Talend Open studio ,database ,warehouse course by mahesh

Talend Lead ,Talend Developer and Talend Certification

find the software and practice files in below google drive location

simply copy paste the below link in browser and start download all

https://drive.google.com/drive/folders/1tsyC7KWDVINhaaXIgACkHsIMnLO0pucr?usp=sharing

download all softwares to your local system

1. goto control pannel and uninstall java from ur machine.   

2. Install jdk 8 (jdk-8u121-windows-i586 / jdk-8u161-windows-x64)

just double click on it and keep on clicking next

3. Unzip the software TOS_DI-20160704_1411-V6.2.1.zip

                   OR TOS_BD-20170623_1246-V6.4.1.zip

4. install Oracle

  a.unzip the OracleXE112_Win64.zip / OracleXE112_Win32.zip and goto unziped folder

  b.click on setup to install oracle

5.while installing oracle

  you have to set   pwd  :manager

       re enter the pwd  :manager

6.goto command prompt and start typing   sqlplus

userid:system

pwd:manager

7.unzip the sqldeveloper-17.2.0.188.1159-x64.zip

  go to unzipped folder and double click on the sqldeveloper(.exe) icon

  create a new connection by clicking on + or (New connection) button

  give any name ex:oralce_dev

    username:system

pwd : manager1

host:localhost

port: 1521

sid: xe   

service name :xe

click on test you should get success and after that click on save ---->connect

8. go to unzipped folder of TOS_DI-20160704_1411-V6.2.1   or TOS_BD-20170623_1246-V6.4.1

   click on 2 icons  (.exe files) one of the icon will open as per your operating system

  first time when you open talend

  select ---> create new project

  give project name : any name example AMEX  OR VISA

  select project  from list and click on finish button

  when you open talend make sure you will get popups

  and you clicked on accept all and install option all the time

9.Install notepad++ from direct website

In this lecture, we’ll walk through the process of scanning metadata from a CSV (Comma-Separated Values) file and using that metadata to load data into an Oracle database using Talend Open Studio.

You will learn:

• How to import and scan metadata from a delimited CSV file

• How to define the correct schema structure using Talend's metadata repository

• How to create reusable metadata for both input files and Oracle database connections

• How to build a simple ETL job that reads CSV data and writes it to an Oracle table

• Tips for handling data type mismatches and delimiter issues

• Best practices for metadata reuse in large-scale Talend projects

By the end of this lecture, you'll be able to create a fully functional Talend job that seamlessly integrates CSV file input with Oracle DB output, all driven by Talend's metadata system.

One-to-One Mapping – File to Table and Table to File

Lecture Description:

In this session, you'll learn how to perform one-to-one data mapping using Talend by creating two practical jobs:

1. File to Table – Reading data from a structured file (such as a CSV) and loading it into a database table

2. Table to File – Extracting data from a database table and writing it to a flat file (e.g., CSV or TXT)

You will learn:

• How to use components like tFileInputDelimited, tDBOutput, tDBInput, and tFileOutputDelimited

• How to define schemas and maintain consistent metadata between source and target

• How to map fields directly between source and target when both structures match (1:1 mapping)

• How to handle simple transformations and null values

• How to test your jobs using sample data

By the end of this session, you'll be able to build and execute basic ETL jobs that move data from file to database and from database back to file with one-to-one field mapping.

This forms the foundation for more complex transformations and workflows in later modules.

In this lecture, you will learn how to perform data filtering, column selection, sorting, and deduplication using key Talend components. These operations are essential for preparing and cleaning data before loading it into a target system.

You will explore:

• tFilterRow: Filter records based on conditions (e.g., filter customers by region, orders above a certain value)

• tFilterColumns: Select only the required columns from your dataset for performance and clarity

• tSortRow: Sort your data in ascending or descending order based on one or more fields

• tUniqRow: Remove duplicate rows from your dataset using key fields

Through hands-on demonstrations, you will:

• Build simple yet powerful ETL pipelines using these components

• Understand how to define filter expressions and sort keys

• Apply deduplication techniques using tUniqRow

• Combine these transformations in a single Talend job for clean and sorted output

By the end of this lecture, you'll be able to filter, trim, sort, and deduplicate data efficiently in Talend, laying the groundwork for advanced data quality workflows.

we move beyond the basics and dive into advanced-level techniques for working with Talend components used in data filtering, sorting, deduplication, and parallel data flows.

You will explore deeper use cases and performance tuning tips for the following components:

• tFilterRow (Advanced):

  • Apply complex filter conditions using logical operators

  • Filter using expressions across multiple fields

  • Use dynamic filtering based on runtime parameters or context variables

• tSortRow (Advanced):

  • Perform multi-level sorting (e.g., sort by department, then by salary)

  • Configure custom sort orders

  • Optimize sorting for large datasets to improve job performance

• tUniqRow (Advanced):

  • Deduplicate data based on composite keys

  • Handle scenarios with partial duplicates or near-duplicates

  • Understand how tUniqRow differs from SQL-level DISTINCT

• tReplicate:

  • Create multiple parallel data flows from a single input stream

  • Use in scenarios like splitting data for separate processing or logging

  • Best practices for downstream component handling after replication

Through real-world examples and job design walkthroughs, you'll learn:

• How to combine these components for robust data quality flows

• How to use branching logic and multi-path outputs for conditional processing

• How to improve readability, efficiency, and modularity in your Talend jobs

By the end of this session, you’ll be equipped to use these components in complex ETL scenarios, helping ensure clean, well-organized, and high-quality data pipelines.

you'll learn how to control the volume of data being processed in Talend by working with sampling, header/footer configuration, and record limiting techniques. These tools are especially useful for testing, debugging, and performance tuning in ETL development.

Key concepts covered in this session include:

• tSampleRow:

  • Extract a specific number of rows from the dataset (top N, random, or based on interval)

  • Use in job design to test logic on smaller data sets

  • Understand sampling modes: First, Last, Every Nth Row, Random Sample

• Header/Footer Handling:

  • Configure tFileInputDelimited to skip headers or footers

  • Manage files with titles, summaries, or trailers

  • Apply this feature when working with real-world data dumps or log files

• Limit Functionality:

  • Limit rows at the component level for controlled data processing

  • Use the "Limit" field in tInput components to restrict records

  • Compare this to SQL-level LIMIT/ROWNUM filters

By the end of this session, you will be able to:

• Efficiently sample and control the volume of data passing through your ETL jobs

• Safely process files with headers and footers

• Use record limits to build lightweight test jobs and quick validations

These techniques are critical for faster development, clean test cases, and data validation in production workflows.

you will learn how to use the tUnite component in Talend to combine data from multiple input sources into a single unified flow, replicating the behavior of the SQL UNION ALL operation.

This session covers:

• The purpose and use cases of tUnite

• How to merge data from two or more input components (files, tables, APIs, etc.)

• Schema alignment requirements — ensuring consistent structure across all input sources

• Understanding how tUnite differs from tMap and SQL UNION DISTINCT

• Real-world use cases such as:

  • Merging sales data from multiple regions

  • Combining multiple monthly extracts into a consolidated flow

  • Integrating datasets from different file types or databases

You will also learn:

• How to use tUnite efficiently in performance-sensitive jobs

• How to debug schema mismatches and ensure smooth data merging

• When to use tUnite vs. conditional joins or mapping logic

By the end of this session, you will be able to confidently apply tUnite to build scalable, consolidated data pipelines in Talend that handle data from multiple sources with ease.

In Session 8, you'll learn how to automate the loading of multiple input files with the same structure (metadata/schema) into a single target table or file using Talend’s tFileList component.

This session will teach you how to:

• Use tFileList to loop through all files in a folder (e.g., daily/monthly sales files, logs, or extracts)

• Connect tFileList with tFileInputDelimited to dynamically process file-by-file

• Reuse metadata across all files (same columns, delimiters, and data types)

• Append data into a single output destination such as:

  • A database table (e.g., Oracle, MySQL, PostgreSQL)

  • A consolidated flat file (CSV, TXT, etc.)

• Handle scenarios where file names change dynamically or are date-based

• Apply logging or error handling during iteration

Real-world use cases include:

• Loading all monthly sales reports into a central warehouse

• Merging CSV logs from different servers or locations

• Consolidating regional data exports into one common structure

By the end of this session, you’ll be able to build a dynamic Talend job that processes any number of input files in a folder and loads them into a single, clean output stream, reducing manual effort and improving automation.

covering PreJob, PostJob, Multi-thread Execution, tHashOutput/Input, tBufferOutput/Input, tNormalize, and tDenormalize — ideal for advanced Talend automation and optimization concepts:

Lecture Title: Job Control, Multithreading, and Data Structure Conversion – PreJob, PostJob, Hashing, Buffering, Normalize/Denormalize

Lecture Description:

In Session 9, you'll explore advanced Talend components and techniques that enhance job control, performance, and data structure flexibility. This session focuses on Talend’s job orchestration tools and data transformation patterns that are essential in real-world ETL design.

Topics covered include:

Job Control:

• PreJob and PostJob:

  • Execute preparatory or cleanup tasks (e.g., logging, validations, environment setup)

  • Connect PreJob/PostJob to main flows to ensure sequence integrity

  • Real-world example: Check DB connection before job and send email after job completion

Performance & Parallelism:

• Multithread Execution:

  • Enable and configure parallel execution of sub-jobs

  • Boost performance for large or split workflows

  • Caution areas: thread safety, shared resources, and join conditions

In-Memory Processing:

• tHashOutput / tHashInput:

  • Share lookup or intermediate data between sub-jobs without writing to disk

  • Useful for joining large datasets or when reusing lookup data in different parts of the job

• tBufferOutput / tBufferInput:

  • Enable in-memory data flow between parent and child jobs

  • Use in scenarios where child jobs act like subroutines returning rows to main flow

Data Restructuring:

• tNormalize:

  • Convert columns to multiple rows (flattening repeated fields)

  • Useful in splitting delimited data into structured rows (e.g., order items per order ID)

• tDenormalize:

  • Convert multiple rows into a single row by aggregating or combining fields

  • Common in reporting formats, pivoted views, or grouped summaries

By the end of this session, you will be able to:

• Design modular, scalable Talend jobs with PreJob/PostJob handling

• Improve job performance through multithreading and memory-based operations

• Transform data structures using normalize/denormalize logic

• Reduce I/O overhead with in-memory data sharing using Hash and Buffer components

This session lays the groundwork for building enterprise-grade ETL solutions using Talend.

we take a deep dive into one of the most important and flexible components in Talend — the tMap. This session will give you a complete understanding of how tMap works and how it can be used to handle complex data transformations, joins, filters, and expressions within your ETL jobs.

You will learn:

• The structure and layout of the tMap interface: input area, expression editor, and output mapping

• How to perform column-to-column mappings from input to output schemas

• How to apply row-level transformations using Talend expression language

• How to use tMap for:

  • Filtering rows (equivalent to a WHERE clause)

  • Joining multiple inputs (inner, left outer joins)

  • Creating multiple outputs with different conditions

  • Handling nulls, case conversions, string/date manipulations

Advanced Concepts Covered:

• Lookup joins with tMap using static and dynamic data

• Handling unmatched records with join models (Inner, Left Outer, etc.)

• Using variables inside tMap to simplify complex expressions

• Best practices for performance tuning and debugging in large mappings

By the end of this session, you’ll be able to use tMap to:

• Build dynamic and powerful transformation logic

• Handle multi-source joins and conditional routing

• Replace complex SQL with visual mapping logic

• Optimize mappings for performance and reusability

tMap is considered the core of Talend development, and mastering it will make you more effective in designing and maintaining scalable ETL jobs.

we continue working with the tMap component, focusing specifically on string functions and how to use them effectively within transformation logic. String operations are crucial in ETL jobs for cleaning, formatting, and standardizing textual data before loading it into target systems.

In this session, you will learn:

• How to use Talend’s built-in string functions inside tMap, including:

  • StringHandling.UPCASE() / LOWCASE() – for upper/lowercase conversions

  • TRIM(), LEFT(), RIGHT(), SUBSTRING() – for trimming and extracting substrings

  • REPLACE(), CONCAT(), SPLIT() – for pattern and delimiter handling

  • StringHandling.CONTAINS(), STARTSWITH(), ENDSWITH() – for conditional string filtering

• Real-world use cases:

  • Cleaning inconsistent customer names

  • Extracting domain names from email addresses

  • Parsing codes or splitting delimited fields

  • Masking sensitive fields (e.g., showing only last 4 digits of a number)

• How to combine string functions with conditional expressions and tMap variables

• Tips for null-safe string handling and avoiding transformation errors

By the end of this session, you will be able to:

• Perform dynamic and complex string transformations inside tMap

• Clean and standardize textual data before loading to targets

• Build readable, efficient, and reusable transformation logic using string expressions

Mastering string manipulation inside tMap helps create high-quality, production-ready ETL flows where data consistency and formatting matter.

we continue working with the tMap component, focusing specifically on string functions and how to use them effectively within transformation logic. String operations are crucial in ETL jobs for cleaning, formatting, and standardizing textual data before loading it into target systems.

In this session, you will learn:

• How to use Talend’s built-in string functions inside tMap, including:

  • StringHandling.UPCASE() / LOWCASE() – for upper/lowercase conversions

  • TRIM(), LEFT(), RIGHT(), SUBSTRING() – for trimming and extracting substrings

  • REPLACE(), CONCAT(), SPLIT() – for pattern and delimiter handling

  • StringHandling.CONTAINS(), STARTSWITH(), ENDSWITH() – for conditional string filtering

• Real-world use cases:

  • Cleaning inconsistent customer names

  • Extracting domain names from email addresses

  • Parsing codes or splitting delimited fields

  • Masking sensitive fields (e.g., showing only last 4 digits of a number)

• How to combine string functions with conditional expressions and tMap variables

• Tips for null-safe string handling and avoiding transformation errors

By the end of this session, you will be able to:

• Perform dynamic and complex string transformations inside tMap

• Clean and standardize textual data before loading to targets

• Build readable, efficient, and reusable transformation logic using string expressions

Mastering string manipulation inside tMap helps create high-quality, production-ready ETL flows where data consistency and formatting matter.

you'll learn how to use Date Functions within Talend's tMap component to manipulate, format, and calculate values from date and time fields. This is a crucial skill for ETL developers working with time-based data such as transactions, logs, customer activity, or system events.

In this session, you will explore:

• Commonly used Talend date functions inside tMap, such as:

  • TalendDate.getCurrentDate() – to get the system date/time

  • TalendDate.formatDate() – to convert date objects into strings

  • TalendDate.parseDate() – to convert strings into date objects

  • TalendDate.addDate(), addDays(), addMonths() – for date arithmetic

  • TalendDate.diffDate() – to calculate differences between dates

  • TalendDate.compareDate() – for conditional filtering

• Practical examples:

  • Convert string-based dates into database-compatible date formats

  • Calculate customer age from birthdate

  • Derive reporting month/quarter/year from transaction dates

  • Find records older/newer than a specific threshold

• Best practices:

  • Handling null or incorrectly formatted date values

  • Using date functions inside tMap variables for clarity

  • Working with time zones and formatting standards (yyyy-MM-dd, dd-MM-yyyy, etc.)

By the end of this session, you will be able to:

• Apply date parsing, formatting, and arithmetic in your Talend jobs

• Transform and validate temporal data inside tMap

• Design logic based on date conditions and relative time filters

These skills are especially useful for building time-sensitive ETL pipelines such as historical reporting, data aging, and time-based data warehousing.

you'll learn how to use Date Functions within Talend's tMap component to manipulate, format, and calculate values from date and time fields. This is a crucial skill for ETL developers working with time-based data such as transactions, logs, customer activity, or system events.

In this session, you will explore:

• Commonly used Talend date functions inside tMap, such as:

  • TalendDate.getCurrentDate() – to get the system date/time

  • TalendDate.formatDate() – to convert date objects into strings

  • TalendDate.parseDate() – to convert strings into date objects

  • TalendDate.addDate(), addDays(), addMonths() – for date arithmetic

  • TalendDate.diffDate() – to calculate differences between dates

  • TalendDate.compareDate() – for conditional filtering

• Practical examples:

  • Convert string-based dates into database-compatible date formats

  • Calculate customer age from birthdate

  • Derive reporting month/quarter/year from transaction dates

  • Find records older/newer than a specific threshold

• Best practices:

  • Handling null or incorrectly formatted date values

  • Using date functions inside tMap variables for clarity

  • Working with time zones and formatting standards (yyyy-MM-dd, dd-MM-yyyy, etc.)

By the end of this session, you will be able to:

• Apply date parsing, formatting, and arithmetic in your Talend jobs

• Transform and validate temporal data inside tMap

• Design logic based on date conditions and relative time filters

These skills are especially useful for building time-sensitive ETL pipelines such as historical reporting, data aging, and time-based data warehousing.

In this session, you will learn how to make your tMap transformations more robust by handling null values, avoiding NullPointerExceptions, and generating numeric sequences for surrogate keys or row identifiers.

This session focuses on:

Null and Error Handling in tMap:

• How null values can cause runtime errors, especially with string/date/number operations

• Avoiding NullPointerException using:

  • row.column != null ? row.column : "default" (ternary operator)

  • StringHandling.ISNULL() function for safer expressions

  • Default values for missing fields (e.g., 0, "", "Unknown")

• Practical examples:

  • Replacing null names with “Unknown”

  • Assigning 0 to null sales values

  • Handling null dates gracefully

Generating Numeric Sequences:

• Using Numeric.sequence("seq", 1, 1) inside tMap to:

  • Generate unique IDs for rows

  • Create surrogate keys for dimension tables

  • Maintain sequential numbering even across large datasets

• Best practices when using Numeric.sequence:

  • Ensure it's used in a single-threaded flow unless configured carefully

  • Resetting the sequence only when needed

By the end of this session, you will be able to:

• Prevent and handle NullPointerExceptions during transformation

• Write safer, more stable tMap expressions using conditional logic

• Generate custom numeric sequences to enrich and uniquely identify your data

These techniques help ensure your ETL jobs are reliable, error-free, and ready for production-level deployments.

In this session, you will explore how to use Talend’s tMap component for mathematical calculations and data joins between multiple sources. These are critical features for building analytical and integrated ETL workflows.

Mathematical Functions in tMap:

• Perform numeric transformations such as:

  • Math.abs(), Math.round(), Math.floor(), Math.ceil()

  • Math.pow(), Math.sqrt() for power and root operations

  • Arithmetic operations: +, -, *, /, %

  • Conditional calculations using the ternary operator (e.g., apply discount if quantity > 10)

• Real-world examples:

  • Calculate revenue = quantity × unit price

  • Round off totals for financial reporting

  • Derive tax, discount, or percentage contributions

Joins Using tMap:

• Combine data from multiple input sources using tMap as a join processor

• Types of joins supported:

  • Inner Join: Only matching records from both sides

  • Left Outer Join: All records from the main table + matching data from lookup

• Configure lookup tables inside tMap and choose join models

• Handle unmatched records with default/null values or filters

• Use case examples:

  • Join customer and order tables to create customer order history

  • Combine product and pricing tables for enriched output

By the end of this session, you will be able to:

• Apply complex mathematical formulas directly inside tMap

• Use lookup joins to merge data from multiple sources

• Handle different join types confidently and apply filtering to joined results

This session strengthens your ability to build real-time reporting, financial processing, and data integration logic using Talend's most powerful component.

In this session, you will explore how to use Talend’s tMap component for mathematical calculations and data joins between multiple sources. These are critical features for building analytical and integrated ETL workflows.

Mathematical Functions in tMap:

• Perform numeric transformations such as:

  • Math.abs(), Math.round(), Math.floor(), Math.ceil()

  • Math.pow(), Math.sqrt() for power and root operations

  • Arithmetic operations: +, -, *, /, %

  • Conditional calculations using the ternary operator (e.g., apply discount if quantity > 10)

• Real-world examples:

  • Calculate revenue = quantity × unit price

  • Round off totals for financial reporting

  • Derive tax, discount, or percentage contributions

Joins Using tMap:

• Combine data from multiple input sources using tMap as a join processor

• Types of joins supported:

  • Inner Join: Only matching records from both sides

  • Left Outer Join: All records from the main table + matching data from lookup

• Configure lookup tables inside tMap and choose join models

• Handle unmatched records with default/null values or filters

• Use case examples:

  • Join customer and order tables to create customer order history

  • Combine product and pricing tables for enriched output

By the end of this session, you will be able to:

• Apply complex mathematical formulas directly inside tMap

• Use lookup joins to merge data from multiple sources

• Handle different join types confidently and apply filtering to joined results

This session strengthens your ability to build real-time reporting, financial processing, and data integration logic using Talend's most powerful component.

In this advanced session, you'll learn how to fine-tune Talend's tMap performance by choosing the right lookup model, managing heap memory usage, and handling temporary in-memory data effectively.

This session focuses on understanding Talend’s internal data handling during joins and lookups, especially when working with large datasets.

Lookup Models in tMap:

• Overview of lookup types:

  • Load once (default) – for small, static datasets

  • Reload at each row – for dynamic lookups that depend on main input

  • Cache – stores lookup data in memory for performance

• When and how to use each model efficiently

• Lookup performance tips:

  • Use key-based joins to improve lookup speed

  • Filter lookup data before it enters tMap

Heap Memory and Large Lookups:

• Understand how large lookups affect Java heap space

• Common issues: OutOfMemoryError, slow performance, and job failure

• Best practices:

  • Use tHashOutput/tHashInput or tFileInputDelimited for lookup staging

  • Increase JVM heap memory settings when needed

  • Use store on disk (if available) for memory-heavy joins

Temporary Data Handling:

• How Talend stores lookup data temporarily in memory or disk (depending on configuration)

• Using tHashOutput / tHashInput as reusable in-memory lookup datasets

• Avoiding memory bottlenecks in multi-join workflows

By the end of this session, you’ll be able to:

• Choose the right lookup strategy for optimal job performance

• Prevent memory-related job failures using best practices

• Efficiently manage large joins using in-memory vs. disk-based storage

• Build scalable, memory-aware ETL jobs with tMap at the center

In this advanced session, you'll learn how to fine-tune Talend's tMap performance by choosing the right lookup model, managing heap memory usage, and handling temporary in-memory data effectively.

This session focuses on understanding Talend’s internal data handling during joins and lookups, especially when working with large datasets.

Lookup Models in tMap:

• Overview of lookup types:

  • Load once (default) – for small, static datasets

  • Reload at each row – for dynamic lookups that depend on main input

  • Cache – stores lookup data in memory for performance

• When and how to use each model efficiently

• Lookup performance tips:

  • Use key-based joins to improve lookup speed

  • Filter lookup data before it enters tMap

Heap Memory and Large Lookups:

• Understand how large lookups affect Java heap space

• Common issues: OutOfMemoryError, slow performance, and job failure

• Best practices:

  • Use tHashOutput/tHashInput or tFileInputDelimited for lookup staging

  • Increase JVM heap memory settings when needed

  • Use store on disk (if available) for memory-heavy joins

Temporary Data Handling:

• How Talend stores lookup data temporarily in memory or disk (depending on configuration)

• Using tHashOutput / tHashInput as reusable in-memory lookup datasets

• Avoiding memory bottlenecks in multi-join workflows

By the end of this session, you’ll be able to:

• Choose the right lookup strategy for optimal job performance

• Prevent memory-related job failures using best practices

• Efficiently manage large joins using in-memory vs. disk-based storage

• Build scalable, memory-aware ETL jobs with tMap at the center

In this session, you’ll learn how to use the tJoin component in Talend to perform joins between datasets, just like SQL joins. This component is essential when you need to combine data from multiple sources with precision and performance.

What You’ll Learn:

✅ Understanding tJoin:

• How tJoin works behind the scenes (main flow vs. lookup flow)

• Differences between tJoin and tMap when joining datasets

✅ Types of Joins:

• Inner Join: Combine only matching rows from both datasets

• Left Outer Join: Keep all records from the main flow and match with available data from the lookup

• How to simulate right outer and full joins using Talend components

✅ Configuring tJoin:

• Defining join keys and matching conditions

• Using case sensitivity and null handling options

• Optimizing joins for performance and memory usage

Real-World Use Cases:

• Join customer master data with transaction details

• Enrich sales records with product or region metadata

• Identify unmatched records using left joins and process separately

By the end of this session, you’ll be able to:

• Configure and execute inner and outer joins using tJoin

• Handle mismatches and null joins effectively

• Understand when to use tJoin vs. tMap based on job complexity

In this session, we dive deep into how Talend integrates with Java through its powerful scripting components: tJava, tJavaRow, and tJavaFlex. These components are essential when default Talend functions are not enough and custom Java logic is required to manipulate, transform, or control your ETL flow.

What You’ll Learn:

✅ tJava:

• Insert Java code to execute once during job execution (e.g., logging, variable declarations, timestamps)

✅ tJavaRow:

• Apply row-level Java logic for each record in a flow

• Use cases include:

  • String formatting, case conversion

  • Conditional checks and transformations

  • Mathematical calculations

✅ tJavaFlex:

• Use full control with three Java code blocks:

  • Start Code: Executed once before any rows

  • Main Code: Executed for every incoming row

  • End Code: Executed once after all rows are processed

• Best for complex business logic that spans initialization, processing, and cleanup

Use Cases Covered:

• Dynamically generate UUIDs, timestamps, or calculated fields

• Write custom date parsers or data validators

• Log start/end of processing with record counts

• Perform lookups or transformations using Java collections or conditions

Bonus:

• Common Java methods used in ETL (e.g., substring(), replace(), parseInt(), SimpleDateFormat)

• Error handling within Java code blocks

By the end of this session, you’ll be able to:

• Decide when and how to use each Java-related component in Talend

• Inject efficient Java code into your jobs to extend Talend’s capabilities

• Build dynamic and flexible jobs using a hybrid of Talend and Java

In this session, we explore two essential Talend components that serve very different but powerful purposes: tAggregateRow for summarizing data, and tRowGenerator for creating synthetic/test data without source files.

What You’ll Learn:

✅ tAggregateRow – Grouping & Summarization:

• Perform aggregations like SUM, AVG, COUNT, MIN, and MAX

• Group data by one or more columns (e.g., Region, Category)

• Design use cases like:

  • Total sales per product or store

  • Average scores per department

  • Count of transactions by status

✅ tRowGenerator – Generate Sample Data:

• Quickly create mock/test data without external files or databases

• Define field types (Integer, String, Date, etc.) and value patterns

• Useful for:

  • Demo jobs

  • Testing logic without production data

  • Learning and POCs

Hands-On Scenarios:

• Aggregate retail sales by product category using tAggregateRow

• Create 1000 dummy customer records using tRowGenerator

• Combine both: Generate test sales data and then group by region for analytics

By the end of this session, you’ll be able to:

• Build reusable aggregation logic for reporting and data preparation

• Generate flexible test data for development, debugging, or training

• Understand the importance of data summarization in ETL and BI workflows

In this session, we focus on Context Variables and Context Groups — powerful features in Talend that help you build flexible, reusable, and environment-independent ETL jobs.

These tools allow you to run the same job in different environments (Dev, QA, Prod) without changing the job logic, simply by switching context values.

What You’ll Learn:

✅ Context Variables:

• Create and use variables for database connections, file paths, filters, and dynamic values

• Set and access context variables in components like tFileInputDelimited, tDBConnection, etc.

• Use context.<variableName> syntax across your job

✅ Context Groups:

• Organize related context variables into groups for easier management

• Define multiple environment configurations (e.g., Dev, Test, Prod)

• Switch between environments with a single click

✅ Dynamic Value Handling:

• Assign values from job parameters, parent jobs, or through command-line execution

• Pass context values between jobs using tRunJob

• Load external .properties or .csv files at runtime

Real-World Use Cases:

• Run the same job for multiple databases or folders by changing only the context

• Deploy your job to different environments without editing any components

• Parameterize data filters, filenames, limits, or thresholds

By the end of this session, you’ll be able to:

• Implement context-driven jobs that are reusable and maintainable

• Build environment-agnostic pipelines suitable for production deployments

• Enhance job flexibility and reduce hardcoded values

In this session, we focus on Context Variables and Context Groups — powerful features in Talend that help you build flexible, reusable, and environment-independent ETL jobs.

These tools allow you to run the same job in different environments (Dev, QA, Prod) without changing the job logic, simply by switching context values.

What You’ll Learn:

✅ Context Variables:

• Create and use variables for database connections, file paths, filters, and dynamic values

• Set and access context variables in components like tFileInputDelimited, tDBConnection, etc.

• Use context.<variableName> syntax across your job

✅ Context Groups:

• Organize related context variables into groups for easier management

• Define multiple environment configurations (e.g., Dev, Test, Prod)

• Switch between environments with a single click

✅ Dynamic Value Handling:

• Assign values from job parameters, parent jobs, or through command-line execution

• Pass context values between jobs using tRunJob

• Load external .properties or .csv files at runtime

Real-World Use Cases:

• Run the same job for multiple databases or folders by changing only the context

• Deploy your job to different environments without editing any components

• Parameterize data filters, filenames, limits, or thresholds

By the end of this session, you’ll be able to:

• Implement context-driven jobs that are reusable and maintainable

• Build environment-agnostic pipelines suitable for production deployments

• Enhance job flexibility and reduce hardcoded values

In this session, we explore tLogCatcher and tStatCatcher, two essential components for job monitoring, debugging, and logging in Talend. These tools help track execution behavior, capture errors, and log performance stats — crucial for production-level ETL workflows.

What You’ll Learn:

✅ tLogCatcher:

• Automatically capture Java exceptions, component errors, and tDie messages

• Log details such as job name, timestamp, component name, and error message

• Route logs to console, files, or databases for auditing or alerting

✅ tStatCatcher:

• Collect execution statistics like:

  • Row counts (input/output)

  • Start and end time

  • Duration of component execution

• Useful for analyzing job performance and bottlenecks

Hands-On Scenarios:

• Track failed records and log them to a file or DB table

• Build an error handling framework using tLogCatcher and tWarn

• Monitor job performance across components using tStatCatcher

Bonus:

• How to enable Log/Stats in Project Settings

• Using tFlowToIterate and tFileOutputDelimited to store log data

• Integration with monitoring tools and error alerts

By the end of this session, you’ll be able to:

• Add robust logging and error-catching mechanisms to your Talend jobs

• Track performance and row-level metrics for optimization

• Build professional, production-ready jobs with built-in monitoring

This session focuses on advanced Talend components and features that improve data integrity, communication, and deployment in enterprise ETL pipelines. You’ll learn how to validate schema compliance, convert data types, send email notifications, and build deployable job packages.

What You’ll Learn:

✅ tSchemaCompliance – Data Validation Against Metadata:

• Automatically validate if incoming data matches a defined schema

• Detect missing fields, type mismatches, or extra columns

• Route compliant and non-compliant data for separate processing

✅ tConvertType – Dynamic Data Type Handling:

• Convert field types (e.g., String to Integer, Date to String)

• Useful when input formats are inconsistent or need transformation

• Common in file-to-database or database-to-database workflows

✅ tSendMail – Email Notifications:

• Send success or failure emails at the end of job execution

• Customize subject, body, attachments (e.g., logs or output files)

• Use for alerting operations teams or stakeholders

✅ Build Job – Packaging Talend Jobs:

• Export your job as a standalone .zip or .jar file

• Configure build options, context parameters, and environments

• Deploy jobs to Talend JobServer, cron scheduler, or remote machines

Real-World Scenarios:

• Validate incoming vendor files using tSchemaCompliance

• Convert CSV data to appropriate types before loading to DB

• Send success/failure status emails with attachments

• Build and deploy a packaged Talend job for production

By the end of this session, you’ll be able to:

• Ensure data quality through schema validation

• Handle type mismatches seamlessly

• Add alerting for real-time job tracking

• Deploy and share standalone Talend jobs across environments

In this session, we dive into advanced Talend components that give you control over job flow, system-level execution, and automation. These features are especially useful in job orchestration, batch processing, and real-time integration scenarios.

What You’ll Learn:

✅ tLoop – Repetitive Execution Logic:

• Configure for-loops, while-loops, and count-based iterations

• Use loop variables dynamically in file names, queries, or input values

• Practical examples: polling for files, processing multiple dates or IDs

✅ tSleep – Introducing Delays:

• Pause execution for a specified duration (ms/seconds)

• Useful for wait logic between retries or batch submissions

✅ Infinite Loop Handling:

• Create controlled infinite loops (e.g., for continuous file monitoring)

• Learn how to break the loop safely using custom logic or status checks

✅ tSystem – Run OS Commands:

• Execute shell commands, batch scripts, Python, or OS utilities from within Talend

• Use it for post-processing tasks like file zipping, DB backups, or alerts

✅ tSSH – Remote Execution:

• Run remote scripts or commands over SSH

• Connect securely to Linux servers for automation and integration

✅ tRunJob – Triggering Subjobs:

• Execute child jobs from a parent job

• Pass context variables, manage parallel or sequential executions

• Use for modular design and job chaining

Real-World Use Cases:

• Polling folders every 10 seconds using tLoop and tSleep

• Triggering shell scripts post data load using tSystem

• Running ETL pipelines on remote servers using tSSH

• Creating master-job orchestration using tRunJob

By the end of this session, you’ll be able to:

• Build looping logic for repeatable or continuous tasks

• Introduce time delays where needed in processing

• Execute system and remote commands as part of your job

• Design reusable, modular ETL architectures with subjob execution

In this session, you will learn how to handle file operations and integrate with FTP servers to enable secure and automated file transfers as part of your ETL workflow. These skills are essential for projects involving data exchange with external systems, vendors, or clients.

? What You’ll Learn:

✅ File Processing Components:

• Reading and writing flat files (CSV, TXT)

• Managing file paths, naming conventions, and encoding

• Handling multiple files using tFileList and dynamic file loading

✅ FTP Operations using Talend:

• tFTPConnection: Connect securely to remote FTP/SFTP servers

• tFTPPut: Upload files from your local system to an FTP server

• tFTPGet: Download files from FTP server to local directories

• tFTPDelete: Delete remote files after processing

• Error handling and connection timeout strategies

✅ Best Practices:

• Directory structure and file naming automation

• Looping through multiple files using tFileList + tFlowToIterate

• Using context variables to make FTP credentials dynamic and secure

Real-World Use Cases:

• Automate upload of daily sales reports to FTP

• Download input files from remote vendor server and load to database

• Archive files post-transfer to prevent reprocessing

By the End of This Session, You Will:

• Be able to set up secure file transfers using FTP/SFTP

• Automate dynamic file upload/download workflows

• Incorporate robust file handling logic into your ETL jobs

You’re working for a retail company that receives daily sales reports from various store locations via an FTP server. Your task is to automate the download, process the data, and load it into a database, ensuring error handling and archiving.

Objectives:

1. Connect to FTP Server to retrieve CSV sales files.

2. Download files dynamically based on today’s date.

3. Process each file (validate structure, clean data).

4. Load data into a target table (e.g., daily_sales_fact in MySQL/Oracle/PostgreSQL).

5. Archive files locally and delete them from the FTP server.

6. Log success/failure of each step for monitoring.

Talend Components Used:

• tFTPConnection, tFTPGet, tFTPDelete

• tFileList, tFileInputDelimited, tMap

• tLogRow, tWarn, tMySQLOutput / tOracleOutput

• tFileCopy, tFileArchive, tJavaRow (optional for custom validations)

• tDie, tLogCatcher for error handling

Bonus Features to Add:

• Parameterize FTP host, user, password with context variables

• Add email alerts using tSendMail for success/failure

• Enable job scheduling via cron or TMC for daily execution

Learning Outcome:

By building this project, students will:

• Gain confidence working with real FTP servers

• Understand how to build robust, production-ready Talend jobs

• Practice exception handling, file iteration, and modular job design

In this session, you'll learn how to integrate Talend with relational databases such as Oracle, MySQL, and PostgreSQL using essential DB components. You'll understand how to perform read/write operations, manage connections, and handle transactions efficiently as part of your ETL workflows.

? What You’ll Learn:

✅ Database Connectivity:

• How to configure tDBConnection for Oracle, MySQL, PostgreSQL

• Use of tDBCommit and tDBRollback for transaction control

• Dynamic parameterization using context variables

✅ Reading from Databases:

• tDBInput, tOracleInput, tMySQLInput, tPostgresqlInput

• Use of custom SQL queries, table names, and WHERE clauses

✅ Writing to Databases:

• tDBOutput, tOracleOutput, tMySQLOutput, tPostgresqlOutput

• Insert, update, and upsert modes

• Error handling: reject flows and batch execution

✅ Advanced Components:

• tDBRow: Execute custom DML/DDL SQL scripts inside Talend jobs

• tDBSQLRow: Run multiple SQL statements in one go

• tDBTable: Create or drop tables dynamically during job execution

✅ Database Metadata:

• Importing schemas from databases for mapping

• Handling data types, primary keys, nullability during mapping

? Use Cases Covered:

• Load cleaned customer data into a target Customer_Dim table

• Extract sales transactions from a live DB for data warehouse loading

• Perform incremental load using SQL queries with timestamps

? By the End of This Session, You Will:

• Be confident in reading/writing to any supported database using Talend

• Understand how to handle transactions and control commits/rollbacks

• Use Talend DB components for flexible and dynamic SQL execution

This session focuses on Bulk Load Components in Talend, which are designed to handle large volumes of data efficiently. When performance is critical—such as in daily batch ETL jobs or data warehouse loads—using bulk components can significantly reduce load time compared to traditional row-by-row insertion.

? What You’ll Learn:

✅ Why Bulk Loading Matters:

• Difference between standard tDBOutput vs bulk components

• When and why to use bulk load strategies in ETL

✅ Talend Bulk Components Explained:

• tMySQLBulkExec, tPostgresqlBulkExec, tOracleBulkExec

• How they use intermediate files (e.g., CSV) for high-speed loads

• Temporary file generation and linking with execution components

✅ Workflow Example:

• tFileOutputDelimited → bulk file creation

• tMySQLBulkExec or similar → import into target database table

✅ Configuration Tips:

• Handling field separators, nulls, headers, and encodings

• Table truncation options

• Commit control and performance tuning settings

✅ Bulk with Transaction Control:

• Using tDBConnection, tDBCommit, tDBRollback with bulk

? Real-World Use Case:

• Load 1 million+ transaction records into a Sales_Fact table using tPostgresqlBulkExec in under a minute

• Automate a daily batch that extracts from source, generates CSV, and loads to DW with high efficiency

? Tools Used:

• tFileOutputDelimited

• tMySQLBulkExec, tOracleBulkExec, tPostgresqlBulkExec

• tDBConnection, tDBCommit, tDBRollback (optional)

? By the End of This Session, You Will:

• Know how to implement high-speed data loads with Talend

• Understand the difference between standard and bulk execution

• Be able to design fast, scalable ETL jobs for production environments

In this session, you'll learn how to handle Excel files (.xls/.xlsx) and positional (fixed-width) flat files using Talend components. These are common file types encountered in real-world ETL scenarios, especially in legacy systems or enterprise data exchanges.

? What You’ll Learn:

✅ Reading Excel Files:

• How to use tFileInputExcel for .xls and .xlsx files

• Configuring sheet names, header rows, and cell formats

• Handling multiple sheets or dynamic sheet names

✅ Working with Positional (Fixed-Width) Files:

• How to use tFileInputPositional to read fixed-width files

• Defining metadata manually: field lengths and column names

• Parsing and validating positional records

• Handling edge cases (missing fields, trailing spaces, etc.)

✅ Real-World Integration:

• Convert legacy mainframe extracts into structured datasets

• Transform Excel source data into a staging database

• Combine Excel + positional sources using tUnite or tMap

? Components Covered:

• tFileInputExcel

• tFileInputPositional

• tMap, tLogRow, tFileOutputDelimited, etc.

? Use Cases:

• Reading supplier master from Excel and loading to MySQL

• Parsing mainframe-exported sales records from fixed-width files

• Combining Excel + flat files into a single unified data pipeline

? By the End of This Session, You Will:

• Be able to read Excel and positional files confidently

• Design flexible jobs for mixed-format data ingestion

• Handle legacy file formats as part of your ETL pipeline

In this session, you’ll learn how to read, transform, and write XML and JSON data using Talend. These formats are common in web services, APIs, and data interchange between applications. You'll master Talend components like tFileInputXML, tXMLMap, tExtractXMLField, tFileInputJSON, and tExtractJSONFields.

? What You’ll Learn:

✅ XML Handling:

• Use tFileInputXML to read XML files with nested or repeated structures

• Define XPaths manually or with Talend's schema editor

• Use tXMLMap to map, transform, and flatten complex XML

• Extract multiple nodes and attributes using loops

✅ JSON Handling:

• Use tFileInputJSON and tExtractJSONFields for structured and nested JSON

• Read from local files or web responses (combined with tRestClient)

• Define JSONPath expressions to extract key data points

? Components Covered:

• tFileInputXML, tXMLMap, tExtractXMLField

• tFileInputJSON, tExtractJSONFields, tRestClient

• tLogRow, tMap, tFileOutputDelimited

? Use Cases:

• Read an XML product catalog and load it into a relational database

• Parse JSON customer transactions received from an API

• Combine XML and JSON sources using tMap and load into a warehouse

? Tips & Best Practices:

• When to use loop XPath vs absolute XPath

• Handling null/missing values and unexpected formats

• Flattening hierarchical data for database storage

• Schema inference vs manual metadata definition

? By the End of This Session, You Will:

• Be able to parse and transform both XML and JSON in Talend

• Use Talend’s powerful mapping components to flatten nested structures

• Build flexible pipelines for semi-structured data ingestion

In this session, you will learn how to structure and manage multiple environments (Development, QA, and Production) in real-world Talend projects. Understanding how to handle environment-specific configurations is crucial for ensuring smooth deployment, testing, and maintenance of ETL pipelines.

? What You’ll Learn:

✅ Environment Setup Strategy:

• Importance of separating Dev, QA, and Prod environments

• Folder structure and best practices for managing multiple Talend environments

• Environment-specific parameterization using context variables and context groups

✅ Context Management:

• Create and manage reusable context groups

• Store environment values (like DB URLs, credentials, file paths) for Dev, QA, and Prod

• Dynamically switch between environments using tContextLoad, .properties files, or command-line parameters

✅ Deployment Workflow:

• How to export jobs with environment bindings

• Use of Talend TAC/TMC for deploying and scheduling across environments

• Promote code from Dev → QA → Prod with minimal risk

✅ Best Practices:

• Version control integration (e.g., Git)

• Audit logs and rollback handling

• Change management and testing before production deployment

? Real-World Use Cases:

• One-click deployment across environments using parameterized builds

• Scheduled production jobs pulling credentials and DB configs dynamically

• QA testing using masked data with different FTP paths or email IDs

? By the End of This Session, You Will:

• Confidently manage and configure Dev, QA, and Prod environments in Talend

• Build jobs that are environment-independent and scalable

• Follow enterprise-grade practices for deploying ETL workflows

In this session, you'll gain insights into how real-world Talend and Data Warehousing projects are executed in enterprise environments. You'll understand how teams are organized, how clients engage with service providers, and how Agile methodology is applied in ETL development and delivery.

? What You’ll Learn:

✅ Team Hierarchy & Roles:

• Typical project structure in a Talend/Data Engineering team

  • ETL Developer

  • Data Analyst

  • QA/Testing Team

  • Scrum Master

  • Technical Architect

  • DevOps/Deployment Team

• Role of each member and how they collaborate

✅ Client & Vendor Collaboration:

• How service-based companies (like LTI, TCS, Infosys) deliver solutions to clients

• Client-side roles: Product Owner, Data Owner, Business Users

• Vendor-side roles: Onsite/Offshore leads, Developers, Coordinators

• Communication models and reporting structures

✅ Agile in ETL Projects:

• Overview of Agile principles: Sprints, Scrum, Daily Standups

• Using tools like JIRA or Azure DevOps to track ETL tasks

• Writing User Stories, defining Acceptance Criteria for data jobs

• How Talend jobs are planned, developed, tested, and demoed in sprints

✅ Project Lifecycle Overview:

• Requirement gathering → Design → Development → Testing → Deployment → Maintenance

• Role of UAT (User Acceptance Testing) and Production Support

? Real-World Scenarios:

• A retail client asks for a new sales report: how it's estimated, developed, and delivered

• How bugs in QA are handled and communicated between teams

• Weekly sprint planning and demos with the client

? By the End of This Session, You Will:

• Understand how Talend projects function in corporate environments

• Know your potential role in a real-time team

• Be prepared for interviews with questions on Agile and team structure

• Communicate effectively with clients and cross-functional teams

In this session, you'll learn the core building blocks of Data Warehousing: Fact tables and Dimension tables. These concepts are essential to understanding how analytical databases are designed, queried, and optimized for reporting and business intelligence.

? What You’ll Learn:

✅ Fact Tables:

• What are Fact tables?

• Types of facts: Additive, Semi-additive, Non-additive

• Examples: Sales Fact, Order Fact, Transaction Fact

• Grain of the fact table (importance of granularity)

✅ Dimension Tables:

• What are Dimension tables?

• Examples: Customer, Product, Time, Location

• Slowly Changing Dimensions (SCD Types 1, 2, 3 – overview)

• Surrogate keys vs natural keys

✅ Fact-Dimension Relationship:

• Star Schema vs Snowflake Schema

• Primary and foreign key relationships

• Handling many-to-many relationships (bridge tables)

? Real-World Examples:

• A Retail Sales DWH: Sales_Fact, Date_Dim, Store_Dim, Product_Dim

• Healthcare: Patient_Fact with Doctor_Dim and Treatment_Dim

• Finance: Transaction_Fact with Account_Dim and Branch_Dim

? By the End of This Session, You Will:

• Be able to identify fact and dimension tables in any data model

• Understand how to design or read star and snowflake schemas

• Know how facts and dimensions power business intelligence and analytics

• Be confident in discussing DWH design in interviews or client meetings

In this session, we will explore the theoretical foundation of Slowly Changing Dimensions (SCD)—a critical concept in Data Warehousing. You’ll learn how organizations handle changes in dimensional data over time and why proper SCD implementation is vital for historical accuracy and reporting.

? What You’ll Learn:

✅ Why SCD Is Needed:

• Understanding the need to track changes in dimension data

• Real-world impact of historical changes (e.g., address change, product price update)

✅ SCD Type 1 (Overwrite):

• Description: Updates the existing record; no history is kept

• Use Cases: Correcting errors like misspelled names

• Pros & Cons

✅ SCD Type 2 (Track History):

• Description: Inserts a new row for every change, maintaining full historical data

• Attributes: Surrogate Key, Effective Date, Expiry Date, Current Flag

• Use Cases: Address change, job title changes, etc.

• Example: Tracking employee's department over time

✅ SCD Type 3 (Partial History):

• Description: Stores previous value in additional columns

• Use Cases: Only limited historical data needed (e.g., previous address)

• Pros & Limitations

? Real-World Examples:

• Retail: Tracking customer address history over years

• HR Data: Monitoring changes in employee designations

• Banking: Change in account type or branch movement

? By the End of This Session, You Will:

• Clearly understand the differences between SCD Type 1, Type 2, and Type 3

• Be able to choose the appropriate SCD type based on business requirements

• Prepare for real-time DWH design and Talend implementations of SCD

• Answer SCD-related questions confidently in interviews

In this session, we move from theory to hands-on implementation by exploring how to apply SCD Type 1 (Slowly Changing Dimension – Type 1) logic using Talend Open Studio. You'll learn how to overwrite dimension data in the target system whenever updates occur, without preserving history.

? What You’ll Learn:

✅ SCD Type 1 Recap

• Overwrites old data with new values

• No historical tracking

• Used when only the most recent information is needed

✅ Talend Components Involved:

• tMap: for join and field-level comparison

• tDBInput: source and target data

• tDBOutput or tOutputActionOnUpdate: update records

• Optional: tLogRow for debugging

✅ Step-by-Step Implementation:

• Extract data from source and dimension table

• Join on business/natural key

• Compare fields to detect changes

• Update only if data is different

• Handle insert of new records if needed

✅ Best Practices:

• Use hashing or field-by-field comparison to detect changes

• Audit changed records using tLogRow or write to a log table

• Validate updates with test scenarios

? Real-World Use Case:

• Updating customer phone numbers or email addresses

• Correcting typos in product descriptions

• Fixing employee name spelling mistakes

? By the End of This Session, You Will:

• Understand the logic behind SCD Type 1 updates

• Be able to implement overwrite logic using Talend’s drag-and-drop interface

• Confidently apply SCD Type 1 in your data warehousing projects

In this session, we dive deep into implementing Slowly Changing Dimension Type 2 (SCD2) using Talend Open Studio. SCD2 is essential for maintaining a full history of changes in dimensional data. You’ll learn to create logic that tracks changes over time by inserting new records instead of updating existing ones.

? What You’ll Learn:

✅ SCD Type 2 Recap

• Tracks full history of changes

• Inserts a new row for each change

• Maintains previous versions with timestamps or flags

✅ Talend Components Involved:

• tMap: compare source and target data

• tDBInput: fetch dimension and source data

• tHashInput/tHashOutput: for in-memory lookups (optional)

• tFilterRow: detect changed rows

• tDBOutput: insert new rows with metadata

• Optional: tLogRow, tDenormalize, tUniqRow

✅ Step-by-Step Implementation:

1. Extract current dimension and new source data

2. Join using business key (natural key)

3. Compare dimension fields (non-key attributes)

4. If change detected:

   • Expire existing record (set end_date, current_flag = N)

   • Insert a new row with updated values and current_flag = Y

5. If no change: skip or log as no update

✅ Metadata Fields Used:

• Surrogate Key

• Start Date / End Date

• Current Flag

• Version Number (optional)

? Real-World Use Cases:

• Tracking customer's address or phone number history

• Monitoring changes in employee department over time

• Maintaining price history for a product

? By the End of This Session, You Will:

• Be able to identify when to use SCD Type 2

• Design and implement history-preserving ETL logic

• Use Talend to manage versioning and audit trails in DWH

• Handle surrogate keys, date handling, and version tracking in practice

In this session, you’ll learn how to implement Slowly Changing Dimension Type 3 (SCD Type 3) using Talend Open Studio. Unlike SCD Type 1 and 2, Type 3 retains only limited historical changes—typically storing the current and one previous value in the same record.

This approach is ideal when only the most recent change history needs to be preserved (e.g., current and previous region or status).

? What You’ll Learn:

✅ SCD Type 3 Recap

• Stores previous and current values in the same row

• Commonly used with two fields: current_column, previous_column

• No new row insertion or surrogate key versioning

✅ Talend Components Involved:

• tDBInput / tFileInputDelimited: fetch source and dimension data

• tMap: to compare values and map previous and current columns

• tFilterRow: filter only changed rows

• tDBOutput: to update the row with new current and old value moved to previous

✅ Implementation Steps:

1. Join source and dimension table using business key

2. Compare current value (e.g., current_region)

3. If different:

   • Move current value to previous column

   • Update current column with new value

4. If same: ignore or log

✅ Best Practices:

• Clearly define which fields should be tracked historically

• Initialize previous_ columns with null or placeholder value for new records

• Avoid using SCD3 for scenarios needing full historical analysis

? Real-World Use Cases:

• Tracking changes in customer region, employee role, or manager name

• Monitoring product status (e.g., from "Planned" to "Launched")

? By the End of This Session, You Will:

• Understand when and why to use SCD Type 3

• Implement column-level change tracking in Talend

• Design compact DWH tables with limited history

• Write efficient Talend logic to update fields conditionally

In this dual-topic session, you’ll explore two crucial concepts in data warehousing and Talend job design:

1. Dimensional modeling with Star and Snowflake schemas

2. Context variable management and passing between parent and child jobs in Talend

This session bridges theory with implementation to help you design scalable, modular ETL workflows.

? Part 1: Star vs. Snowflake Schema

✅ Star Schema:

• Denormalized structure

• One central fact table

• Directly connected dimension tables

• Faster for querying and reporting

✅ Snowflake Schema:

• Normalized structure

• Dimensions broken into sub-dimensions

• Reduced data redundancy

• Slightly complex joins, but better space optimization

✅ Use Cases & Examples:

• Retail sales (Star): Simple, faster

• Healthcare claims (Snowflake): Hierarchical dimension structures

? Part 2: Passing Context Variables (Parent ↔ Child Jobs)

✅ Context Basics:

• Define variables for environment configs (paths, DB connections, limits)

• Use groups like dev, test, prod

✅ Passing from Parent to Child:

• Use tRunJob

• Check "Transmit whole context"

• Or pass selective values via context parameters in tRunJob

✅ Passing from Child to Parent (returning values):

• Define output variables in child

• Use tBufferOutput or globalMap if needed

• Capture results in parent job using tFlowToIterate or globalMap

✅ Practical Examples:

• File path control

• DB connection config

• Dynamic table or schema names

• Returning row counts or status flags

? By the End of This Session, You Will:

• Be able to choose the right schema (star or snowflake) for your project

• Implement robust Talend job designs with dynamic context control

• Pass variables between jobs in both directions efficiently

• Understand real-time use cases of context variables in Dev → QA → Prod pipelines

In this session, we’ll shift gears from technical tools to project-level thinking and domain understanding—the crucial skills that separate a beginner from a job-ready professional.

You’ll learn how Talend is applied in real-world ETL projects across various industries, and what kind of business/domain knowledge you need to make meaningful contributions to any data engineering or BI team.

? What You'll Learn:

✅ Real-Time ETL Project Scenarios:

• End-to-end data pipelines using Talend Open Studio

• Integration with databases like Oracle, MySQL, PostgreSQL

• Working with multiple data formats: CSV, Excel, JSON, XML

• Loading data into data marts or warehouses using fact/dimension modeling

✅ Common Project Domains:

1. Retail – Sales, Inventory, Order tracking

2. Finance – Transactions, Account balance, Risk reporting

3. Healthcare – Claims data, Patient records, Billing

4. Telecom – Customer usage, Recharge behavior, Location-based data

5. E-commerce – Clickstream data, User behavior, Conversion metrics

✅ Typical ETL Pipeline Structure:

• Source system extraction

• Data cleaning and transformation

• Slowly Changing Dimensions (SCD)

• Loading to DWH fact/dimension tables

• Scheduling and monitoring with TMC or cron

✅ Domain Knowledge Importance:

• Understand business keys, metrics, and KPIs

• Read and interpret mapping documents

• Communicate with business users and analysts

• Align technical work with business goals

✅ Sample Project Walkthroughs:

• Loading daily sales data to a retail DWH

• Processing patient claims and generating audit logs

• Cleaning customer master data for reporting

? By the End of This Session, You Will:

• Understand how Talend fits into real ETL workflows

• Recognize the importance of business knowledge in technical roles

• Be familiar with multiple project domains and their data patterns

• Feel confident discussing ETL solutions in interviews and project meetings

This session introduces you to the Talend Administration Center (TAC)—the web-based administration console used in Talend Enterprise Edition. TAC is a critical component for enterprise deployments, offering full control over user roles, job orchestration, versioning, deployment, monitoring, and environment promotion.

? What You'll Learn:

✅ TAC Overview & Architecture

• How TAC fits into the Talend ecosystem

• Interactions with Talend Studio, Git/SVN, JobServer, and databases

✅ Key Modules in TAC:

• Projects: Create and manage project repositories

• Users & Roles: Assign access and permissions to developers, admins, operators

• Job Conductor: Schedule, deploy, and monitor Talend jobs

• Execution Plan: Design and manage sequences of jobs

• Monitoring: Real-time job status, error logs, and history tracking

• ESB Conductor: Manage services and APIs (for ESB projects)

• Resources & Contexts: Configure contexts (DEV, QA, PROD) and variables centrally

✅ Environment Promotion & Context Management:

• How to switch jobs between DEV, QA, and PROD using TAC

• Promote jobs without manual changes in code

✅ Scheduling Jobs:

• Use cron-like expressions

• Monitor execution history

• Set alerts and retry options

✅ Best Practices:

• Role-based access control

• Logging and audit compliance

• Secure deployment using JobServer

? By the End of This Session, You Will:

• Be confident in navigating and using Talend Administration Center (TAC)

• Know how to schedule, deploy, and monitor Talend jobs in a team or enterprise setup

• Understand how to manage users, environments, and secure resources effectively

• Prepare for real-world DevOps and Production tasks using Talend Enterprise

In this session, you'll gain an essential understanding of Talend's commercial offerings beyond Talend Open Studio. We’ll explore the capabilities of Talend Enterprise Edition and Talend for Big Data, both of which are widely used in enterprise-level data projects across industries.

? Part 1: Talend Enterprise Edition Overview

✅ What is Talend Enterprise Edition?

• A licensed version of Talend with advanced features for production-ready environments.

• Comes with tools for collaboration, monitoring, governance, and team development.

✅ Key Features:

• Talend Studio for Data Integration (Licensed) – with enhanced components and performance

• Talend Administration Center (TAC): Centralized job orchestration, user role management, job scheduling

• Git & SVN Integration: Version control for team collaboration

• Job Deployment: Build, export, and deploy jobs in secure environments

• Monitoring & Logging: Real-time status, error tracking, and audit logs

• Context Promotion: Seamless environment switching (Dev → QA → Prod)

✅ Use Cases:

• Enterprise data warehousing

• Batch ETL pipelines in regulated environments

• Governance-compliant data integration

? Part 2: Talend Big Data Overview

✅ What is Talend for Big Data?

• A specialized edition built for integrating and processing data across big data platforms.

✅ Supported Technologies:

• Hadoop Ecosystem: Hive, Pig, HDFS, HBase, Spark

• Distributed File Systems: Amazon S3, Azure Data Lake

• Real-time Tools: Kafka, Spark Streaming

• Cloud-native Support: Integration with AWS, GCP, Azure

✅ Special Big Data Components:

• tHDFSInput, tHiveInput, tPigLoad, tKafkaInput, tSparkConfiguration

• Support for MapReduce, Spark Batch, and Spark Streaming

✅ Use Cases:

• Processing high-volume transactional data

• Real-time analytics (e.g., user behavior, fraud detection)

• Data lakes for enterprise-wide analytics

? By the End of This Session, You Will:

• Understand the benefits of Talend’s Enterprise tools over open-source

• Know how Talend fits into Big Data ecosystems

• Be able to discuss enterprise architecture and tools in interviews or client meetings

• Prepare for advanced data integration roles in cloud, big data, or regulated domains

In this hands-on session, you'll learn how to integrate Talend with Amazon Web Services (AWS) to upload files to an S3 bucket—a widely used cloud storage solution in real-time data pipelines and enterprise projects.

? What You'll Learn:

✅ Introduction to AWS S3:

• What is S3?

• Bucket, Object, and Key concepts

• S3 use cases in Data Engineering

✅ Talend Components for AWS S3 Integration:

• tS3Connection: Set up secure access to AWS

• tS3Put: Upload files to a specified S3 bucket

• tS3Close: Close the S3 connection properly

✅ Authentication Methods:

• Using Access Key and Secret Key

• IAM Role-based access (if deployed on EC2)

✅ Real-Time Use Case:

• Upload local files (CSV, JSON, XML, etc.) to S3

• Validate upload and check in AWS console

✅ Error Handling & Logging:

• Handle authentication failures

• Capture upload success/failure logs

• Retry mechanism using subjobs or tLoop

✅ Security Best Practices:

• Never hard-code credentials

• Use Talend Context Variables for secure access

• Encrypt configuration files

? By the End of This Session, You Will:

• Understand how to configure and authenticate S3 access in Talend

• Upload files to AWS S3 programmatically using Talend components

• Apply these concepts to real-world cloud migration and hybrid data integration projects

• Be ready to use Talend with cloud services like AWS in production environments

This session is specially designed to prepare you for interviews in the fields of ETL Development, Data Engineering, BI, and Talend-based roles. Whether you're a fresher or an experienced professional aiming for a job switch or upgrade, this module gives you strategies, confidence, and practice to ace technical interviews.

? What You'll Learn:

✅ Interview Preparation Strategy:

• How to approach interviews for ETL/Data Engineering roles

• Resume building tips: Highlight Talend, SQL, and DWH skills

• How to present project experience and tools used

✅ Real-Time Interview Questions:

Talend:

• Difference between tMap and tJoin

• How to handle null values in Talend

• What is context variable and how do you promote environments?

• Difference between tReplicate and tFlowToIterate

• Error handling components in Talend

SQL:

• Joins, Group By, Having vs Where

• Complex queries using subqueries or CTE

• Window functions (RANK, DENSE_RANK, ROW_NUMBER)

• Real-time case-based scenarios (e.g., “top-selling product each month”)

Data Warehousing:

• Fact vs Dimension tables

• Star Schema vs Snowflake Schema

• What is Slowly Changing Dimension (SCD)?

• When to use SCD Type 1 vs Type 2

ETL Concepts:

• Difference between ETL and ELT

• Full vs Incremental load techniques

• Data Quality and Validation steps in ETL

✅ Mock Question Practice:

• Scenario-based questions with sample answers

• Questions from service-based and product-based companies

✅ Soft Skills & Behavioral Questions:

• “Tell me about yourself” for ETL profile

• How to explain your project architecture clearly

• Answering with STAR (Situation, Task, Action, Result) method

? By the End of This Session, You Will:

• Be confident in handling both technical and behavioral rounds

• Know how to explain Talend workflows and real-time scenarios clearly

• Be prepared with real interview questions and ideal responses

• Gain tips to stand out in interviews and secure your next data job