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concept#Data#Platform#Analytics#Architecture

Big Data Framework

Conceptual framework for the architecture and organisation of processing large, heterogeneous datasets.

A Big Data framework is a conceptual blueprint for processing, storing, and analyzing large, heterogeneous datasets.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Message broker (e.g., Apache Kafka)Distributed processing (e.g., Apache Spark, Flink)Object storage / data lake (e.g., S3-compatible)

Principles & goals

Separation of storage and compute for independent scalingClear data ownership and governance (ownership, lineage)Design for fault tolerance and idempotent processing
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data silos when governance is missing
  • Inaccurate analytics from poor data quality
  • Operational risks due to insufficient monitoring
  • Versioning schemas and transformations
  • Automated testing and replayable pipelines
  • Secure access controls and encrypted storage

I/O & resources

  • Source systems (APIs, databases, files)
  • Data schema and metadata
  • Operational and scaling requirements
  • Prepared datasets for analytics
  • Real-time metrics and dashboards
  • Audited pipelines and data lineage

Description

A Big Data framework is a conceptual blueprint for processing, storing, and analyzing large, heterogeneous datasets. It defines architectural principles, communication patterns, and integration requirements for scalable data pipelines and batch/streaming workloads. Trade-offs between latency, cost, and consistency are central considerations.

✔

  • Scalable processing of large data volumes
  • Improved access to raw data and self-service analytics
  • Consistent architectural principles for diverse workloads

✖

  • High operational overhead and required specialised skills
  • Costs for storage and compute can grow
  • Complexity in consistency and data integration

  • Throughput (events/s)

    Measure of processed events per second to assess capacity.

  • End-to-end latency

    Time from event ingress to final output/result delivery.

  • Data quality rate

    Share of records that pass validation rules.

Hadoop-based data lake

Batch-oriented data lake using distributed HDFS, YARN for resource management, and MapReduce/Spark for processing.

Streaming platform with Apache Kafka

Event-driven architecture using Kafka for ingestion and stream processing with Flink/Spark Structured Streaming.

Cloud-native data platform

Combination of object storage, serverless processing pipelines, and orchestrated analytics services.

1

Requirements analysis and architecture design

2

Proof-of-concept for core components (ingestion, storage, processing)

3

Incremental rollout to production with monitoring and governance

⚠️ Technical debt & bottlenecks

  • Non-refactored ETL jobs with hard-coded paths
  • Insufficient modularization of transformation logic
  • Missing automation for scaling and recovery processes
Ingestion bottleneckNetwork and throughput limitStorage and I/O bottleneck
  • Storing all raw data without quality checks leads to unusable analytics
  • Scaling only storage, not processing components
  • Ignoring cost optimization for long-running big data jobs
  • Underestimating network and I/O needs
  • Missing schema registry for heterogeneous sources
  • Unconsidered data retention and deletion requirements
Distributed systems and cluster operationsData modelling and ETL/ELT practicesMonitoring, observability and performance tuning
Throughput and latency requirementsData quality and governanceScalability and cost optimization
  • • Existing privacy and compliance requirements
  • • Limited infrastructure budgets
  • • Legacy systems with limited integration