Multiple Concept & hands-on Lab on Fabric Spark Pool, PySpark, Version Control, Security, Governance, Eventstream, KQL

What you'll learn

Official exam topics with CONCEPT and Hands-On Lab/DEMO to pass DP-700 Exam; topics includes
How to configure Spark workspace settings in Fabric
How to configure security and governance in Fabric
How to transform data by using PySpark, KQL (Kusto Query Language), SQL in Fabric
How to configure version control in Fabric
How to create and configure deployment pipelines in Fabric
How to design and implement loading patterns
How to optimize a lakehouse table
How to ingest data by using pipelines
and many others concept and demo session mentioned in

Description

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Requirements

Most important requirement is your desire to learn consistently to upskill yourself.
You will learn everything from basics/fundamentals; knowing basic python/pyspark/SQL is plus not mandatory.
For hands-on practice which is always recommended in course, you need Fabric capacity or trial capacity; in course you will get step by step guide to get free trial capacity to perform hands-on practice.

Description

This course covers official exam syllabus and Study Guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric; For exam topics, it contains concept class followed by related hands-on Lab/demo session immediately to visualize how to use or implement it for effective learning.. For each of the important topic for Exam DP-700, there are Multiple Concept & hands-on Lab/demo session.Important topic can be : Fabric Spark Pool, PySpark, Version Control, Security, Governance, Eventstream, KQL (Kusto Query Language)IMPORTANT NOTE: As per Official update from Microsoft, "Microsoft Certified: Azure Data Engineer Associate Certification and its related Exam DP-203: Data Engineering on Microsoft Azure will all be retired on March 31, 2025."So Exam DP-700 & Certification is VERY IMPORTANT as it is future of Data Engineering certification from Microsoft: DP-700: Microsoft Fabric Data Engineer AssociateThis EXAM DP-700 contains 3 sections having equal priority:Implement and manage an analytics solution (30–35%)Ingest and transform data (30–35%)Monitor and optimize an analytics solution (30–35%)and each of these section contains multiple subsection and each subsection contains several topics.What you will learn from this course? This course will help you to understand concept of each topic and how to use or implement required Fabric item in project. Hence it will help only to pass DP-700 exam & become Microsoft Certified: Fabric Data Engineer Associate but also to become good Data Engineer in Microsoft Fabric to continue in Data Engineering career. For Configure Spark workspace settings: Learn concept and fundamentals/Architecture of Spark Pool in fabric along with Hands-on lab/demo session - What is Starter Pool ?How to modify Starter Pool in Fabric and how its related Fabric settings impacts Starter Pool?What is custom Spark Pool? How to create custom Spark pool in Fabric and how its related settings impacts custom Spark pool What is Environment and what are their features and related settings that affects these.How to create Environment and how its related settings impacts compute configuration of Environment.What is the impact when these pool/environment are made as default pool in workspace.For Configure security and governance in Fabric, you will learn concept using diagram on the different data security layers-What is workspace-level access controls?

Overview

& ConceptHow to implement workspace-level access controls in Fabric through Hands on Lab/Demo.What is item-level access controls?

Overview

& ConceptHow to implement item-level access controls in Fabric through Hands on Lab/Demo.What is file-level access controls?

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& ConceptHow to implement file-level access controls in Fabric through Hands on Lab/Demo.What is object-level access controls?

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& ConceptHow to implement object-level access controls in Fabric through Hands on Lab/Demo.What is row-level access controls?

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& Concept - Row Level Security (RLS)How to implement row-level access controls in Fabric through Hands on Lab/Demo.What is column-level access controls?

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& Concept - Column Level Security (CLS)How to implement column-level access controls in Fabric through Hands on Lab/Demo.What is dynamic data masking in Fabric?

Overview

& Concept How to implement dynamic data masking in Fabric through Hands on Lab/Demo. For Transform data by using KQL (Ingest and transform batch data - Part 5) , through hands-on lab/demo you will learn KQL Fundamentals: Query Operator & | PipeKQL Fundamentals & Hands on Lab: Query Operator - Project , count, getschemaHow to translate SQL query to KQL QueryHow to find relevant data using distinct, take operator, Let statement in KQL How to find relevant data using Filter/Where in KQL How to find relevant data using Case (like if/then/elseif ) in KQL How to use KQL SearchHow to implement sorting records using Sort operatorHow to returns first N rows using top operator in KQL How to Create Columns using Extend operator in KQL How to Keep/Remove/Reorder Columns using KQL Project operators - project, project-away , project-keep,project-reorder, project-renameKQL join & best performanceHow to implement left right outer, Left semi join, Left anti join, Right semi join, Right anti join,full outer join in KQLHow to use summarize operator to perform Aggregation in KQLHow to perform Aggregation using KQL Aggregation functions Count() ,Countif(), sum() , sumif(), avg(), avgif() ,max(), maxif() ,min(), minif() How to perform KQL Aggregation (Group and aggregate data) - summarize by (Group and aggregate dаta:) - single aggregation, multiple aggregation (GROUP BY)For Transform data by using PySpark (Ingest and transform batch data - Part 3), through hands-on lab/demo you will learn How to use or implement select take using PySpark in Fabric How to implement Filter/Where transformation PySpark to clean dataHow to implement Drop, distinct, printschema using PySpark How to implement Sort()/OrderBy() to sort records using PySpark How to implement WithColumn, ColumnRenamed transformation using PySpark How to implement joins using PySpark How to implement Aggregations using PySpark How to implement Group and aggregate data using PySpark For Process data by using eventstreams (Ingest and transform streaming data) , through hands-on lab/demo you will learn How to perform Manage fields transformation in eventstreamsHow to perform filter transformation in eventstreamsHow to perform aggregation transformation in eventstreamsHow to perform group by transformation using tumbling window in eventstreams How to perform group by transformation using hopping window in eventstreams How to perform group by transformation using sliding window in eventstreams How to perform Expand transformation in eventstreamsHow to perform union transformation in eventstreamsHow to perform join transformation in eventstreamsFor Configure version control, you will learn What is Version control? Concept & Integration ProcessWhat are related Fabric/Git permission and settings (and Tenant settings ) required to configure version control in Fabric?Hands-on Lab/Demo: How to set up Azure Repo that to be used as part of version control configuration.Hands-on Lab/Demo: How to configure version control/ Git integration with Azure Repo from Fabric workspaceFor Implement database projects, you will learn What is Database projects - concept &

Overview

Database projects - Setup & Architecture for demoWhy we need SQL database projects?Hands-on Lab/Demo: How to implement database projects in FabricFor Create and configure deployment pipelinesWhat is deployment pipeline in Fabric?

Overview

Architecture of deployment pipeline for Demo & prerequisitesHands on Lab/Demo :How to Create and configure deployment pipelines Hands on Lab/Demo :How to assign workspace to respective stages and deploy content from one stage to next stage.For Configure domain workspace settings What is domain in Fabric ?What are the Delegated Setting for domainHands on Lab/Demo: how these delegated settings impacts domain in FabricFor SQL database projects:We will understand why we need SQL database projectsHow to Setup Demo components & understand through Architecture diagramHands on Lab/Demo - How to Implement database projects in FabricFor Configure security and governance in Fabric, you will learn concept & implementation of governance -What is sensitivity labels?

Overview

& ConceptSensitivity labels: related admin settings in FabricHow to apply sensitivity labels to items in Fabric?For Orchestrate processes, you will learn concept & implementation of Orchestrate processesHow to Choose between a pipeline and a notebook in Fabric?Design and implement schedules triggers - Design components for demoHow to implement schedules triggers in Fabric Data Factory pipeline.For Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressionData Factory pipeline good practiceWhat is Pipeline parameter and dynamic expression conceptHow to implement parameters and dynamic expression in pipelineHow to configure pipeline to retry if pipeline run failsHow to implement orchestration patterns with notebooks and pipelinesFor Design and implement loading patterns (Ingest and transform data), you will learnHow to design full and incremental data loads in FabricHow to implement full and incremental data loads in Fabric through hands-on lab/demoFor Ingest and transform batch data - part 1, you will learnhow to choose an appropriate data storehow to choose between dataflows, notebooks, and T-SQL for data transformationShortcuts

Overview

in fabricShortcuts type in FabricShortcuts folder structureHow to create and manage shortcuts to data in Fabric through hands-on lab/demoFor Ingest data by using pipelines (Ingest and transform batch data - Part 2), you will learnHow to design Ingest data by using pipelines into LakehouseHow to ingest data by using pipelines into LakehouseHow to design Ingest data by using pipelines into warehouseHow to ingest data by using pipelines into warehouseHow to design Ingest data by using pipelines into KQL DatabaseHow to ingest data by using pipelines into KQL DatabaseFor Transform data by using SQL (Ingest and transform batch data - Part 4), through hands-on lab/demo you will learn How to implement SQL top distinct keyword How to implement SQL Filter on dataHow to implement SQL Sort on dataHow to implement Case & create dynamic or computed columnHow to implement SQL Inner Join, left Join, right Join, outer JoinHow to implement Aggregation in SQLHow to implement SQL Group and aggregate dаta: Group by & Having Clause AggregationHow to create Create Stored ProcedureHow to transform the data using Stored Procedure activity in Data pipeline For Optimize a lakehouse table (Optimize performance - Part 1) , through hands-on lab/demo you will learn How to optimize a lakehouse table using Optimize command in FabricHow to optimize a lakehouse table using V-Order in FabricHow to optimize a lakehouse table using VACUUM command in FabricHow to optimize a lakehouse table using Optimizetwrite command in FabricHow to optimize a lakehouse table using Partition in FabricHow to optimize a lakehouse table using Table maintenance feature in Fabric

Who this course is for

If you are University Student who wants to start career as data engineer in Microsoft Fabric
Anyone who is working in any Cloud but wants to up-skill to work as data engineer in Microsoft Fabric
Anyone who is already working as Azure, AWS, GCP Data Engineer but want to work in Microsoft Fabric
any existing Data Architect who wants to to work in Microsoft Fabric
anyone who wants career in Data Engineering on Microsoft Fabric
If you want to add "Microsoft Certified: Fabric Data Engineer Associate" certification in resume.
Homepage:

https://www.udemy.com/course/exam-dp-700-fabric-data-engineer-associate-ultimate-guide/