Evolution of Data Pipelines: Cost, Skill, and Usability

The way businesses handle and utilize data has undergone a radical transformation.

This timeline tracks the evolution of data pipelines across five critical eras, highlighting how changes in technology have dramatically impacted cost structures, the technical skills required to both build and use them, and their overall usability.

From the slow, rigid Batch Processing of the 70s to the instant, flexible, and accessible Cloud-Native and Data Oil approaches of today and tomorrow, discover how data movement has shifted from a complex IT challenge to a source of instant, widespread business value.

Batch of Files

Batch Processing

1970s–1990s
  • Pros: Simple to implement, predictable schedules
  • Cons: High latency, limited flexibility, insights delayed until next batch
  • Cost Structure: Low infrastructure cost, high inefficiency over time
  • Skill to Build: Basic scripting (e.g., shell, cron), low technical barrier
  • Skill to Use Output: Moderate, requires manual interpretation of static reports

Reference: Early ETL pipelines relied on overnight batch jobs – EA Journals

  • Pros: Structured data, standardized schema, improved reporting
  • Cons: Complex to maintain, brittle workflows, ETL failures common
  • Cost Structure: Moderate infrastructure cost, high maintenance overhead
  • Skill to Build: Intermediate SQL, data modeling, ETL tools (e.g., Talend, Informatica)
  • Skill to Use Output: Moderate requires understanding of schema and report logic

Reference: Hadoop-era ETL pipelines required heavy pre-processing — LinkedIn

ETL Pipelines

1990s–2000s

Real-Time Streaming

2010s
  • Pros: Low latency, continuous data flow, high availability
  • Cons: Resource intensive, higher cost, requires specialized engineering
  • Cost Structure: High compute and storage cost, especially at scale
  • Skill to Build: Advanced Kafka, Spark, distributed systems, DevOps
  • Skill to Use Output: High requires real-time dashboards and alerting systems

Reference: Kafka and Spark improved efficiency by 20% – EA Journals

  • Pros: Scalability, integration with AI/ML, serverless efficiency
  • Cons: Vendor lock-in, specialized skill sets, bias risks in AI models
  • Cost Structure: Pay-as-you-go pricing, unpredictable at scale
  • Skill to Build: Advanced cloud platforms (AWS, GCP), Python, orchestration tools
  • Skill to Use Output: Moderate to high, requires familiarity with ML outputs and cloud dashboards

Reference: Serverless ELT (AWS Glue, Google Dataflow) dominate pipelines – ResearchGate

Cloud-Native Analytics

2020s

Data Oil

Future
  • Pros: Anticipatory processing, on-demand insights, reproducibility, non-lock-in portability
  • Cons: Currently limited deployment, requires infrastructure adaptation
  • Cost Structure: Low entry cost, scalable with usage; avoids vendor lock-in premiums
  • Skill to Build: Low and simple upload-to-insight workflows
  • Skill to Use Output: Non-technical users can ask questions and receive direct insights

Reference: Data Oil processes continuously and exports/imports across systems – Data Oil documentation