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concept#Data#Analytics#Architecture#Cloud#Platform

Big Data Processing

Concept for scalable processing of large, heterogeneous datasets to extract actionable insights in batch and streaming scenarios.

Big data processing encompasses techniques and architectures for ingesting, storing, transforming and analyzing massive, heterogeneous datasets to derive actionable insights.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Advanced

Technical context

Message brokers (e.g., Kafka, Kinesis)Object storage/filesystems (e.g., S3, HDFS)Orchestrators and workflow engines (e.g., Airflow)

Principles & goals

Treat data as a product and define clear SLAs.Separate storage, processing and serving layers.Ensure automated tests, observability and reproducible pipelines.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data quality and inconsistencies lead to wrong insights.
  • Cost explosion from uncontrolled cloud scaling.
  • Insufficient governance causes compliance and security issues.
  • Define schema evolution and versioning early.
  • Implement idempotent processing and clear error handling.
  • Measure observability, SLAs and cost metrics from the start.

I/O & resources

  • Raw data from production systems or external sources
  • Metadata, schemas and quality requirements
  • Compute and storage resources (clusters, cloud accounts)
  • Prepared datasets for analytics and models
  • Real-time metrics, alerts and dashboards
  • Archived raw and transformation artifacts with lineage

Description

Big data processing encompasses techniques and architectures for ingesting, storing, transforming and analyzing massive, heterogeneous datasets to derive actionable insights. It covers batch and stream processing, scalable storage, distributed compute and orchestration patterns, and often integrates cloud services, data lakes and governance practices across engineering and analytics teams.

✔

  • Scalable processing of large datasets for deeper insights.
  • Support for both batch and real-time analytics.
  • Better decision basis through integrated data platforms.

✖

  • High effort for operations, cost optimization and governance.
  • Complex data integration and schema management across sources.
  • Latency limits for demanding real-time requirements.

  • Throughput (events/s or GB/s)

    Measures volume of data processed per unit time.

  • End-to-end latency

    Time from event ingress to availability in the target system.

  • Cost per processed unit

    Monetary effort relative to processed data volume.

Real-time analytics at a telecom operator

A provider uses streaming pipelines to detect network issues and trigger automated alerts.

Data lakehouse for financial analytics

A financial firm integrates batch ETL and OLAP queries in a unified lakehouse for risk reporting.

Feature engineering pipelines at an e-commerce company

E-commerce company runs distributed aggregations to create consistent feature sets for recommendations.

1

Assess: analyze requirements, data sources and SLAs.

2

Design: craft architecture for storage, compute and orchestration.

3

Build: implement pipelines, tests and monitoring.

4

Operate: manage cost, security and governance in daily operations.

⚠️ Technical debt & bottlenecks

  • Ad-hoc schemas without versioning and documented migrations.
  • Temporary workarounds instead of scalable partitioning strategy.
  • Lack of automation for pipeline tests and deployments.
Network bandwidth for distributed shufflesI/O performance for massive parquet scansCoordination and orchestration of large pipelines
  • Using large clusters to compensate poorly optimized SQL queries.
  • Temporarily storing sensitive data in public buckets.
  • Neglecting cost forecasting for cloud workloads.
  • Underestimating costs and operational effort when scaling.
  • Missing data quality tests for both offline and streaming paths.
  • Failing to consider privacy requirements (e.g., PII).
Understanding of distributed systems and streaming modelsKnowledge of data modeling and ETL/ELT pipelinesOperational knowledge of scaling, cost optimization and observability
Throughput and latency requirements of business processesData volume, variety and change rate of sourcesSecurity, privacy and compliance requirements
  • • Budget limits for cloud services and storage
  • • Regulatory requirements for data retention
  • • Incompatible source formats and missing metadata