IBM DataStage

Connect to Mainframe via FTP/SSH stage.

Import COBOL Copybooks for schema definition.

Use the Unpack stage to handle packed decimals.

Convert to Parquet format.

Write to S3 using S3 Connector.

Set up CDC (Change Data Capture) on Oracle DB.

Stream changes through a Continuous DataStage job.

Apply business logic for stock levels.

Push updates to a Kafka cluster.

Consume Kafka events in the e-commerce app.

Read from Production SQL Database.

Apply Data Masking stages for emails and names.

Use SHA-256 Hashing for ID fields.

Validate masking against compliance rules.

Write anonymized data to Snowflake.

Ingest data from 3 disparate sources.

Use the Lookup stage to find common keys.

Apply Fuzzy Matching to identify duplicate records.

Merge attributes into a Golden Record.

Load into a Master Data Management (MDM) hub.

Pull transactional logs from various branches.

Apply standardized currency conversion logic.

Maintain 'Data Lineage' at the column level.

Generate aggregate reports.

Store output with immutable timestamps.

Ingest MQTT stream data via custom stage.

Filter out noise and outliers.

Aggregate by minute/hour/day windows.

Calculate moving averages.

Load results into a time-series database.

Identify new records using timestamps.

Compare source vs. target schemas.

Automate schema evolution if source columns change.

Bulk load using FastLoad utilities.

Trigger post-load validation scripts.