Data Pipeline Development Malta

ETL transforms data before loading it into the destination, typically used when the target system has limited processing power. ELT loads raw data first and transforms it within the destination, leveraging modern cloud data warehouse compute for transformation. We increasingly recommend ELT with tools like dbt for flexibility and auditability, but the right choice depends on your specific architecture.

Every pipeline includes automated retry logic with exponential backoff, dead-letter queues for unprocessable records, and idempotent design that allows safe re-execution. When retries are exhausted, automated alerts notify your team with diagnostic information. Failed records are quarantined without blocking the rest of the pipeline from processing.

Yes, we have extensive experience integrating with legacy systems through database connections, file-based transfers, screen scraping, and custom adapters. Change data capture from legacy databases enables near-real-time integration without modifying the source system. We work with whatever your systems provide.

Simple pipelines connecting one source to one destination take 1-2 weeks. Complex multi-source pipelines with business logic, quality checks, and error handling typically take 3-6 weeks. Enterprise-scale pipeline platforms with dozens of integrations are delivered iteratively over 2-4 months.

Apache Airflow is the most mature option with the largest community and widest adoption. Dagster offers a more modern developer experience with better testing and data asset management. Prefect provides a simpler model for straightforward workflows. We recommend based on your team's skills, existing infrastructure, and workflow complexity.

We embed quality checks at every pipeline stage using Great Expectations, dbt tests, and custom validation rules. Checks cover completeness, uniqueness, referential integrity, range validation, and business rule compliance. Quality failures trigger alerts and can halt downstream processing to prevent bad data propagation.

Yes, we design pipelines with schema evolution handling that detects and adapts to source schema changes. New columns are added automatically, removed columns are handled gracefully, and type changes are caught and flagged. Schema registry integration provides advance warning of planned changes.

Pipeline costs depend on data volume, processing frequency, and infrastructure choices. We optimise for cost-efficiency using serverless compute for variable workloads, spot instances for batch processing, and efficient transformation patterns that minimise compute usage. Most clients find pipeline automation saves far more in manual labour than it costs in infrastructure.