Cloud Desktop Teaching Platforms

This course session provides a hands-on, step-by-step tutorial on using Talend Data Integration to interact with distributed file systems—specifically HDFS (Hadoop Distributed File System) and Amazon S3—within a virtualized big data environment. The session covers configuration of connections, file operations (upload, download, list), context variable usage, error handling, component chaining, and best practices for metadata schema extraction and reuse. Participants are guided through real-world scenarios involving file transfers between S3 and HDFS, with emphasis on proper syntax for context variables, connection management, and job design patterns using pre- and post-job components. The session concludes with a reminder to submit case study evidence and transitions to the next topic: data processing with big data tools.

Topic (Timeline)

1. HDFS File Operations and Context Configuration [00:00:02 - 00:08:55]

Instructor guides participants through accessing a local file system via browser and verifying file upload to a directory named inputs.
Demonstrates configuring conditional logic (if component) to trigger actions based on file download success, using a variable set to 1 to indicate success.
Clarifies correct syntax for error messages: “error al cargar archivo” and “error al descargar archivo”.
Shows how to remove incorrect connections and re-establish proper component links between hdf and rumif components.
Emphasizes that context variables (e.g., server address, directory paths) must not be enclosed in double quotes when referenced in components, as they are dynamic values, not static strings.
Troubleshoots common errors: incorrect IP format (missing colons), misplaced quotes around context variables, and misconfigured directory paths.
Confirms successful execution of jobs and verifies file downloads in local temporary directories.

2. HDFS Cluster Connection Setup and Reuse [00:08:58 - 00:20:57]

Guides creation of a new HDFS cluster connection using Open Source distribution, version 3.0.
Configures connection parameters: replaces localhost with server IP 10.0.3.250, updates port from 8020 to 9000, and sets username to chedub.
Demonstrates verifying connection status via “Check Service” and downloading required drivers.
Creates an HDFS connection named hdfs_axa linked to the cluster.
Shows how to reuse this connection across jobs via repository-based property tie, avoiding redundant configuration.
Highlights importance of defining connections at the project context level to ensure consistency across multiple jobs.

3. Listing Files in HDFS with Iteration and Java Integration [00:20:59 - 00:36:08]

Creates a new job named job_listar_ficheros_con_hdfs to list files in HDFS.
Uses tHDFSList component connected via repository-based connection (hdfs_axa).
Imports project-level context variables (e.g., context.ruta_raiz) into the job to dynamically set the HDFS directory path.
Demonstrates use of iterate flow to process arrays returned by tHDFSList (e.g., list of filenames).
Integrates tJavaRow component to print each filename and directory path using System.out.println() with autocomplete assistance.
Teaches proper use of Talend component variables (e.g., current_filename, current_file_directory) via the online perspective.
Fixes syntax errors: incorrect variable references (e.g., using hyphens instead of underscores), missing semicolons, and incorrect lowercase/uppercase in Java methods (println).
Executes job successfully, returning file names (produccion) and their HDFS paths.

4. Reading Files from HDFS with Schema Extraction [00:36:18 - 00:47:03]

Creates job job_leer_fichero_desde_hdfs to read a CSV file (produccion.csv) stored in HDFS.
Uses tHDFSInput component with static connection to hdfs_axa.
Demonstrates extracting schema from a local copy of the CSV file using tFileInputDelimited:
- Specifies delimiter as comma.
- Enables header detection.
- Exports schema as .xml file for reuse.
Imports the exported schema into tHDFSInput via “Import Schema” button.
Confirms successful data read by executing job and verifying output in console.
Notes that no data processing has occurred yet—only storage and retrieval operations.

5. S3 Integration: Uploading Files with Connection Components [00:47:18 - 01:05:19]

Introduces Amazon S3 integration using tS3Connection, tS3Put, and tS3Close components.
Clarifies that S3 connections cannot be stored in repository like HDFS; must be configured per job.
Uses tPreJob and tPostJob components to encapsulate connection logic for better job readability.
Configures tS3Connection using AWS credentials (access key and secret key) copied from a shared file.
Sets bucket name (noble_prog) and key (filename) for upload.
Executes job and verifies file upload via S3 console.
Troubleshoots execution failures by restarting Talend and rechecking credentials and bucket paths.

6. Downloading from S3 and Uploading to HDFS [01:05:22 - 01:22:32]

Creates job job_descargar_archivo_s3_subirlo_a_hdfs to transfer a file (demográficos.csv) from S3 to HDFS.
Reuses tPreJob and tPostJob from previous job.
Adds tS3Get (download), tHDFSPut (upload), and tFileDelete (cleanup) components.
Configures tS3Get to download file to local temporary directory using context variable context.ruta_raiz without quotes.
Configures tHDFSPut to upload file to HDFS inputs directory using same context variable.
Uses tFileDelete to remove local copy after upload.
Fixes tHDFSPut error by changing “Action on file” from “Create” to “Overwrite” to avoid conflicts on re-execution.
Verifies successful transfer by checking HDFS inputs directory and confirming local file deletion.

7. Administrative Wrap-up and Transition to Data Processing [01:22:38 - 01:29:38]

Requests participants to email case study submissions from the previous day for grading credit.
Confirms receipt of submissions from several participants and notes missing submissions.
Instructs participants to save their virtual machine state via “Save” option in Hyper-V to preserve configuration.
Clarifies that the session focused on storage operations (HDFS/S3 upload/download/list) and not data processing.
Notes limitations of Talend for unstructured data (audio/video) and mentions potential need for custom scripts.
Announces next session will cover data processing with Talend on big data workflows.
Confirms all participants have access to the virtual environment and concludes session.

Appendix

Key Principles

Context Variables: Never wrap context variables (e.g., context.ruta_raiz) in double quotes—they are dynamic references, not literals.
Connection Management: Prefer repository-based connections (property tie) over embedded configurations for reusability and consistency.
Component Flow: Use tPreJob and tPostJob to encapsulate connection setup/teardown for cleaner, more maintainable jobs.
Schema Reuse: Extract schema from sample files using tFileInputDelimited → export as .xml → import into tHDFSInput to avoid manual column definition.
File Operations: Use “Overwrite” instead of “Create” in tHDFSPut to prevent job failures on reruns.

Tools Used

Talend Data Integration (Talend Studio)
HDFS (Hadoop Distributed File System)
Amazon S3
Hyper-V (virtual machine hosting)
tHDFSList, tHDFSInput, tHDFSPut, tS3Connection, tS3Put, tS3Get, tJavaRow, tFileInputDelimited, tFileDelete, tPreJob, tPostJob

Common Pitfalls

Using double quotes around context variables → treats them as literal strings.
Incorrect IP/port format (e.g., missing colon in 10.0.3.250:9000).
Case sensitivity in S3 filenames (Linux-based systems require exact casing).
Forgetting to import project context into job → variables unavailable.
Using “Create” action in tHDFSPut → fails if file exists.
Misconfiguring iterate flow → not connected to array-returning components.

Practice Suggestions

Rebuild the HDFS-to-S3 transfer job from scratch without referencing notes.
Create a job that lists files in HDFS and writes their names to a local CSV.
Experiment with tJavaRow to manipulate file paths (e.g., extract filename from full path).
Try uploading a JSON file to S3 and reading it via tS3Get + tFileInputJSON.
Use tFlowToIterate to process multiple files in a loop.

Big Data - jhon-megf-20241010-022829

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Summary

Overview