ADF TRAINING IN HYDERABAD
ANALYTICS BENCHMARK gratefully says to start ADF Training course with real-time examples and real-life scenarios this course change your life and make you a better analyst in every one session has provable examples, ANALYTICS BENCHMARK there so, many students got the job in MNC companies in ADF means Azure Data Factory, a powerful, scalable, and flexible data integration platform that makes it easy to build, manage, and scale data pipelines. Data is the new currency, and data is the new oil, powering the next generation of digital experiences. Companies must not only harvest data but also turn it into actionable information to improve decision-making and drive business value
Introduction To Azure data engineer
o Importance of ETL
o What is ADF
o Why ADF
o Advantages compared to traditional ETL tools
o Different Modern Data warehouse systems in Market
Introduction to the Azure Data Engineering Products
Roles and Responsibilities of Azure Data Factory and Data Engineer
o Different types of data
o Difference between ADF version1 and version2 o Building Blocks of ADF
▪ Pipelines
▪ Activities
▪ Datasets
▪ Linked Services
▪ Integration Runtimes
Azure Storage Introduction
Blob Storage ,Data Lake Storage
Differences between Data Lake Gen1 and Gen2 o Storage Redundancy options
o Difference between blob storage and data lake gen2 o File Shares
o Queues
o Table Storage
o Load the data from Azure blob storage to Power BI o Load the data from Azure Data Lake to Power BI o Different Tiers in Azure Storage explanation
o Storage Life Cycle Management
o RBAC (Role-Based Access Control)
o ACL(Access Control Lists)
o Shared Access Signatures
o Access Keys
Azure Data Factory Practicals
Copy Activity,and it types
Scenario1
o Copy the file from one container to another container from the same storage account
o Copy the file from one storage account container to another storage account container
Scenario2
o How to copy the data from Azure SQL to Azure Data Lake Gen2 o How to load the data into Azure SQL Table from Azure Data Lake Gen2 o How to delete the files on a successful copy of entire folder contents
Scenario3
o How to copy the files with some pattern to another container
o How to Get the File Size and delete the files with a size greater than 4MB
Scenario4
o Conditional Split with if the condition
Scenario5
o Send an email on Pipeline Failure
Scenario6
o Migrate all tables of the database into a data lake with a single pipeline
Parameters
Scenario7
o How to apply more than one conditional split in ADF Activities
Scenario8
o How to run the pipeline until the file arrives in the Data lake Container
Scenario9
o How to load the filtered data to the azure SQL table/Synapse table
o Perform Transformation using script activity
Data Flows Introduction
o Difference between source, sink of “Copy Data” activity And the source, sink of “Mapping Data Flows”.
Dataflow Scenario1
o Source as (data lake) Sink as (SQL table) --> by using dataflow source and sink transformation
Extract data from RDBMS to Data lake --> Apply Filter
o Filter Transformation (Source --> Filter --> Sink) Execute with Dataflow Activity
Data Flow Scenario on Select Transformation
o Select Transformation
● Source --> Select --> Sink (Execute with Mapping Dataflow)
Derived Column use case
o Derived column Transformation
Union Transformation use cases
o Combining one or more datasets with the same schema and writing output to a single file
o Combining one or more datasets with different schema and writing output to a single file
o Select with union
Branching Transformation and its use cases
Exists Transformation
o Exists transformation and its use cases
o Exists with Custom Expression
Lookup transformation and its use cases and types
Conditional split transformation and its use cases
o Distribute data into multiple datasets based on given conditions o Split datasets
Join Transformation and its use cases
Aggregate Transformation and its use cases
Pivot Transformation
Unpivot Transformation
Aggregation
Stringify Transformation
Parse Transformation
Flatten Transformation
Implementation of Incremental data loading using ADF Activities Implementation of SCD (Slowly Changing Dimensions)
Data Loading to Synapse from Azure Data Lake
1. Today we are excited to announce that the data factory is now available in Azure.
2. This new data platform allows you to build, manage, and analyze data in Azure
3. Azure building a secure data factory for our customers to store, manage, and analyze their data
4. platform is built on the Microsoft Azure data factory which makes it easy to store, manage, and analyze all of your data in one place
5.azure data is secure and reliable, making it an ideal environment for building and deploying real-time applications.
ANALYTICS BENCHMARK presented the Azure Data Factory, a defacto solution for orchestrating data movements. We walked through a basic introduction. We described how to use different connectors and data sources and how to use DirectQuery. Finally, we looked at how to use DirectQuery with the Azure Resource Manager in these adf training in hyderabad these course provide effortable and (ARMs). "THANKING YOU"
ADRESS;
ANALYTICS BENCHMARK
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