Azure Data Lake.mp4 | Size: (21.46 MB)
FileName :04 Microsoft Purview - Azure Synapse.mp4 | Size: (22.63 MB)
FileName :05 Microsoft Purview - Azure Data Factory.mp4 | Size: (34.75 MB)
FileName :06 Best practices for data storage - Azure Data Lake.mp4 | Size: (9.79 MB)
FileName :07 Azure Data Lake Gen2 - Access tiers.mp4 | Size: (19.44 MB)
FileName :08 Azure Data Lake Gen2 - Look at Access tiers.mp4 | Size: (17.54 MB)
FileName :09 Azure Data Lake Gen2 lifecycle policies.mp4 | Size: (7.5 MB)
FileName :10 View on Azure Monitor.mp4 | Size: (4.97 MB)
FileName :11 Lab - Azure Data Factory - Alert Rules.mp4 | Size: (19.96 MB)
FileName :12 Azure Data Factory Logging.mp4 | Size: (26.39 MB)
FileName :13 Azure Data Factory - Annotations.mp4 | Size: (7.97 MB)
FileName :14 Azure Data Factory - Note - Scaling up the runtime.mp4 | Size: (19.49 MB)
FileName :15 Azure Data Factory - Note - Troubleshooting.mp4 | Size: (14.05 MB)
FileName :16 Azure Synapse Monitoring - Quick Overview.mp4 | Size: (15.14 MB)
FileName :17 Azure Synapse - System Views.mp4 | Size: (17.97 MB)
FileName :18 Azure Synapse - Workload Management.mp4 | Size: (16.09 MB)
FileName :19 Azure Synapse - Result set caching.mp4 | Size: (10.77 MB)
FileName :20 Note - Azure Synapse - Data Skew.mp4 | Size: (16.59 MB)
FileName :21 Azure Synapse - Log Analytics.mp4 | Size: (19.84 MB)
FileName :22 Azure Stream Analytics - Optimization.mp4 | Size: (10.49 MB)
FileName :23 Azure Stream Analytics - The importance of time.mp4 | Size: (17.11 MB)
FileName :24 Azure Databricks - Monitoring.mp4 | Size: (13.86 MB)

]
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