D Turtle Academy

Azure Data Factory Training in Bangalore


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Azure Data Factory Training in Bangalore - New Batch Details

Trainer NameMr. Hemanth
Trainer Experience15+ Years
Next Batch Date22-02-2023 (8:00AM IST)
Training Modes:Classroom Training, Online Training (Instructor Led)
Course Duration:45 Days
Call us at:+91 87223 55666
Email Us at:dturtleacademy@gmail.com

Azure Data Factory Training in Bangalore - Curriculum

ADF Session 1 : Azure Data Engineer Services Introduction Part 1
ADF Session 2 : Azure Data Engineer Services Introduction Part 2
ADF Session 3 : Azure Data Engineer Services Introduction Part 3
ADF Session 4 : Azure Data Engineer Services Introduction Part 4
ADF Session 5 : Azure Data Engineer Services Introduction Part 5
ADF Session 6 : Azure Data Engineer Services Introduction Part 6
ADF Session 7: ADF introduction

Difference between ADF version 1 and Version 2. 

  • DataFlows and Power Query are new features in Version 2 
  • DataFlows are for data transformations 
  • PowerQuery is for Data preparations and Data Wrangling Activities.
  • Building blocks of ADF 
  • -> PipeLine 
  • -> Activities 
  • -> Datasets 
  • -> Linked Service 
  • -> Integration RunTime 

    * Auto Integration Runtime 

    * Self Hosted Integration runtime 

    * SSIS Integration Runtime.

ADF Session 8:
  • More on Technical differences between ADF version 1 and Version 2 – Part 1
ADF Session 9:
  •  More on Technical differences between ADF version 1 and Version 2 – Part 2
ADF Session 10:
  •  Introduction to Azure Subscriptions

  •  Types of Subscriptions 

    -> Free Trial 

    -> Pay-AS-You-Go

    • Why Multiple subscriptions are required? 
    • What are resources and Resource Groups? 
    • Resource Group Advantages 
    • Why Multiple resource Groups need to be created? 
    • What are regions 
    • Region Advantages. 
ADF Session 11:
  • Create Storage Account with Blob Storage feature 
  • Converting Blob storage feature as DataLake Gen2 feature 
  • Create Storage Account with Azure Data Lake Gen2 features. 
  • How to Enable Hierarchical name space. 
  • Creating Containers 
  • Creating sub directories in the Container of Blob Storage. 
  •  Creating sub directories in Container of DataLake Gen2 Storage 
  • Uploading local files into Container/sub directories. 
  • When is ADF required?
ADF Session 12:
  • Create Azure SQL and Play with Azure Sql – Part 1 
  • Azure sql as OLTP 
  • Create Azure Sql Database 
  • Create Azure Sql Server 
  • Assign Username and Password for Authentication 
  • Launching Query Editor 
  • Adding Client IP address to FireWall Rule settings 
ADF Session 13:
  • Create table 
  • Insert rows into table 
  • Default schema in azure sql 
  • Create schema 
  • Create table in user created schema 
  • Loading query data into to table 
  • Information_schema.tables 
  • Fetching all tables and views from database 
  • Columns of Information_schema.tables 





Fetching Only tables from database

ADF Session 14:
  • How to create Linked Service for Azure data lake 
  • Possible errors while creating linked service for Azure datalake account. 
  • How to solve errors for Linked Service for Azure data Lake 
  • Two ways to Solve Linked Service Connection Error. 

-> Enable Heirarchical namespace for Storage Account 

-> Disable Soft Delete options BLOB and CONTAINER

  • How to create datasets for Azure data lake file and containers 
  • Your First ADF pipeline for Datalake to data lake file loading 
  • COPY DATA activity used in pipeline 
  • Configuring Source dataset for Copy Data Activity 
  • Configuring Sink dataset for Copy Data Activity 
  • Run the pipeline: 
  • Two ways to Run a Pipeline. 

-> Debug mode 

-> Trigger mode

  • Two Options in Trigger mode 

-> On demand 

-> Scheduling

ADF Session 15:
  • How to load data from Azure data lake to Azure sql table. 
  • Create linked service for Azure sql Database 
  • Error resolving , while creating Linked service for Azure Sql Database
  • Create a dataset for Azure Sql Table. 
  • Create a pipeline to load data from Azure data lake to Azure sql Table 
  • Helped activities 
  • Copy activity
ADF Session 16:
  •  If data lake file schema, and azure sql table schema are different, How to load Using Copy Data Activity. 

ADF Session 17 :
  • Perform ETL With “Copy Data” Activity 
  • Copy Data Activity With “Query” Option 
  • Loading Selected Columns and Matching Rows with Given Condition , From Azure Sql Table to Azure Data Lake
  • Creating new fields based on existed Columns of table and Load into Azure Data Lake
ADF Session 18:
  • Problem Statement : A file has n fields in the header , but data has n+1 field values . How to Solve this problem in Copy Data Activity
ADF Session 19:
  • Solution to Above Problem Statement. Practical implementation.
ADF session 20:
  • Get Metadata – part 1 
  • Get Metadata Field List Options for Folder 
  • “Exists” Option of Field List 
  • Data type of Exists field in “Get Metadata” output as Boolean(true/false) 
  • “Item Name” Option of Field List 
  • “Item Type “ Option of Field List 
  • “Last Modified” Option of Field List 
  • “Child Items” Option of Field List 
  • Data Type of “child Items” field in Json Output. 
  • What is each element of “Child Items” called?
  • Data type of item of “ChildItems” Field
  • What are subfields of each item in “childItems” Field 

ADF Session 21:
  • Get Metadata Activity – Part 2 
  • If Input dataset is file, what are Options of “Field List” of Get Metadata.
  • How to Get the number of Columns in a file? 
  • How do you make sure the given file exists ? 
  • How to get the file name, which is configured for the dataset.? 
  • How to get a dataset back end data object type(file or folder) ? 
  • How to Know when a file was last modified? 
  • How to get File size ? 
  • How to get File structure(schema)? 
  • How to get all above information of a file/folder/table with a single pipeline run 
  • If the input dataset is an RDBMS table, what are options of “Field List” of Get Metadata Activity.? 
  • How to get the number of columns of a table ? 
  • How to make sure the table exists in the database ? 
  • How to get the structure of a table ?
  • In this Session, you will learn answers for all above questions practically.
ADF Session 22:
  • Introduction to “Get Metadata” activity. 
  • How to fetch File System information Using “ Get metadata” Activity.
  • How “Get metadata” activity writes output in “JSON” format. 
  • How to Configure Input dataset for “Get Metadata” activity. 
  • What is the “Field List” for “Get Metadata” activity? 
  • Small introduction to “Field List” options.
  • Importance of “Child Items” option of “Field List” in “Get Metadata” activity. 
  • How to Check and understand output of “Get Metadata” activity.
  • “childItems” field as JSON output of “Get Metadata”. 
  • “childItems” data type as Collection as data type As Array. 
  • What is each element of “childItems” output 
  • What is the “exists” option in “Field List”. 
  • Introduction to “Filter” activity.
  • Problem statement: in a container of a Azure Datalake storage account , there are 100s of files, some files related with adf , and some files related with employees, and some files related with sales and some files related with others like log.
  • How to fetch only required files from Output of “get metadata” activity ? 
  • How to place “Filter” activity in the pipeline. 
  • How to Connect “Get metadata” activity and “Filter” activity. 
  • What happens , If we don’t connect two activities?(bcoz, this is the first scenario with multiple activities in a single pipeline). 
  • How to pass output of “Get metadata” Activity to “Filter Activity” 
  • “Items” field of “Filter” Activity. 
  • What is the “@activity()” function and “@activity().output”. 
  • Above Output produces a lot of fields , How to take specific fields as input to filter activity. 

    Example : @item().name, @item().type


    @startswith() function example 
  •  How to check output of “Filter” activity

  • What is the Field name of “Filter” activity , in which required information is available. 

  • @not() function example 

  • @item() output as nested json record. How to access each field. Example, @item().name, @item().type
  • get metadata + filter activities and how to apply single condition how to apply multiple conditions all the things 

    there are functions 



    @greater( ) 

    @greaterOrEquals( ) 

    @or (C1,C2 …….) 

    @and (C1,C2 …..) 

    @not (equals( ))

ADF Session 23:
  •  Task : Get Metadata – fetch only files , and reject folders of Given Container with 

    “Get Metadata” and “Filter” Activity.

  • steps to achieve Above Task. 

  • Step1: Create a Pipeline and Drag Get MetaData Activity 

  • Step2 : Configure Input DataSet of Container 

  • Step3: Add a Field List With “child Items” Option. 

  • Step4: Add “Filter” activity to “Get Metadata” 

  • Step5: Configure “Items” Field , Which is input for “Filter” activity From Output of “Get Metadata”
  • Step6: Configure Filter Condition to take only Files. 
  • Step7: Run the Pipeline 
  • Step8: Understand Output of “Filter” Activity.
  • In which field of Json, Filter output is available ? 
  • What is data type of Filtered output 
  • In this task you will work with below ADF expressions 
  • @ activity() Function .. 
  • @ activity(‘Get Metadata1’).output 
  • @ activity(‘Get Metadata1).output.childItems 
  • @ item().type 
  • @ equals(item().type, ‘File’) 
  • After Completion of this session you will be able to implement all above steps , and can know answers for above questions , and able to use adf expressions practically.
ADF Session 24:
  •  Scenario : with GetMetadata and Filter Activities – Part 1 

ADF Session 25:
  •  Scenario : with GetMetadata and Filter Activities – Part 2 

ADF Session 26:
  • Task : bulk load of files one storage account to another storage account (from one container to other container ) 

    Helped activities 

    -> get metadata 

    -> foreach 

    -> copy data 

ADF Session 27:
  • Bulk load of files from one storage account to Other storage account with “Wild card “ option.

  • Bulk load of files from one storage account to Other Storage account with “Get Metadata” and “ForEach” and “Copy Data” Activities. 

ADF Session 28 :
  •  Data Set Parameterization


    Bulk load of files into Other Storage Account with “Parameterization”
ADF Session 29:
  •  copy only files which starts emp into target container Using Get MetaData , Filter , ForEach, Copy Data Activities – [ in target container, file name should be same as source ] 

ADF Session 30:
  • load multiple files into the table.(Azure Sql) 

  • Helped activities 


    get metadata + filter + foreach + copy data activities 

ADF Session 31:
  • Conditional split.

  • Conditionally distributing files (data) into different targets (sinks) two ways.

    using filter 

    using if condition activity 

    Conditional Split implementation with “Filter” activity 

    Problem with Filter , explained.

ADF Session 32:
  • Conditional split with “if condition”.
  • Helped activities 

get metadata + filter + for each + if condition

ADF Session 33:
  • Lookup activity 
  • migrate all tables of the database into a data lake. with a single pipeline. 
  • Helped activities 
  • lookup activity 
  • for each activity 



ADF Session 34:
  • Data Flows Introduction. 
  • ELT (Extract Load and Transform) 
  • Two DataFlows in ADF 
  • Dataflow activity in pipeline. 
  • Mapping Data Flows 
  • Configuring Mapping Dataflows as Data Flow Activity in PipeLine 
  • Introduction to Transformations
  • Source 
  • Sink 
  • Union 
  • Filter 
  • Select 
  • Derived Column 
  • Join etc.
  • Difference between source , sink of “Copy Data” activity And source , sink of “Mapping Data Flows”.
ADF Session 35:
  • Source as (data lake) Sink as (SQL table) ——-> by using dataflow source and sink transformation
  • Extract data from RDBMS to Datalake ———–> Apply Filter ——> sink 

  •  Assignment1 

    SQL Table to Datalake 

    Helped Transformation 



    (Dataflow Activity)

ADF Session 36:
  • Filter transformations 
  • Helped Transformation 




    (Dataflow Activity)

ADF Session 37:

● Select transformations
1. Rename columns
2. Drop columns
3. Reoder the columns
● Helped Transformation
(Source )

(Dataflow Activity)



ADF Session 38:

● Derived column Transformation Part1
1.You can generate new column with given expression
2.You can update existed column values
● Helped Transformation
Derived column
(Dataflow Activity)

ADF Session 39:

● How to clean/handle null values in data.
● why should we clean nulls
1.computational errors
2.data loading errors into target table synapse table.

ADF Session 40:

● Cleaning names(cleaning means not always replace nulls with some constant value, cleaning means transform data according to business tool).formating of the data (names)
● Helped transformation
Derived column

ADF Session 41:
  • Generate new columns,with conditional transformation. 

    two options: 

    1.iif() —->nested ifs,
    ● Helped transformation
    Derived column
    ADF Session 42:
    ● Conditional transformation with case() function.
    emp—->derived columns—–>select—–sink
    ● Helped transformation
    derived column
    (Dataflow activity)
ADF Session 43:

● Union transformation part 1.
● Merging Two different files with the same schema by using two sources using the union transformation, And writes output into a single file.
● Helped transformation
Two sources
(Dataflow activity)

ADF Session 44:
  •  Merging three different files with different schemas by using derived column transformation, select transformation and union transformation, finally we get a single file with a common schema.

  • Helped transformation Derived column
    (Dataflow activity)

ADF Session 45:

● Two different files by using derived column transformation,
union transformation and aggregate transformation, to get
branch1 total and branch 2 total.
● Helped transformations
Derived column
*(After this Watch session 70, for more on Unions).

ADF Session 46:

● Joins transformations 5 types
1.Inner join
2.Left outer join
3.Right outer join
4.Full outer join
(5.Cross jons)
● Helped transformation

(Dataflow activity)

ADF session 47:
  • Full Outer Join Bug fix 

ADF session 48 :
  • How to join more than two datasets (example 3 datasets). 

ADF Session 49:

● Inter linked scenario related to 25th session.
treat, dept as project
task is ” summary Report”
active employes (who already engaged into project -> 11,12,13 projects,-> these projects total salary budget (bench team 20,21 total salary ,bench project )
● Helped transformations
Derived column
(Dataflow activity)

ADF Session 50:

● To use full outer join advantage (complete information,no information missing)
task~1 : Monthly sales report by using derived column transformation, aggregate transformation.

● Helped transformation
Derived column

ADF Session 51:

● Task~2 : Quarterly sales report by using derived column transformation, aggregate transformation.
● Helped transformation
Derived column

ADF Session 52:

● Task~4 : Comparing Quartely sales report: Comparing Current Quarter sales with its Previous Quarter Sales.
● Helped transformations

Derived column 

ADF Session 54:
  • A real time scenario on Sales data Analytics – Part 1 
ADF Session 55:
  • A real time Scenario on Sales data Analytics – Part 2 

ADF session 56:

● More on Aggregation Transformation.
● Configuration Required for Aggregation Transformation
● Entire Column aggregations.
● Entire Column Multiple Aggregations.
-> Sum()
-> Count()
-> max()
-> min()
-> avg()
● Single grouping and Single Aggregation
● Single Grouping and Multiple Aggregations
● Grouping by Multiple Columns with Single aggregation
● Grouping by Multiple Columns with Multiple Aggregations.
● Finding range Aggregation by adding “Derived Column” transformation to “Aggregation” Transformation.

ADF Session 57:

● Conditional Split of data.
● Distributing data into multiple datasets based on given condition

● Split on options:
1. First Matching conditions
2. All Matching conditions
● When to use “First Matching Conditions” and When to use “All Matching Conditions”
● An Example.

ADF Session 58:

● An Use Case on “First Matching Condition” option of “Conditional Split” Transformation in Mapping Data Flows with Sales data.

ADF Session 59:

● An Use Case on “Matching All Conditions” option of “Conditional Split” Transformation in Mapping Data flows with Sales data.

ADF Session 60:

● Conditional split with cross join transformation
by using matrimony example.

ADF Session 61:

● Lookup with multiple datasets
● Helped transformations
(Dataflow activity)

ADF Session 62:

● Lookup with more options
● products as primary stream
● transformation as lookup stream
● Helped transformations

ADF Session 63: Broadcasting
ADF Session 64: Partitioning part 1
ADF Session 65: Partitioning part2
ADF Session 66: Partitioning part3
ADF Session 67:Exists transformation - part 1

● Helped transformation

ADF Session 68: Exists transformations - part 2

● Helped transformation
(Dataflow activity)

ADF Session 69:

● Finding Common Records, Only Records available in first dataset, Only Records available in Second Dataset.

● All Records except Common records in First and 2nd Datasets in Single DataFlow

ADF Session 70:

● How to capture data changes from source systems to Target Data warehouse Systems.
● Introduction to SCD (Slowly Changing Dimensions)
● What is SCD Type 0 and Its Limitation.
● What is Delta in Data of Source System.
● What is SCD Type 1 and Its Limitation
● What is SCD Type 2 and How it tracks History of specific attributes of source data.
● Problem with SCD Type 2
● Introduction to t SCD Type 3
● How It solves problem of SCD Type 2
● How SCD type 3 maintains recent History track
● Limitation of SCD Type 3.
● Introduction to SCD Type 4
● How SCD Type 4 will provide complete Solution to SCD type 2.
● (Remember no SCD 5)
● Introduction to SCD Type 6 and Its benefits.

ADF Session 71:

● How data transformations are done in ADF1 (with out dataflows)
● Aggregate Transformation , Sort Transformation with following examples 1. Single grouping with single aggregation
2. Single Grouping with multiple aggregations

3. Multi Grouping with Multiple Aggregations.
4. Sort with single column
5. Sort with multiple columns.

ADF Session 72:

● What is pivot transformation?
● Difference between Aggregate transformation and Pivot Transformation
● Implement pivot transformation in dataflow
● How to clean pivot output
● How to call multiple dataflows in a single pipeline.
● Why we used multiple dataflows in one single pipeline.

ADF Session 73:

● Assignment on Join, aggregate, pivot transformations.
● Finding Occupancy based on salary.

ADF Session 74:

● Unpivot transformation.
● What is should be the input for Unpivot (pivoted output file).
● 3 configurations :
-> Ungrouping column
-> Unpivoted column. (column names to as column values)
→ aggregated column expression ( which row aggregated values to be turned as column values)
● Difference between output of “aggregate” and “unpivot” transformation.
● What additionally unpivot produces.

● Use case of Unpivot

ADF Session 75:

● Surrogate Key transformation,
● Why we should use Surrogate key.
● Configuring Starting Integer Number for Surrogate Key
● Scenario : your bank is “ICICI”, for every record Unique CustomerKey should be generated as ICICI101 for the first customer, ICICI102 for 2nd customer . But the Surrogate key gives only integer value as 101 for first and 102 for 2nd. How to handle the given scenario , which is a combination of string and integer.

ADF Session 76:

● Rank Transformation in dataflows – Part 1
● Why sorting data is required for Rank transformation
● Sorting options as “Ascending and Descending”
● When to use the “Ascending” option for Ranking.
● When to use the “Descending” option for Ranking.
● Dense Rank and How it Works
● Non Dense Rank (Normal Rank) and How it Works.
● Why we should not use “surrogate key” for Ranking.
● What is difference between Dense Rank and Non Dense Rank(Normal Rank)

ADF Session 77:

● Limitations of Rank Transformation
● Implementing Custom Ranking With a Real time scenario

● Custom Ranking implementation with Sort, Surrogate key, new Branch , aggregate, Join transformations.
● Scenario :
-> problem statement: if a school has 100 students, one student got 90 marks , remaining all 99 students failed and scored 10 marks. The Rank transformation of ADF dataflows gives 1 rank for those who scored 90 , and 2 rank for failed students , who scored 10 marks. But the school management wanted to give a gift to the top 2 Rankers ( 2nd rankers failed and got equal least score , so that all students will get a gift). How to handle this scenario.

ADF Session 78:

● Window transformation part 1
● Window transformation.
● Cumulative Average
● Cumulative Sum
● Cumulative Max
● Dense Rank for each partition.
● Making all rows as Single Window and apply Cumulative Aggregation.

ADF Session 79: Window transformation part 2
ADF Session 80: Window transformation part 3
ADF Session 81: Window transformation part 4
ADF Session 82:Window transformation Part 5
ADF Session 83:Window transformation Part 6
ADF Session 83.1: Custom Rank Implementation with Window Transformation,
ADF session 84:

● Parse transformation in Mapping DataFlows.
● How to handle and parse string collection ( delimited string values )
● How to handle and parse xml data.
● How to handle and parse json data

ADF session 85:

● Converting Complex Json nested structures into CSV/Text file.
● Complex data processing (transaformations).
● How parse nested Json records
● How to flatten array of values into multiple rows.
● If data has complex structures, what are supportive data store formats .
● How to write into json format.
● How to write into flatten file format( csv).
● Transformation used to process data.
1. Parse
2. Flatten
3. Derived Column
4. Select
5. Sink.

ADF Session 86:

● Reading Json data (form vertical format)

● Reading a single Json record(document)
● Reading array of documents .
● Converting complex types into String using Strigify and derived column transformations.
● Used transformations in Data Flow.
-> source
->derived column
Expression : toString(complexColumn)
→ select
-> sink.

ADF session 87:

● Assert Transformation
● Setting Validation Rules for Data.
● Types of Assert:
-> Expect True
-> Expect Unique
-> Expect Exists
● isError() function to validate a record as “Valid” or “Invalid”.

ADF session 88:

● Assert Transformation part 2.
● Assert Type “Expect Unique”
● Rule for Null values
● Rule for ID ranges.
● Rule for ID uniqueness

● hasError() function to identify which rule is failed.
● Difference Between isError() and hasError()

ADF session 89:

● Assert Transformation part 3.
● How and why to configure Additional streams
● Assert type “Expect Exists”
● How to validate a record reference available in any one of multiple additional streams. (A scenario implementation).
● How to Validate a record reference available in all multiple additional streams.(A scenario Implementation),

ADF SESSION 90: Incremental data loading (part 1)
ADF session 91: Incremental data loading (part 2)
ADF session 92:

● Incremental data loading (part 3).
● Implementation of Incremental load(delta load) for multiple tables with a single pipeline.

ADF session 93: Deduplication part 1
ADF session 94: Deduplication part 2
ADF Session 95: Deduplication Part 3
ADF Session 96: Solving Bugs of Incremental Loading . - Part1
ADF Session 97: Solving Bugs of Incremental Loading – Part2
  • Error Handling and Validations
  • Snowflake Pricing model and selecting best Edition and Calculation of Credits usage
  • Resource Monitoring
  • Data Masking
  • Partitioning and Clustering in snowflake
  • Materialized View and Normal View
  • Integration with Python
  • Integration with AWS, Azure, and Google Cloud
  • Best Practices to follow
  • SCD implementation using streams (CDC)
ADF session 98:

● AlterRow Transformation Part – 1
● Removing List of Records from Sink of a Specific criteria (Table)

ADF session 99:

● AlterRow Transformation part- 2
● Removing a given List of Records from Sink of no specific Criteria.

ADF Session 100:

● AlterRow Transformation Part – 3.
● Update given List of Records in Sink.

ADF session 101:

● SCD (Slowly Changing Dimensions Type 1 ) Implementation.
● Alter Row Transformation with UpSert Action.

ADF Session 102:

● A realtime Assignment (Assignment1 and Assignment 2) on SCD.
● Incremental Load and SCD combination

ADF Session 103:

● How to implement SCD before “Alter Row” Transformation not introduced in DataFlows.
● How exists transformation helpful.
● How to implement SCD before ADF version 2 features of DataFlows.
● How Stored procedures helpful and implement SCD.

ADF Session 104:

● Combining rows of multiple tables with different schemas with a single Union Transformation.
● Problem with Multiple Unions in DataFlow.

ADF Session 105:

● Types of Sources and Sinks
● Source Types:

-> Dataset
-> Inline
● Sink Types:
-> Dataset
-> Inline
-> Cache
● When to use dataset and Inline
● Advantage of Cache as Reusage of output of Transfomation

ADF Session 106:

● How to write output into Cache.
● How to Reuse the Cached output.
● Scenario : Generating Incremental IDs based on Maximum ID of Sink DataSet – Part 1

ADF Session 107:

● Scenario: Generating Incremental IDs based on Maximum ID of Sink DataSet – Part 2

ADF Session 108:

● Writing outputs into JSon format
● Load Azure SQL data into Json File.

ADF Session 109:

● Cached Lookup in Sink Cache . Part 1
● Scenario :
Two Source Files, 1. Employee 2. Department
Common Column in both dno(department number) which is as Joining Column or Key Column.

● Task: Without using Join Transformation , Lookup Transformation , we need to join two datasets to increase performance using “Cache Lookup” of Cache Sink Type.
● Other alternatives of this Cached Lookup.
● How to configure Key Column for Cached Lookup
● How to access values of Lookup key in Expression Builder.
● lookup() function in Expressions

ADF Session 110:

● Cached Lookup in Sink Cache – Part 2
● Below Scenario will teach you, how cached output is used by multiple transformations.
● Scenario:
Single Input file : Employee,
Two Transformations required.
1. Foreach employee , find his salary occupancy in his department
2. Foreach employee, find his average salary status in his department as “Above Average” or “Below Average”.
For these two transformations common input sum(), avg() aggregations with group by department number . This should be sent to Cache Sink as Cached Lookup.
Each transformation output should be in separate Output file.
● In above Dataflow, What flows are executed in parallel and What flows executed in sequence.
● How spark knows dependency between flows. [Using DAG

(Direct Acyclic Graph ) engine ]
● Scope of “Sink Cache” output. [ problem statement ]

ADF Session 111:

● Using “ Multiple Cache Sink “ outputs in Single Transformation.
● Scenario:
Employee table has id, name, salary, gender, dno, dname, location columns with two records 101 and 102 ids.
New employees are placed in datalake file newdata.txt with name,salary, gender, dno fields.
Department name, department location fields are available in department.txt file of datalake.
Insert all new employees into Employee table of azure SQL , with incremental ID based on maximum id of the Table along with department name and location .
● Sink1 : write max(id) from Employee table (cache sink)
● Sink2 : write all rows of department.txt into Cache Sink. and Configure dno as Key Column of Lookup
● Read data from new employees file and generate next employee id with help of surrogate key and sink1 output.
● Generate lookup columns department name, location from Sink2 and Load into Target Employee table Azure SQL.

ADF Session 112:

● How two Different DataFlows exchanging Values – Part 1.
● Scope of Cache Sink of a DataFlow.

● What is Variable
● What is Difference between Parameter and Variables
● How to Create a Variable in Pipeline
● Variable Data Types:
-> String
-> Boolean
-> Array
● Create a DataFlow with Following Transformation Sequence to find Average of Column
1. Source
2. Aggregate (for finding Average)
3. Sink Cache (to write Average of column)
● How to pass Cache Sink output to Activity Output. How to Enable this feature ● Create a Pipeline to Call this Data Flow which writes into Sink Cache ● How to Understand Output of DataFlow Activity which writes in Sink Cache and Writes as Activity Output
● Fields of DataFlow Activity output.
1. runStatus
2. Output
3. Sink
4. value as Array
● “Set Variable” Activity
● How to Assign Value to Variable using “Set Variable” Activity.

ADF Session 113:

● How two Different DataFlows exchanging Values – Part 2
● How to Assign DataFlow Sink Cache Output to Variable .
● Dynamic Expression to Assign Value to Variable Using “Set Variable” Activity.

● Understand Output of “Set Variable” Activity.

ADF Session 114:

● How two Different DataFlows exchanging Values – Part 3
● DataFlow Parameters
● How to create Parameter for DataFlow
● Fixing Default Value for DataFlow Parameter
● How to access DataFlow Parameter
● $<parameter_name>
● Accessing variable value from pipeline into DataFlow Parameter
● @variables(‘<variable_name>’)
● Dynamic expression to access variable value in DataFlow Parameter
● From above 3 sessions, you will be learning how to pass output of cache sink output to other dataflows .

ADF Session 115:

● Categorical Distribution of Data into Multiple Files (foreach data group one separate file Generation ). → Part 1
● Scenario: There is department name column with “Marketing”, “Hr”, “Finance” values. But these values are duplicated. All rows related with these 3 categories . We need to distribute data dynamically into 3 categories. 

● Example: all rows related with marketing department into “marketing.txt” file.
● Find Unique Values in Categorical Column and Eliminate Duplicate Values
● Convert Unique Column into Array of String
● collect() function
● Aggregate Transformation using Collect() function
● Write Array of String into Sink Cache
● Enable Cache to “Write to Activity Output”
● Create a pipeline, to Call this Dataflow.
● Create a variable in pipeline with Array Data type
● Access DataFlow Cache Array into Array Variable with “Set Variable” activity Using “Dynamic Expressions”.

ADF session 116:

● Categorical Distribution of Data into Multiple Files (foreach data group one separate file Generation ). → Part 2
● Array variable to Foreach
● Configuring input (value of Array Variable) for Foreach Activity
● DataFlow sub Activity under Foreach.
● Passing Foreach Current item to Dataflow parameter
● Apply Filter with DataFlow Parameter.
● DataFlow Parameter as file name in Sink Transformation,

ADF Session 117:

● SCD (Slowly Changing Dimensions ) Type 2 – Part 1
● Difference between SCD Type 1 and SCD Type 2

● What is Delta of Source data
● How to Capture Delta (Solution : Incremental loading)
● Behavior of SCD Type 1
● If Record of Delta existed in Target, what happens for SCD Type1
● If Record of Delta does not exists in Target, What happens for SCD Type 1
● How to capture Deleted records from source into Delta.
● Behavior of SCD Type 2
● What should happen for Delta of Source in Target
● What should happen for old records of Target when Delta is inserted in Target
● Importance of Surrogate Key in Target Key table.
● What is version of a record
● How to recognize a record is active or inactive in Target Table
● Importance of Active Status Column in Target table to implement SCD Type 2
● What additional columns are required for Target table, to implement SCD Type 2
● Preparing data objects and data for SCD Type 2
● Create target table @ Azure sql or Synapse
● What is identity column is azure sql
● Identity column as surrogate key in target table

● Possible values for active status column in target table
● Preparing data lake file for delta

ADF Session 118: SCD (Slowly Changing Dimensions )

Type 2 – Part 2 

ADF Session 119: SCD (Slowly Changing Dimensions)

Type 2 – Part 3 

ADF Session 120: SCD (Slowly Changing Dimensions)

Type 3 – Part 1 

ADF Session 121: SCD (Slowly Changing Dimensions)

Type 3 – Part 2 

ADF Session 122: SCD (Slowly Changing Dimensions )

Type 4 – Part 1 

ADF Session 123: SCD (Slowly Changing Dimensions)

Type 4 – Part 2 

ADF Session 124: SCD (Slowly Changing Dimensions)

Type 6 – part 1 

ADF Session 125: SCD (Slowly Changing Dimensions)

Type 6 – Part2 

ADF Session 126: SCD (Slowly Changing Dimensions)

Type 6 – Part 3 

ADF Bonus Sessions :
ADF session 127: How to write activity output into file: ( datalake). - part1
ADF session 128: How to write activity output into file: ( datalake). - part2
ADF session 129: How to write activity output into file: (datalake). - part3
ADF session 130: How to write activity output into file: ( datalake). - part4
ADF Session 131: More Information on Integration Runtime. – part 1
ADF Session 132: More Information on Integration Runtime – Part 2
ADF session 133: Until Activity Part 1
ADF session 134: Until Activity Part 2
ADF Session 135: Web activity Part 1
ADF Session 136: Web Activity Part 2
ADF Session 137: Switch Activity
ADF Session 138: Script Activity
ADF Session 139: More on Lookup Activity with Set Variable part -1
ADF Session 140: More on Lookup Activity With Set Variable Part - 2
ADF Session 141: More On Lookup Activity With Append Variable Activity
ADF session 142: Importing Data From SNOWFLAKE to Azure Blob Storage Part - 1
ADF session 143: Importing Data From SNOWFLAKE to Azure Blob Storage Part - 2

Key Features of Azure Data Factory Training in Bangalore

Why Choose us for Azure Data Factory Course?

Expert Trainers

Expert Trainers

The trainers at D Turtle Institute are experienced industry experts with in-depth knowledge of Azure Data Factory and data integration technologies.

Comprehensive Course Material

Comprehensive Course Material

The institute provides comprehensive course material that covers all the essential topics related to Azure Data Factory.

Hands-on Experience

Hands-on Experience

The course is designed to provide you with hands-on experience with Azure Data Factory through real-world use cases and projects.



D Turtle Institute offers flexible training schedules and modes, such as online, classroom, and self-paced learning.

Customizable Course Curriculum

Customizable Course Curriculum

Our instructors customize the course curriculum to meet your specific learning requirements and goals.



Upon completing the course, you will receive a certification from D Turtle Institute, which will add value to your resume and career.

Industry Networking

Industry Networking

D Turtle Institute offers networking opportunities with industry professionals and experts, which can help you expand your knowledge and career prospects.

Affordable Pricing

Affordable Pricing

The course fees at D Turtle Institute are affordable and competitive compared to other training institutes.

Compact Batch

Compact Batch

We take only 10 students in each of our batches to make sure each of them get individual attention from the trainers. 

Benefits of Azure Data Factory


Azure Data Factory is a scalable service that can handle large amounts of data processing and integration, making it an ideal choice for organizations with growing data needs.

Integration with Azure Services

Azure Data Factory seamlessly integrates with other Azure services, such as Azure Synapse Analytics and Azure Databricks, allowing for a unified data integration and analysis environment.


Azure Data Factory is a cost-effective solution that enables organizations to manage data integration and processing without the need for expensive hardware and infrastructure.


Azure Data Factory enables organizations to automate data integration and processing workflows, reducing manual effort and improving efficiency.


Azure Data Factory provides robust security features that ensure the confidentiality, integrity, and availability of data, making it a trusted choice for sensitive data processing.

Real-time Data Integration

Azure Data Factory supports real-time data integration, enabling organizations to get insights from data in near real-time, which can help them make informed decisions and respond quickly to changing business needs.


Azure Data Factory Training Offline

Azure Data Factory Training Offline

D Turtle Institute provides Azure Data Factory Training in Bangalore classroom-based training where students can learn in a traditional face-to-face setting with trainers.

Azure Data Factory Training Online

Azure Data Factory Training Online

D Turtle Institute offers Azure Data Factory Training Online courses that enable students to learn from anywhere at their own pace using a computer, laptop, or mobile device.

Azure Data Factory Corporate Training

Corporate Training

D Turtle Institute offers corporate training programs for organizations that want to train their employees in new technologies, such as Azure Data Factory.

Placement Program

Placement Program

D Turtle Institute provides placement assistance and guidance to students, which helps them land their dream job in the field of data integration and Azure Data Factory.

Project Training

Project Training

The ultimate goal of our  project training is to ensure that individuals have the necessary tools and techniques to effectively plan, execute, and deliver successful projects within the constraints of time, scope, and budget

Azure Data Factory Training Videos Pre-Recorded

Azure Data Factory Training Videos Pre-Recorded

D Turtle Institute provides pre-recorded video courses that enable students to learn at their own pace, anytime and anywhere.

Our Students Feedback

I recently completed the Azure Data Factory Training in Bangalore at D Turtle Institute, and I was blown away by the quality of the Azure data factory course. The trainers were knowledgeable and experienced, and the hands-on exercises provided me with real-world experience with the tool. Highly recommended!
Rahul S
I was a complete beginner to ADF before taking this Azure data factory course, but the trainers at D Turtle Institute made the learning process easy and enjoyable. The practical projects helped me gain confidence in my skills, and the certification added value to my resume. Best Azure Data Factory Training in Bangalore. Thank you, D Turtle Institute!
Sanjay R
The Azure Data Factory course at D Turtle Institute was a game-changer for me. The trainers were patient and thorough, and the training methodology was engaging and effective. I would definitely recommend D Turtle Institute's Azure Data Factory Training in Bangalore to anyone looking to enhance their skills in ADF.
Priya G
I appreciated the flexibility and affordability of the Azure Data Factory course at D Turtle Institute. The online training allowed me to learn at my own pace, and the pre-recorded videos were a great resource. The project-based training helped me gain valuable experience, and the certification boosted my career prospects.
Suresh T
The course curriculum for Azure Data Factory Training in Bangalore at D Turtle Institute was comprehensive and well-structured, covering all the key features and use cases of the tool. The trainers were responsive and helpful, and the placement assistance was top-notch. I landed a great job thanks to D Turtle Institute.
Nisha K
The trainers at D Turtle Institute were passionate and dedicated to providing quality education in Azure Data Factory Training in Bangalore. The corporate training program for my organization was tailored to our specific needs, and the placement assistance was valuable for our employees. I highly recommend D Turtle Institute for corporate training.
Ravi M
Enrolling in Azure Data Factory Training in Bangalore at D Turtle Academy turned out to be one of the most rewarding decisions I've made. The training was both comprehensive and practical, focusing on real-world scenarios. The instructors displayed remarkable expertise and supportiveness, and the institute's state-of-the-art facilities further enhanced the learning experience. Their placement assistance program also helped me a lot. Thank you.
I enrolled for the azure data factory training online and surpassed all expectations. Their highly knowledgeable instructors and well-designed azure data factory course content provided invaluable hands-on training. I developed a profound understanding of azure data factory technology and its significance in the automotive industry. I enthusiastically endorse this institute to anyone seeking to master Azure Data Factory Training in Bangalore.
My experience at D Turtle Academy while learning about Azure Data Factory course was simply amazing. The instructors demonstrated exceptional patience and skill in explaining complex concepts clearly. The training sessions were interactive and engaging, offering ample opportunities for practical learning. I extend my heartfelt gratitude to the institute for providing me with such an outstanding learning experience. Best azure data factory training in Bangalore.

About Azure Data Factory Training in Bangalore

Azure Data Factory is a cloud-based data integration service that allows organizations to create, schedule, and manage data pipelines that move data from various sources to target destinations. With Azure Data Factory, organizations can connect to a wide range of data sources, such as structured, unstructured, and semi-structured data, both on-premises and in the cloud.

Azure Data Factory provides a code-free and easy-to-use interface for designing and deploying data pipelines, which can help organizations save time and money. With Azure Data Factory, organizations can also automate their data processing and integration workflows, reducing manual effort and improving efficiency.

The Azure Data Factory Training in Bangalore at D Turtle Institute provides students with comprehensive knowledge and hands-on experience with the cloud-based data integration tool.

Our course curriculum covers all the essential features and functionalities of Azure Data Factory, and the trainers are experienced and knowledgeable.

Overall, the Azure Data Factory Training in Bangalore at D Turtle Institute is a valuable investment for anyone looking to enhance their skills in data integration and work with one of the most powerful cloud-based data integration services.

Enroll in our Azure Data Factory Training in Bangalore and get comprehensive training to increase your skill set to start a lucrative career. 

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Azure Data Factory Course & Certification

Azure Data Factory Certifications are one of the most sought-after certifications in the IT industry.

The certification is proof of your expertise and knowledge of Data Factory, and it can help you land a new job or even get a promotion.

The Azure Data Engineer Associate certification validates the candidate’s ability to design and implement data solutions using Azure services, including Azure Data Factory. 

Azure Data Factory Training in Bangalore

Skills Developed Post Azure Data Factory Training in Bangalore

Prerequisites for Azure Data Factory Course


Is azure data factory easy to learn?

Yes, it’s fast and easy to build code-free or code-centric ETL and ELT processes using Azure data factory

What is Azure Data Factory?

As part of the Azure cloud platform Azure Data Factory is a cloud-based data integration service provided by Microsoft. It allows you to create, schedule, and manage data integration and transformation pipelines that move and transform data from various sources to different destinations.

How to learn azure data factory?

D Turtle Academy provides different modes of training in Azure Data Factory such as – Online training, classroom training and self paced video learning. You can enroll in any modes of training to learn the Azure Data Factory from a basic to advanced level. 

What is covered in the Azure Data Factory training at D Turtle Institute?

The Azure Data Factory training at D Turtle Institute covers topics such as data ingestion, transformation, data output, and monitoring and troubleshooting data pipelines using Azure Data Factory.

Who are the trainers at D Turtle Institute for Azure Data Factory training?

The trainers at D Turtle Institute are experienced and knowledgeable professionals with expertise in data integration and cloud computing.

What is the duration of the Azure Data Factory training at D Turtle Institute?

The duration of the Azure Data Factory training at D Turtle Institute varies based on the type of training program, but typically ranges from 1-2 months.

Is the Azure Data Factory training at D Turtle Institute available online?

Yes, D Turtle Institute offers online training programs in addition to classroom and corporate training programs.

What is the mode of assessment for the Azure Data Factory training at D Turtle Institute?

Students are assessed through practical projects and assignments that enable them to gain real-world experience with Azure Data Factory.

Does D Turtle Institute offer placement assistance after the completion of the Azure Data Factory training?

Yes, D Turtle Institute provides placement assistance to help students find suitable job opportunities after completing the training program.

Azure data factory certification cost?

To know detailed information about the cost of the course, contact us or send us an email. Our team will get in touch with you as soon as possible.

What is the significance of Azure Data Factory certification?

Azure Data Factory certification validates the candidate’s skills and knowledge in data integration and processing using Azure Data Factory, which can enhance their career prospects and enable them to take on more challenging roles in the field of data engineering.

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