Automated ETL Pipelines
Data extraction, transformation, and loading using Dataflow & Cloud Functions.
Manual data processing is slow, error-prone, and cannot scale with growing data volumes. Businesses need automated ETL (Extract, Transform, Load) pipelines to efficiently move and process data for analytics and operations.
We build serverless, auto-scaling data pipelines using Dataflow for large-scale data processing. Workflows are orchestrated and scheduled using Cloud Composer (Apache Airflow). Data is transformed and loaded into BigQuery for analysis, with Cloud Functions handling event-driven triggers and smaller tasks.
Data Extraction
Automated connectors pull data from various sources like APIs, databases, and file storage into a Cloud Storage data lake.
Transformation with Dataflow
Serverless Dataflow jobs clean, enrich, and transform the raw data in-stream, applying business logic and ensuring data quality.
Loading into BigQuery
The processed data is streamed into BigQuery, making it immediately available for real-time analytics and reporting.
Orchestration with Composer
Cloud Composer manages the end-to-end workflow, handling schedules, dependencies, and retries to ensure pipeline reliability.
- Dataflow
- Cloud Composer
- BigQuery
- Cloud Functions
- Reliable and timely data for decision-making.
- Reduced manual effort and operational costs.
- Scalable data processing that handles any volume of data.
Ready to Discuss Automated ETL Pipelines?
Let's explore how our expertise can be tailored to your specific needs. Contact us today for a consultation.