How do I choose between batch and streaming pipelines?

Batch: when hourly/daily freshness is acceptable, simpler to build. Streaming: when you need real-time or near-real-time data. Start with batch unless real-time is truly required.

What happens when a pipeline fails?

Good pipelines have: alerting, retry logic, idempotent processing (safe to rerun), dead letter queues, and backfill capabilities. Design for failure from the start.

How do I monitor data pipeline health?

Track: run success/failure, data volumes, latency, data quality metrics, and resource usage. Alert on anomalies. Use pipeline orchestration tools with built-in monitoring.

Should I build or buy data pipelines?

Buy/use existing tools for standard sources (Fivetran, Airbyte). Build custom for unique sources or complex transformations. Focus engineering effort on business logic, not plumbing.

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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."

Frequently Asked Questions

Related Terms

ETL Data Warehouse Streaming

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