Catalog
concept#Data#Integration#Architecture#Platform

Data Ingestion

Concept for structured capture and transfer of data from sources to target systems; includes batch and streaming mechanisms.

Data ingestion describes the process of collecting, transporting and loading data from diverse sources into target systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Apache KafkaApache NiFiCloud object storage (e.g., S3)

Principles & goals

Avoid monoliths: separate ingestion, processing and storage.Define explicit SLAs for latency and throughput.Isolate failures via dead-letter queues and retries.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Data loss with insufficient persistence or checkpoints.
  • Insufficient validation leads to garbage-in-garbage-out.
  • Cost overruns due to mis-sized infrastructure.
  • Use idempotent producers and unique keys for repeatability.
  • Clearly separate streaming and batch paths and document differences.
  • Implement observability: measure latency, throughput and error rates.

I/O & resources

  • Source data (APIs, logs, files, streams)
  • Metadata and schema definitions
  • Authorization and connection details
  • Persistent records in target systems
  • Monitoring and audit logs
  • Alerts on errors and thresholds

Description

Data ingestion describes the process of collecting, transporting and loading data from diverse sources into target systems. It encompasses batch and streaming approaches, schema handling, transformations and validation. Latency, throughput, consistency and cost drive architectural and operational trade-offs. Effective ingestion balances availability, freshness and operational complexity.

✔

  • Faster availability of relevant data for analytics and ML.
  • Standardized pipelines reduce integration effort.
  • Scalability for growing data volumes through appropriate architecture.

✖

  • Complexity with heterogeneous data sources and formats.
  • Operational costs can increase at high throughputs.
  • Schema evolution requires coordinated governance.

  • Throughput (events/sec)

    Measure of processed events per time unit.

  • End-to-end latency

    Time from creation to availability in target system.

  • Error rate / DLQ volume

    Share of records routed to error paths.

Streaming ingestion with Apache Kafka

Event sources publish messages to Kafka topics; Connect and stream-processing components route data to analytics or storage systems.

Batch ETL into a data warehouse

Nightly extraction from production systems, transformation and loading of structured tables into a data warehouse for reporting and BI.

Edge-to-cloud ingestion for IoT

Edge gateways aggregate sensor data, filter locally and send aggregated data to the cloud for processing and long-term archive.

1

Define requirements and SLAs (latency, throughput, quality).

2

Analyze sources, define data models and validation rules.

3

Implement ingestion path, configure monitoring and run tests.

⚠️ Technical debt & bottlenecks

  • Ad-hoc ingestion scripts without tests and monitoring.
  • No central schema repository or governance process.
  • Tight coupling between producers and target schemas.
Network bandwidthTransformation bottlenecksTarget storage I/O
  • Expecting real-time analytics while only nightly batch processing exists.
  • Storing all raw data unfiltered and cleaning later (leads to cost and complexity).
  • Duplicating sources into multiple targets without central control.
  • Underestimating costs for long-term storage of large volumes.
  • Ignoring schema evolution leads to runtime errors.
  • Missing backpressure mechanisms for streaming sources.
Knowledge of distributed systems and messagingExperience with data formats and schema designOperational skills for observability and error handling
Throughput requirementsLatency / freshness goalsData quality and schema governance
  • • Heterogeneous source formats and protocols
  • • Compliance and data protection requirements
  • • Budget and operational resources