Traditional ETL (Extract, Transform, Load) performed transformations before loading data into the warehouse. Modern ELT (Extract, Load, Transform) leverages the power of cloud data warehouses to transform data after loading, enabling faster ingestion and more flexible transformations. We build hybrid pipelines using Airflow for orchestration, Fivetran/Airbyte for extraction, and dbt for transformations. Our pipelines are idempotent, fault-tolerant, and designed for incremental processing to minimize costs and latency.
Critical for companies integrating data from multiple sources, SaaS platforms syncing customer data, e-commerce businesses consolidating sales channels, financial services requiring regulatory reporting, and any organization moving from manual data processes to automated pipelines.
Design DAGs (Directed Acyclic Graphs) with complex dependencies, branching logic, and conditional execution. Monitor pipeline health with Airflow UI, SLA alerts, and Slack notifications.
Connect 200+ data sources including databases (PostgreSQL, MySQL, MongoDB), SaaS tools (Salesforce, HubSpot, Stripe), and cloud storage (S3, GCS) with pre-built connectors.
Write SQL transformations as code with dbt. Implement data tests, documentation, and version control. Build modular, reusable transformation models with dependencies.
Implement change data capture (CDC), delta loading, and time-based incremental strategies. Process only new or changed records to reduce costs and latency by 90%.
Comprehensive data quality checks using Great Expectations or dbt tests. Validate row counts, column schemas, null checks, uniqueness constraints, and referential integrity.
Robust error handling with automatic retries, exponential backoff, dead letter queues, and detailed error logging. Graceful degradation for partial failures.
Configure cron-based schedules, event-driven triggers, and manual backfill capabilities. Handle timezone complexities and business calendar dependencies.
Real-time monitoring dashboards showing pipeline status, execution times, data volumes, and error rates. Integration with DataDog, Grafana, or custom dashboards.
Automated lineage tracking from source systems through transformations to final tables. Understand impact analysis when schemas change or pipelines fail.
Git-based workflows with pull requests for pipeline changes. Automated testing in development environments before production deployment. Blue-green deployments for zero downtime.
Custom connectors for REST APIs, GraphQL endpoints, and webhooks. Handle pagination, rate limiting, authentication, and incremental sync for APIs without native connectors.
Backfill historical data efficiently with date-partitioned processing. Parallel execution for faster backfills and graceful handling of source system rate limits.
Identify all data sources, assess API capabilities, understand update frequencies, evaluate data volumes, and document authentication requirements.
Design pipeline architecture, choose between ETL/ELT approaches, select orchestration tools, plan for incremental vs full loads, and define data refresh schedules.
Configure Fivetran/Airbyte connectors for standard sources, build custom connectors for APIs, establish secure connections, and validate data extraction.
Write dbt models for data cleaning, standardization, and business logic. Implement data tests, create documentation, and establish model dependencies.
Test pipelines with sample data, validate transformation logic, perform data quality checks, test error scenarios, and benchmark performance.
Deploy to production with monitoring enabled, configure alerting for failures, document runbooks, train team on operations, and establish on-call rotation.
Monitor pipeline performance, optimize slow transformations, adjust schedules based on usage, tune incremental logic, and continuously improve data quality.
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