Azure Data Engineer Certification Training
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Learners : 1080
About Course
An Azure Data Engineer is a specialized IT professional who designs, builds, and manages data solutions using Microsoft’s Azure cloud platform. They focus on integrating, transforming, and consolidating data from various sources into formats suitable for analytics and other data-driven applications. Enroll today to get certified.
Azure Data Engineer Training Course Syllabus
✔ Introduction to SQL
✔ Set Operations
✔ SQL Operators
✔ Aggregate Functions and Group by Clause
✔ Updating and Deleting Data in Existing Table (DML)
✔ Views
✔ Assignment-I
✔ Assignment-II
✔ Advanced Queries
✔ Integrity Constraints
✔ Analytical Queries
✔ Understanding ROLLUP and CUBE
✔ Understanding Joins
✔ Scalar Subqueries
✔ Case Expressions
✔ Multitable Insert and Merge
✔ Regular Expression
✔ Statistical Functions
✔ Model Queries
✔ SQL Quiz
✔ Introduction to Python and Writing Our First Python Program
✔ DataTypes in Python
✔ Operators in Python
✔ Input and Output
✔ Control Statements in Python
✔ Arrays in Python
✔ Strings and Characters
✔ Functions
✔ Lists and Tuples
✔ Dictionaries
✔ Introduction to OOPS
✔ Classes and Objects
✔ Inheritance and Polymorphism
✔ Abstract Classes and Interfaces
✔ Exceptions
✔ Files in Python
✔ Regular Expressions in Python
✔ Data Structures in Python
✔ Date and Time
✔ Threads
✔ GUI
✔ Networking in Python
✔ Database Connectivity
✔ Introduction to Azure
✔ Azure Portal Overview
✔ Creating an Azure Data Factory
✔ Creating an Azure Data Lake
✔ Creating Storage Accounts
✔ Linked Services
✔ Datasets
✔ Pipelines
✔ Copy Activity
✔ Databricks Activity
✔ Get Metadata Activity
✔ General Activities
✔ Move and Transform activities
✔ Iteration and conditional activities
✔ Azure Functions
✔ DataFlows
✔ Integration RunTimes
✔ Triggers
✔ Real-time Scenarios
✔ Spark and Data Bricks Architecture
✔ Mounting ADLS with Azure Data Bricks
✔ RDDs
✔ DataFrames
✔ Parallelize () and repartition
✔ Struct Type and Struct Field
✔ Select, With column and with column renamed
✔ Collect
✔ UDF
✔ Join()
✔ Dataframe Operations
✔ Reading different Files
✔ Pyspark SQL Functions
✔ Mounting to Databricks
✔ Cluster Types and setting up the clusters
✔ Pyspark Built-in Functions
✔ Cosmos DB connectivity with Azure Databricks
✔ Real-Time Scenarios and Project
✔ Real-Time Scenarios and Interview Questions for SQL
✔ Real-Time Scenarios and Interview Questions for Python
✔ Real-Time Scenarios and Interview Questions for Pyspark
✔ Real-Time Scenarios and Interview Questions for ADF and ADB
✔ Project Explanation
| Live Instructor Based Training With Software |
| Lifetime access and 24×7 support |
| Certification Oriented content |
| Hands-On complete Real-time training |
| Get a certificate on course completion |
| Flexible Schedules |
| Live Recorded Videos Access |
| Study Material Provided |
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Instructor-led training, Real-time projects
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Certification Guidance.
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Complete set of live-online training sessions recorded videos.
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Learn As A Full Day Schedule With Discussions, Exercises,
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Azure Data Engineer Training FAQ'S
The focus of Azure Data Engineer is on using data collection and analysis. The data acquired from various sources is basically unprocessed data. Azure Data Engineer aids in converting useless data into informative data. In a word, it is the procedure for transforming, cleaning, profiling, and aggregating huge data sets.
By learning through VISWA Online Trainings, advance in your job.
A corporate tool called Azure Synapse shortens the time it takes to make a decision across different data networks and data storage. The style of SQL (Structured Query Language) technology used in business data warehousing, Spark technologies used for big data, Pipelines for data integration and ETL/ELT, and deep integration with unique Azure services like Power BI, Cosmos DB, and Azure ML are all combined in Azure Synapse.
By giving consumers the option to decide how much of the sensitive data to reveal with the least possible impact on the application layer, data masking assists in preventing unauthorized access to sensitive data. By concealing it from non-privileged users, dynamic data masking restricts acute data exposure. The sensitive material is concealed in the result set of a query over specific database fields using this policy-based security feature. In contrast, the database’s data won’t be altered.
A few data masking policies are:
- SQL users excluded from masking – A set of SQL users or Azure Active Directory identities that get unmasked data in the SQL query results. Users with administrator privileges are permanently banned from masking and seeing the original data without any mask.
- Masking rules – A set of rules defining the designated fields to be masked and the masking function used. The selected fields can be determined using a database schema, table, and column names.
- Masking functions – A set of methods that control data exposure for different scenarios.
A cloud-based integration solution called Azure Data Factory enables customers to create data-driven processes for organizing and automating data transportation and transformation. You can: Use Azure Data Factory,
- Create and schedule workflows based on data that can be accessed from various data sources.
- Utilise computing technologies like HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Azure Machine Learning to process and convert data.
- In the SQL Server Database, create a Linked Service for the source data store
- For the destination data store, build a Linked Service that is the Azure Data Lake Store
- For Data Saving purposes, create a dataset
- Build the pipeline and then add the copy activity
- Plan the pipeline by attaching a trigger.
