DP-203: Data Engineer Associate Certification Preparation

Pass Azure DP-203: Learn Azure SQL Database, CosmosDB, Data warehouse, Data Lake, Data Factory, Data Bricks in 11 hours

3.65 (129 reviews)
Udemy
platform
English
language
IT Certification
category
632
students
12 hours
content
Mar 2023
last update
$64.99
regular price

What you will learn

You Will Master Azure Data Factory

How To Create Data Flows And Control Flows In Azure Data Factory

How To Use Parameters and Variables In Azure Data Factory

How To Implement Different Data Storage Solutions In Azure

implement a solution that uses Data Lake Storage Gen2

How To Create Azure SQL Databases

How To Configure Azure Data Bricks( A Service To Perform Big Data Analysis Using Azure Cloud)

How To Access Azure Data Lake From Azure DataBricks (With Python Code)

How To Connect To Azure SQL Data Base From On premise Using SSMS

How To Execute Query Against Azure SQL Data Base Using Azure Query Editor

How Migrate Data Base From On Premise To Azure Using SSMS

How To Move Data To Azure Data Lake From On-premise Using Storage Explore

How To Access On premise Environment Using Self Hosted Integration Run time From ADF

How To Create Data Set and Connections In ADF (Azure Data Factory)

How To Create Pipeline In Azure Data Factory (ADF)

How To Implement a solution that uses Azure Blob storage

Implement Storage Systems With High Availability, Disaster Recovery, And Global Distribution With Geo Replications

How To Create Elastic Pool And Deploy Multiple Data Bases In single Server

Provide Access To Data To Meet Security Requirements

How To Create Azure SQL Data Warehouse (Azure Synapse Analytics)

configure elastic pools, configure geo-replication, implement PolyBase

Learn to use PolyBase external tables to load data from Azure Data Lake Storage

Create database objects required to load from Data Lake Storage

Connect to a Data Lake Storage directory (With Credential and secret)

implement Copy Activity within Azure Data Factory, create linked services and datasets

Develop batch processing solutions using Data Factory and Azure Databricks

Create pipelines and activities, implement Mapping Data Flows in Azure Data Factory

Learn to implement Azure Databricks clusters creations

Learn to ingest data into Azure Databricks

Learn to implement Azure Databricks notebooks, jobs, and autoscaling,

Description

Microsoft Azure (formerly Windows Azure) is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers.

Azure Provides Three services:

  1. software as a service (SaaS),

  2. platform as a service (PaaS).

  3. infrastructure as a service (IaaS)


Azure supports many different programming languages, tools, and frameworks, including both Microsoft-specific and third-party software and systems. Azure was announced in October 2008, started with the codename "Project Red Dog", and released on February 1, 2010, as "Windows Azure" before being renamed "Microsoft Azure" on March 25, 2014.

In this course, you will learn :

  1. How To Use The Azure Data Factory.

  2. How To Use The Azure SQL Database.

  3. How To Use Azure Blob Storage.

  4. How To Use Azure Data Lake.

  5. How To Use Azure DataBricks.

  6. How To Use Different Azure Data Services In Different applications.

Exam DP-200: Implementing an Azure Data Solution:

Candidates for this exam must be able to implement data solutions that use the following Azure services:

  • Azure Cosmos DB.

  • Azure SQL Database.

  • Azure Synapse Analytics (formerly Azure SQL DW).

  • Azure Data Lake Storage.

  • Azure Data Factory.

  • Azure Stream Analytics.

  • Azure Databricks.

  • Azure Blob storage.

Topics We Cover In This Course:

  • Azure SQL Database.

  • Azure Cosmos DB.

  • Azure Data Lake Storage.

  • Azure Data Factory.

  • Azure Databricks.

  • Azure Blob storage.

  • Azure Synapse Analytics (formerly Azure SQL DW).


Upcoming Modules;

  • Azure SQL failover groups

  • Azure Data Lake Analytics

  • Introduction To Power BI

  • HDinsight.


Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.

Azure data engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

This course covers, how to provisioning data storage services like Azure SQL, Storage account, Data lakes. In the Azure Data factory section, we cover how to transform your data, identifying performance bottlenecks, and accessing external data sources including on-premise SQL server and file systems.


Azure Data Factory (ADF):

The Azure Data Factory service is a fully managed service for composing data storage, processing, and movement services into streamlined, scalable, and reliable data production pipelines. The Azure Data Factory (ADF) is a service designed to allow developers to integrate disparate data sources. ADF or Azure Data Factory is a platform somewhat like SSIS or Alteryx in the Azure environment to manage the data you have both on-prem and in the cloud.

It provides access to on-premises data with the help of a software. By using this link we could connect to the on-premise file system as well as to on-premise SQL databases. From Azure Data Factory, you could access almost all azure services without any difficulties.

Access to on-premises data is provided through a data management gateway that connects to on-premises SQL Server databases and we will show you how to install this software and how to connect your on-premises environment with Azure cloud.

If you ever created any data transfer activities in Azure, or in SSIS, you will find it a similar tool. If you use ADF, you could focus on your data—the serverless integration service does the rest.


Topics In Azure Data Factory:

  • Append Variable Activity

  • Execute Pipeline activity

  • ForEach activity

  • Get Metadata activity

  • If Condition activity

  • Lookup activity

  • Set variable activity

  • Until activity

  • Validation activity

  • Data Flow activity

  • Mapping data flow

    • Aggregate transformation.

    • Alter row transformation.

    • Conditional split transformation.

    • Derived column transformation.

    • Exists transformation.

    • Join transformation.

    • Lookup transformation.

    • The new branch mapping data flow transformation.

    • Select transformation.

    • Sink transformation.

    • Source transformation.

    • Azure Data Factory union transformation.

  • Parameterizing

  • Trigger In Azure Data Factory.

    • Manual Trigger.

    • Scheduled Trigger.

    • Tumbling window

    • Event Trigger.

      • Dynamic Data processing And Pipeline Execution Based on External Event.

  • and many more (with real-life scenarios). Check out our course descriptions for updated information.

SQL Database -Cloud Database as a Service:

Azure SQL Database is a fully managed relational database with built-in intelligence supporting self-driving features such as performance tuning and threat alerts. According to Wiki, Microsoft Azure SQL Database is a managed cloud database provided as part of Microsoft Azure. A cloud database is a database that runs on a cloud computing platform, and access to it is provided as a service. Managed database services take care of scalability, backup, and high availability of the database.

Azure SQL Database: Azure SQL Database is a relational database-as-a-service (DBaaS) based on the latest stable version of Microsoft SQL Server. It is a fully managed Platform as a Service (PaaS) Database Engine that handles most of the database management functions such as upgrading, patching, backups, and monitoring without user involvement.

In this course, we will show you how to launch the Azure SQL database in five minutes, with and without sample data.  We will show you, how to use SQL elastic pools.

Elastic pools help you manage and scale multiple Azure SQL databases. According to Azure documentation, SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. With an elastic pool, you determine the amount of resources that the elastic pool requires to handle the workload of its databases, and the amount of resources for each pooled database.

Azure SQL Database is always running on the latest stable version of the SQL Server Database Engine and patched OS with 99.99% availability.

The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price. By the end of this course, you will have a clear idea about how to configure SQL elastic pool.

Geo-Replication:

Active geo-replication is an Azure SQL Database feature that allows you to create readable secondary databases of individual databases on a SQL Database server in the same or different data center (region). We will show you how you could configure a Geo-replication and force failover to the secondary database manually.

Azure Cosmos DB:

Azure Cosmos DB is Microsoft's globally distributed, multi-model database service. With a click of a button, Cosmos DB enables you to elastically and independently scale throughput and storage across any number of Azure regions worldwide. In our course, we will see.

  • How you could create a cosmos DB account,

  • How to Create Databases inside your cosmosDB account

  • How to insert data into CosmsoDB containers.

  • How to Restive data that you saved in cosmos DB tables or containers by using SQL

Introduction to Azure Storage:

Azure Storage is Microsoft's cloud storage solution for modern data storage scenarios. Azure Storage offers a massively scalable object store for data objects, a file system service for the cloud, a messaging store for reliable messaging, and a NoSQL store.

In this course, we will cover how to create a storage account, how to create containers and file systems and how to upload data into these services and how to access these storage services from different azure data solutions services like data factory, data bricks, and SQL databases.

Azure Data Lake Storage:

Azure Data Lake Storage, is a fully-managed, elastic, scalable, and secure file system that supports HDFS semantics and works with the Hadoop ecosystem. ‎Azure Data Lake Storage Gen2 is a set of capabilities dedicated to big data analytics, built on Azure Blob storage. Data Lake Storage Gen2 is the result of converging the capabilities of our two existing storage services, Azure Blob storage and Azure Data Lake Storage Gen1.

In this course we will see, how to create data lakes, How to move CSV data from azure blob storage to azure data lake using azure data factory. How to read your data (Azure Data lake) using azure Databricks.

Azure Databricks:

Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build Bigdata applications. According to Databricks documents, Azure Databricks is a fast, easy, and collaborative Apache Spark™ based analytics platform optimized for Azure. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

In this course, we will show you how to configure Azure Data bricks, How to launch a cluster, how to create notebooks.

Azure Synapse Analytics (Azure SQL Data Warehouse):

Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. Azure Synapse is Azure SQL Data Warehouse evolved. In this course, you will learn how to create an Azure SQL pool, access a data lake storage account (how to use PolyBase external tables to load data from Azure Data Lake Storage). Will demonstrate how to create a master key and database scoped credential. How to create external tables and external data sources. Finally, we will see how to load data into Azure Data Warehouse from an external table by using the create table as a select command.

Demo 1:

  1. Create a Pipeline in Azure Data Factory.

  2. Create Input Connections to a source.

  3. Create Input Data Set.

  4. Create Output Connections to destinations.

  5. Create an Output Data set.

  6. Create A copy Activities to copy data from on-premise to Azure Blob storage.

  7. Create A copy Activities to copy data from Blob to Azure SQL Database.

  8. Create A copy Activities to copy data from on-premise to SQL Database.

  9. Run Your Copy Activities and validate all the settings

Demo-2:

  1. Migrate Data from On-premise SQL Server to Azure SQL database Without any external services.

Demo 3:

  1. Create An Azure DataBricks.

  2. Connect To Azure Data Lake.

  3. Create a Cluster To Run our notebook

  4. Configure Azure Databricks Data lake configurations.

  5. Assign permission to your external Applications.

  6. Read CSV data saved inside Azure Data lake using a Python notebook.

Demo 4:

  1. Create Your First Data Flow In Azure Data Factory.

  2. Configure The Source Data flow.

  3. Learn To use Filter Conditions.

  4. Learn To configure Sink (Destinations) in Azure.

  5. Run your Azure Data flow and copy data from Azure Blob Data Store And Save filtered result In Azure Data lake.

Demo 5:

  1. Access On-premise SQL Server.

  2. Create Different  Data Set by Executing Custom Stored Procedure By Passing Dynamic Parameter

  3. Save This Data Set Into Data Lake By Creating Custom Filename.

  4. Trigger this action Azure Data Factory.

Demo 6:

  1. Create dynamic result with help of custom parameters and in for each activity.

  2. Save the result into Azure Data lake with a dynamic name.

Demo 7;

Run your activities N times with the help of Until loop (Do loop concepts of programming language)

Demo 8:

  • Create an Azure Data warehouse Pool by using Azure Portal.

  • Create an Azure Data warehouse by using SQL statements from SSMS.

  • Connect and execute SQL statements against  Azure SQL Data warehouse.

  • Execute SQL statements against Data in Azure Data lake using PolyBase external tables And load data from Azure Data Lake Storage into Azure Data warehouse.

  • Learn to use external tables and external data sources in Azure SQL Data warehouse.



Content

Introduction To Azure

How To Create A Azure Account

Create Storage Resources In Azure

Topics Of This Module
Create SQL Databases
Connect to a data base
Create Storage Account
Create Azure Data Factory
Copy Data From On premise To Storage Account With Data Factory
Copy Data From On premise To Storage Account With Data Factory 2
Copy Data From Storage Account To Azure SQL Database

Mastering Azure Data Factory Part 1

Introduction
Components Of Azure Data Factory
Create Resource For Demo 2 (Azure Data Factory & Azure SQL)
Create Pipeline (Copy On Premise Data To Azure)
Create Linked Service (Self Hosted - Input Connection)
Create First Data Set (input Data Set)
Create Second Linked Service (Auto Resolve Integration Run time ) Output Connect
Create Second Data Set (Output Data Set)
Create Azure Data Factory Copy Activity (Execute Our Demo Project)

Introduction To Azure SQL

Introduction
Migrating On Premise Data Base To Azure Data Base
Create Azure SQL DB In Elastic Pool
Deploy More Databases Into Elastic pool
Delete Elastic Pool

Introduction To Azure Data Lake

Introduction
Create A Data Lake using Azure Portal
Install Azure Storage Explorer
Upload Data Into Azure Data Lake Using Azure Storage Explorer

Introduction To Azure Databricks

How To Create Azure DataBricks Works Space
How To Create A Cluster Inside An Azure DataBricks
Connect To Azure Data Lake From Azure Data Bricks-Important Configuration-Part 1
Assign Proper Permission Using Access Control And Key Vault-Connection Part 2
Connect To Azure Data Lake From Azure Data Bricks Part 3 (Read A CSV File)

Mastering Azure Data Factory Part 2 (Building Data Flows In ADF)

introduction To Data Flows in Azure Data Factory
Create Proper Connection (To Azure Data Lake From Data Factory)
Run Your First Data Flow Transformation In Azure Data Factory
Configuration In Source Data Flow transformations
Introduction To Join & Union Data Transformation
Union Data Flow Transfer In ADF Part 1
Union Data Flow Transfer In ADF Part 2
Join Data Flow Transform In ADF
Select Data Flow Transformations
Derived Column Data Transfer In ADF

Screenshots

DP-203: Data Engineer Associate Certification Preparation - Screenshot_01DP-203: Data Engineer Associate Certification Preparation - Screenshot_02DP-203: Data Engineer Associate Certification Preparation - Screenshot_03DP-203: Data Engineer Associate Certification Preparation - Screenshot_04

Reviews

İbrahim
April 16, 2023
İ took different courses about DP-203. This one is the best for content. But 3 expensive headphones and 7+1 sound system can't solve sound problem. İf i listen it max. volume, the music in begin and end of the videos make an earthquake at home, i cannot hear anything with low sound also. I thought i can fallow it easier with subtitles but there is no option. I believe that i will pass the exam with that course but i can lose my ears till complete it.
Bsheridanjones
November 24, 2021
The sound isn't very clear and no transcript is available. There is some redundant content that could be removed, and sections that duplicate other sections that should be cleaned up. Still the content is sound but this clean-up would improve the delivery.
Marcos
January 20, 2021
I was not able to see the closed caption option enabled. It is fundamental, since I can´t understand what the instruction is talking about.
Jinesh
August 13, 2020
Options are not explained in details, Also let say if we are using cosmos DB then why and when we need to use cosmos DB and not other data solution should be explained similarly when we create a resource a more explanation of options is not provided for example there are 3 versions of storage then explanation and difference should be explained between three versions. not happy with in depth details, the course just teach you just the very basics of every thing, and i don't think that is sufficient for DP200 exam.
Debs
August 3, 2020
Presentation of the course could have been better. Topics like stream processing and log analytics can be added in the future revisions.
Miguel
June 15, 2020
His english is horrible !!! there is no even a transcript to read what he is saying !! the pronunciation is so confusing !! At least put some CC in the video so many of us who don't speak english fluently can understand what's he is saying ! So sorry for my words, but I feel cheated and frustrated
Olivier
May 20, 2020
Dommage que la démo ne soit pas sous-titrée. Too bad the training is not subtitled. Not everyone speaks fluent English
Said
May 6, 2020
The content is comprehensive but has a few repeating sections, but the music is very annoying & the instructors voice is muffled specially when watching at higher speed, both issues made watching this unpleasant.
Abu
April 19, 2020
Simple and easy to follow. Multiple demos are there. I think , we need to learn some theory in order to pass the exam. Please add no SQL
Chandrashekar
April 13, 2020
learning whole lot of new things. Few Videos are cropped off and audio could have better in most of the videos.
Sajeeb
April 8, 2020
This course covers multiple practical activities about azure datafactories. Please include some theory.
Vincent
April 7, 2020
Ben nog bezig, maar de geluidskwaliteit is dramatisch slecht en dat ligt niet aan mijn hardware, want bij andere cursussen heb ik dat niet. Hij heeft al een zwaar accent in het Engels, zoals ik ook vaak bij mensen uit India heb, wat het stuk lastiger maakt alles goed te verstaan, maar door de slechte geluidskwaliteit is het echt een probleem, dat ik hem de halve tijd niet versta. Inhoudelijk is het een goede cursus, wat natuurlijk uiteindelijk het belangrijkste is, maar dit is echt een serieus verbeterpunt.

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