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Azure Data Engineer Certification Training
Viswa Online Trainings is one of the world’s leading online IT training providers. We deliver a comprehensive catalog of courses and online training for freshers and working professionals to help them achieve their career goals and experience our best services.
Learners : 1080
About Course
Azure Data Engineer understands the need for a quality training curriculum along with real-time implementation exposure as it forms the very essence of your future career in Azure Engineer Certification. Our well-structured online training course in Azure Data Engineer Training extensively covers all the core aspects of classes with an emphasis on live scenarios. Access to expert trainers and instructor-led training sessions ensures that you can easily clear your doubts and get the exact guidance that is expected from Azure Data Engineer Online Training sessions.
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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Schedule your sessions at your comfortable timings.
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Instructor-led training, Real-time projects
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Certification Guidance.
Self-Paced Learning
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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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Design Your Own Syllabus Based
Azure Data Engineer Training FAQ'S
The application of data collecting and analysis is the emphasis of data engineering. The information gathered from numerous sources is merely raw information. Data engineering helps in the transformation of unusable data into useful information. It is the process of transforming, cleansing, profiling, and aggregating huge data sets in a nutshell.
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Azure Synapse is an enterprise service accelerating time to discernment across data storage and tectonic data networks. Azure Synapse combines the stylish of SQL(Structured Query Language) technologies used in enterprise data warehousing, Spark technologies used for big data, Pipelines for data integration and ETL/ ELT, and deep integration with distinct Azure services like Power BI, Cosmos DB, and Azure ML.
Data masking helps in preventing unauthorized access to delicate data by enabling customers to assign how much of the delicate data to reveal with minimal impact on the application layer. Dynamic data masking limits acute data exposure by masking it to non-privileged users. It is a policy-based security feature that hides the delicate data in the result set of a query over designated database fields. In contrast, the data in the database will not be changed.
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.
Azure Data Factory is a cloud-based integration service that lets users build data-driven workflows within the cloud to arrange and automate data movement and transformation. Using Azure Data Factory, you can:
- Develop and schedule data-driven workflows that can take data from different data stores.
- Process and transform data with the help of computing services such as HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Azure Machine Learning.
- 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
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