Data & Integration

ETL Pipeline Development

Build robust ETL pipelines to extract data from various sources, transform it according to business rules, and load it into data warehouses or analytics platforms. Includes scheduling, error handling, data validation, and comprehensive monitoring.

Complexity: Complex 21-34 effort units 5-8 weeks
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Project Milestone & Feature Breakdown

4
Project Milestones
11
Features
34
Total Effort Units
1

Data Extraction Layer

Extract data from multiple sources

8 pts 1-2 weeks 3 Features

Database Connectors

3 pts Medium

Connect to SQL, NoSQL, and data warehouse sources

API Data Extraction

3 pts Medium

Extract data from REST and GraphQL APIs

File Parsers

2 pts Simple

Parse CSV, JSON, XML, and Excel files

Deliverables
  • Data connectors
  • Extraction scripts
  • Connection pooling
2

Data Transformation Logic

Transform and clean extracted data

13 pts 2-3 weeks 3 Features

Data Cleaning

5 pts Complex

Remove duplicates, handle nulls, standardize formats

Business Rules Application

5 pts Complex

Apply domain-specific transformation rules

Data Validation

3 pts Medium

Validate data quality and completeness

Deliverables
  • Transformation scripts
  • Validation rules
  • Data quality reports
3

Data Loading & Storage

Load transformed data into target systems

5 pts 1 week 2 Features

Data Warehouse Loading

3 pts Medium

Bulk load data into Redshift, BigQuery, or Snowflake

Incremental Updates

2 pts Simple

Handle incremental loads and upserts

Deliverables
  • Loading scripts
  • Upsert logic
  • Batch processing
4

Pipeline Orchestration

Schedule and orchestrate ETL jobs

8 pts 1-2 weeks 3 Features

Job Scheduling

3 pts Medium

Configure cron-based or event-driven scheduling

Pipeline Orchestration

3 pts Medium

Define DAGs and dependencies in Airflow or Prefect

Error Recovery

2 pts Simple

Implement retry logic and failure notifications

Deliverables
  • Scheduled jobs
  • DAG definitions
  • Error handling

Technical Stack

Apache Airflow Python Pandas AWS Glue Snowflake/BigQuery PostgreSQL Docker

Key Considerations

Data quality validation at each stage

Handling schema changes in source systems

Performance optimization for large datasets

Idempotency for reliable reruns

Monitoring and alerting for pipeline failures

Success Criteria

Data extracted from all sources successfully

Transformations produce accurate results

Data loaded into warehouse on schedule

Pipeline handles failures with retries

Data quality metrics tracked and reported

Related Use Cases

View All Use Cases
Complex

Database Modernization & Migration

21-34 effort units 5-8 weeks
Complex

Real-Time Data Synchronization

13-21 effort units 3-5 weeks
Medium

Real-Time Analytics Dashboards

13-21 effort units 3-5 weeks

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