Data Pipeline
An automated sequence of data processing steps that moves and transforms data from sources to destinations. Essential for keeping AI systems and analytics fed with fresh data.
In-Depth Explanation
A data pipeline automates the flow of data from source systems through processing steps to destination systems. Pipelines ensure data moves reliably and consistently.
Pipeline components:
- Sources: Databases, APIs, files, streams
- Ingestion: Extracting data from sources
- Processing: Transformation, validation, enrichment
- Storage: Landing in destination systems
- Orchestration: Scheduling and dependency management
Pipeline patterns:
- Batch: Process data in scheduled batches
- Streaming: Process data in real-time
- Micro-batch: Small frequent batches
- Lambda: Batch + streaming combined
Pipeline tools:
- Orchestration: Airflow, Dagster, Prefect
- Streaming: Kafka, Flink, Spark Streaming
- ETL: Fivetran, Airbyte, dbt
- Cloud: AWS Glue, Azure Data Factory
Business Context
Reliable data pipelines are critical infrastructure. AI models and dashboards are only as good as the data feeding them.
How Clever Ops Uses This
We build robust data pipelines for Australian businesses, ensuring AI systems and analytics have reliable, timely data.
Example Use Case
"Hourly pipeline extracting sales data from Shopify, enriching with customer segments, and loading to the warehouse for real-time dashboards."