Course Syllabus

1. Module 1: Introduction to Talend and Data Integration Fundamentals
2. Topics Covered: Overview of Talend architecture and tools, setting up Talend Studio (Data Integration), creating your first ETL jobs. This module introduces the Talend Data Fabric and Talend Studio environment. You’ll learn how Talend Studio provides a GUI to build ETL workflows with pre-built components, making integration faster and easier. Key topics include the Talend repository, projects, jobs, and basic components for reading, transforming, and writing data.
3. Learning Objectives:
4. Understand Talend Studio (formerly Open Studio) and its role in the Talend Data Fabric.
5. Create a new project and Talend job using the graphical editor.
6. Use basic components to read from files or databases and write to targets.
7. Define schemas and metadata to reuse across jobs.
8. Implement simple transformations (filter, join, aggregate).
9. Run and test a job locally, and use basic error-handling components.
10. Lab Exercises:
11. Install Talend Studio (or launch Talend Cloud Pipeline Designer) and create your first project.
12. Exercise: Build a Job to extract data from a CSV file, transform a field (e.g. calculate age or format date), and load it into a sample database.
13. Configure and reuse shared metadata (e.g. database connection, file schema) in multiple jobs.
14. Practice job organization: create sub-jobs or reuse components for a small workflow.
15. Module 2: Advanced Data Integration Techniques
16. Topics Covered: Scalable, collaborative development features in Talend Studio. Building on the basics, this module covers enterprise topics such as version control, remote execution, parallel processing, and job optimization. You’ll learn how to collaborate via Git in Talend Studio, run jobs on remote Talend Servers or cloud engines, and improve performance with parallel execution and caching.
17. Learning Objectives:
18. Connect Talend Studio to a remote Git repository and manage job source code collaboratively.
19. Execute Jobs on a remote JobServer (Talend Remote Engine) for production deployment.
20. Use Talend’s debugging tools and logging to troubleshoot jobs without deep Java coding.
21. Implement parallel execution in a Job using tParallelize and other components.
22. Design Jobs with memorization logic (cache lookup) and change data capture (CDC) from databases.
23. Create and use Joblets (reusable job fragments) to modularize complex jobs.
24. Integrate custom Java code or routines into Talend Jobs.
25. Lab Exercises:
26. Exercise: Enable Git version control in a Talend project and synchronize job changes between team members.
27. Run an existing Job on a remote Talend JobServer; review logs remotely.
28. Modify a job to use tParallelize (parallel multi-threading) and compare performance.
29. Add a Joblet: identify repeated logic (e.g. date calculation) and replace it with a reusable Joblet.
30. Configure a CDC scenario: set up a CDC table and use Talend components to capture inserts/updates from a source table.
31. Module 3: Big Data Integration with Talend
32. Topics Covered: Working with Hadoop, Hive, HDFS, and other big data technologies. Talend’s Big Data components let you interact with large-scale data stores without hand-coding Hadoop scripts. In this module, you’ll learn to configure Talend jobs for big data clusters and process data with Hadoop-native frameworks.
33. Learning Objectives:
34. Create and manage Hadoop/Hive/HBase cluster metadata in Talend Studio.
35. Connect Talend to a Hadoop cluster and configure HDFS and Hive connections.
36. Read from and write data to HDFS (via tHDFS and tHBase components).
37. Query Hive tables from Talend jobs and write results back into HDFS.
38. Use Talend’s Big Data components (tPig, tHadoopOutput, etc.) to process data via Pig Latin and MapReduce.
39. Build Talend Big Data Batch jobs for ETL on large datasets.
40. Lab Exercises:
41. Exercise: Set up a sample Hadoop environment (or simulate with Docker/Hortonworks sandbox) and define a cluster metadata in Talend Studio.
42. Ingest a CSV file into HDFS and create an external Hive table referencing it.
43. Build a job to perform a Pig transformation: e.g. parse web clickstream logs, compute pageviews per user, and store results.
44. Use tHiveInput and tHiveOutput to migrate data between relational and Hadoop systems.
45. Implement a MapReduce job: for example, word-count on a text file stored in HDFS.
46. Module 4: Data Quality with Talend
47. Topics Covered: Data profiling, cleansing, and matching using Talend Data Quality tools. This module teaches how to assess and improve the quality of enterprise data. Talend Data Quality (DQ) features let you analyze data against metrics (completeness, accuracy, conformity) and perform cleansing and deduplication.
48. Learning Objectives:
49. Connect to a database or file source and perform a column analysis to profile values (data length, pattern frequency).
50. Define and apply data quality indicators and thresholds (for validity and completeness) to flag bad data.
51. Use regular expressions to test data patterns within Talend analysis.
52. Create and run table analyses to evaluate statistics on columns or rules (e.g. percentage nulls).
53. Define SQL business rules and set up analyses to detect rule violations in data.
54. Perform matching analysis to identify duplicate records (using key or fuzzy matching).
55. Apply data cleansing: trimming, standardization, and value replacement.
56. Use data privacy components to shuffle or mask sensitive fields.
57. Lab Exercises:
58. Exercise: Run a Data Profiling job on a sample customer database: generate analysis reports for completeness (nulls), uniqueness, and key constraint violations.
59. Create a pattern analysis using regex (e.g. validate email or ZIP code formats). Set thresholds to mark outliers.
60. Implement an advanced matching scenario: find duplicate customer entries using exact and fuzzy logic, then merge or purge duplicates.
61. Build a simple ETL job to cleanse data: e.g. standardize phone numbers, capitalize names, and export only “clean” records.
62. Module 5: Data Governance and Metadata Management
63. Topics Covered: Talend Data Catalog and data stewardship for governing enterprise data assets. This module introduces Talend’s governance tools to create a centralized, trusted data catalog. You’ll learn how to harvest metadata, define business glossaries, and engage business users in data curation.
64. Learning Objectives:
65. Understand the purpose of Talend Data Catalog: a secure central metadata repository that automatically crawls and profiles data sources.
66. Harvest and stitch metadata: import schemas and lineage from databases, files, and Talend jobs.
67. Define a business glossary and semantic mappings to standardize terminology across datasets.
68. Implement data governance workflows: set up Talend Cloud Data Stewardship campaigns for resolving data issues.
69. Use Talend Data Catalog to search and discover trusted data assets, with lineage tracing and stewardship history.
70. Understand data privacy and compliance support (e.g. GDPR) in Talend’s catalog.
71. Lab Exercises:
72. Exercise: Use Talend Data Catalog to crawl a set of sample databases and file systems. Inspect the automatically generated data inventory (columns, lineage, statistics).
73. Create custom business terms (e.g. define what “CustomerID” means) and link them to catalog entries.
74. Simulate a data stewardship scenario: assign a “Data Steward” user to a dataset with issues (e.g. duplicate or inconsistent records), and walk through resolving it via a Stewardship campaign.
75. Generate lineage diagrams for a data pipeline to illustrate end-to-end data flow.
76. Module 6: Talend Cloud Platform
77. Topics Covered: Cloud-based data integration and preparation. Talend Cloud offers a suite of applications for self-service ETL and data governance. This module covers the essentials of Talend Cloud: Pipeline Designer (a low-code web UI for data flows), Data Preparation (interactive cleaning), and Data Inventory (profiling and trust score). You will also learn about deploying jobs with the Talend Management Console and leveraging remote engines for execution.
78. Learning Objectives:
79. Understand the Talend Cloud ecosystem: the Management Console, user/role management, and workspace organization.
80. Use the Pipeline Designer to create and run simple pipelines entirely in the cloud (e.g. copy data between sources).
81. Perform data preparation tasks in Talend Cloud: upload a dataset, clean values (fix formats, fill missing data), and export results.
82. Explore Talend Cloud Data Inventory: profile a cloud dataset and interpret the Trust Score metrics.
83. Create and manage Data Stewardship campaigns in Talend Cloud for resolving data quality issues.
84. Deploy and schedule Talend jobs using the Cloud UI (including using remote engines on cloud instances).
85. Lab Exercises:
86. Exercise: In Talend Cloud Pipeline Designer, build a pipeline that reads from an online data source (e.g. S3 or RDS), transforms data (filter/map), and writes to a target (e.g. Snowflake or Google BigQuery).
87. Use Talend Cloud Data Preparation: take an unstructured dataset, apply transformations (split columns, find/replace), and export a cleansed dataset.
88. Create a simple Stewardship campaign in the Talend Cloud interface: load a small CSV with errors, assign stewards, and resolve duplicates or missing data.
89. Deploy a Talend Data Integration job to the Talend Cloud Remote Engine and execute it on a schedule via the Management Console.
90. Module 7: Capstone Project – Integrating It All
91. Topics Covered: A real-world, project-based exercise that combines all learned skills in an end-to-end scenario. For example, students might simulate an “E-commerce Data Pipeline”: ingest sales and customer data (from both relational DB and Hadoop), integrate and clean the data, and populate a data warehouse or data lake with high-quality, cataloged data.
92. Project Objectives:
93. Design and implement a complete ETL process: extract from multiple sources (databases, CSV, cloud storage), transform (business rules, enrichment), and load into a target system.
94. Apply Data Quality checks and cleansing during the ETL flow.
95. Use Talend Data Catalog to document and govern the final datasets (capturing metadata and lineage).
96. Utilize Talend Cloud tools as appropriate for orchestration, preparation, or stewardship.
97. Deliver the project with proper version control, joblets/modules reuse, and documentation.
98. Capstone Activities:
99. Work in teams to specify requirements and design the data workflow.
100. Hands-on: Build the integrated solution in Talend Studio/Cloud.
101. Review and present the data flow, quality results, and governance artifacts (catalog entries, glossaries) to the class.