A data pipeline in IT describes the end-to-end process by which data is collected, cleaned, transformed, analyzed, and presented for decision-making.
For a Business Analyst (BA), the pipeline doesn’t just mean the technical ETL (Extract–Transform–Load) steps, but also includes business understanding, data validation, reporting, and insight delivery.
Below is a comprehensive list of 100 examples of data pipelines (grouped by category) that a Business Analyst might encounter, manage, or design in an IT or business environment 👇
Pulling sales data from SAP into a data warehouse
Importing customer records from Salesforce CRM
Extracting web analytics data from Google Analytics
Collecting social media mentions via APIs (Twitter, LinkedIn)
Downloading financial data from Oracle ERP
Fetching marketing campaign data from HubSpot
Importing HR data from Workday
Extracting email campaign performance from Mailchimp
Loading service tickets from Jira into a dashboard
Pulling IoT sensor data from Azure IoT Hub
Removing duplicate customer IDs
Converting dates into consistent formats (e.g., ISO)
Handling missing revenue fields in Excel imports
Normalizing supplier names (IBM vs I.B.M.)
Creating calculated columns (profit = revenue - cost)
Joining orders and customer tables
Splitting address fields into street, city, zip
Converting currency data to USD for global reports
Masking PII (Personally Identifiable Information)
Aggregating transaction data to monthly summaries
SQL-based ETL from SQL Server to Snowflake
Python-based ETL with Pandas
Airflow orchestrated pipeline for weekly data refresh
Talend data pipeline for product catalogs
Informatica data pipeline for finance data
AWS Glue ETL job for sales forecasting data
Azure Data Factory pipeline for HR analytics
Pentaho ETL process for operations data
DataStage job for manufacturing KPIs
Matillion pipeline connecting Salesforce and Redshift
Loading data into Google BigQuery
Creating star schema in Snowflake
Storing cleaned data in PostgreSQL warehouse
Setting up data marts by department (HR, Sales, Finance)
Using data lakes (AWS S3) for raw unstructured data
Incremental loading into Azure Synapse
Partitioning tables by region or date
Creating historical tables for time-series analysis
Archiving old transaction data for compliance
Automating schema updates when source changes
Power BI dashboard refresh using SQL source
Tableau scheduled extracts from data warehouse
Excel Power Query pulling live API data
Looker dashboard using BigQuery datasets
Google Data Studio visualization for marketing KPIs
Business Objects report pipeline for finance
SAP Analytics Cloud data refresh
Python dashboard using Plotly/Dash
R Shiny dashboard for survey results
Automated daily email reports for executives
Cleaning training data for churn prediction
Feeding customer data to ML model for scoring
Tracking model accuracy over time
Using Python scripts to forecast sales trends
Integrating ML model outputs into dashboards
Creating feedback loops for retraining models
Combining structured (sales) + unstructured (reviews) data
Real-time fraud detection data stream
Product recommendation model pipeline
Time-series forecasting for inventory management
Retail sales and returns pipeline
E-commerce checkout behavior pipeline
Marketing attribution analysis pipeline
Financial reconciliation between systems
Customer lifetime value analysis pipeline
Supply chain logistics tracking pipeline
Employee attrition prediction pipeline
Insurance claims validation pipeline
Healthcare patient outcome analytics
Telecom network performance dashboard pipeline
API integration between ERP and CRM
Real-time event streaming via Kafka
Zapier automation from Google Sheets to Slack
Power Automate flow for updating SharePoint lists
Robotic Process Automation (RPA) data transfer
SFTP batch upload to partner system nightly
Webhook-based notification data collection
Data ingestion via REST API endpoints
Scheduled data sync between Excel and database
Syncing master data across multiple platforms
Automated validation checks (null, range, logic)
Data lineage tracking for audit trails
Metadata management via Collibra or Alation
Compliance data pipeline for GDPR
Role-based access control for sensitive data
Duplicate data detection alerts
KPI deviation alerts using threshold logic
Data catalog pipeline documenting sources
Audit log aggregation for regulatory reporting
Continuous data profiling pipeline
Real-time website visitor tracking
Live transaction monitoring pipeline
IoT-based temperature alerting system
Clickstream analytics using Kafka + Spark
Stock market data stream for dashboards
Payment gateway transaction tracking
Chatbot conversation analytics
Fleet tracking data pipeline
Energy consumption monitoring
Real-time customer support ticket updates
A Business Analyst’s role in these pipelines is typically:
Mapping business requirements to data flows
Working with data engineers to define transformations
Validating data accuracy and KPI definitions
Building dashboards or reports
Presenting actionable insights to stakeholders