Catalog
concept#Data#Analytics#Architecture#Integration

Data Processing

Concept for collecting, transforming and orchestrating raw data into usable information for analytics, integration and operational systems.

Data processing describes collecting, validating, transforming and organizing raw data into usable information.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Message brokers (e.g. Kafka)Data warehouses and data lakesFeature stores and analytics platforms

Principles & goals

Define a single source of truthValidate data quality earlyMake processing semantics explicit (idempotent, exactly-once)
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data inconsistencies due to missing transactional boundaries
  • Privacy breaches if anonymization is insufficient
  • Cost overruns from uncontrolled throughput or storage
  • Plan and version schema evolution
  • Implement end-to-end observability (logs, metrics, traces)
  • Introduce automated quality checks and alerts

I/O & resources

  • Raw data streams or batch files
  • Schemas, mappings and validation rules
  • Infrastructure and operational parameters
  • Cleaned, normalized datasets
  • Metrics, events and audits
  • Persistent stores for analytics and integration

Description

Data processing describes collecting, validating, transforming and organizing raw data into usable information. It includes batch and stream processing, ETL/ELT, enrichment, and data quality and governance checks. The goal is reliable, scalable delivery of consistent data for analytics, system integration and operational workflows, considering privacy, monitoring and cost constraints.

✔

  • Consistent and reproducible data deliveries
  • Improved decision making through high-quality data
  • Scalability of analytics and integration processes

✖

  • Complexity with heterogeneous data schemas
  • Latency vs. consistency trade-offs for real-time needs
  • Increased operational effort for quality and governance

  • Throughput (events/sec)

    Measures number of processed events per time unit.

  • Latency (end-to-end)

    Time from arrival of a data item to availability in the target system.

  • Data quality score

    Aggregate index from completeness, accuracy and freshness.

ETL pipeline for reporting

A batch ETL extracts logs, transforms them and loads aggregated metrics into a data warehouse.

Real-time stream transformation

Stream processors normalize events, compute metrics and feed dashboards with second-level latency.

Feature engineering for models

Process to produce stable features from raw data including lineage and reproducibility.

1

Define requirements and SLAs

2

Catalog and prioritize data sources

3

Design pipeline architecture, test and roll out incrementally

⚠️ Technical debt & bottlenecks

  • Ad-hoc transformation scripts without tests
  • No metadata capture and lineage
  • Hardcoded endpoints and credentials in pipelines
I/O and network throughputSchema migrationsState management in streaming
  • Using batch pipelines for hard real-time requirements
  • Storing personal data without a deletion strategy
  • Uncontrolled replication of large raw datasets
  • Hidden costs from unlimited retention
  • Lack of test data for edge cases
  • Unclear SLAs lead to operational disputes
Data modeling and ETL designStreaming and batch processing techniquesData governance and privacy knowledge
Processing scalability (throughput and latency)Data quality and traceabilityPrivacy and compliance requirements
  • • Existing data formats and legacy sources
  • • Budget and operational resources
  • • Legal requirements for data retention