2020-02-12 • 8 min read
What big data really means—from volume, velocity, variety to data lakes, ETL, and analytics—plus tools that turn raw events into decision-ready insights.
Overview
Big Data describes datasets whose size or complexity exceed the limits of traditional databases. The goal is not just to store data, but to transform it into insights quickly and reliably.
The 3Vs (and beyond)
- Volume: Terabytes to petabytes of logs, clicks, and sensor data.
- Velocity: Continuous arrival that requires streaming or micro-batch.
- Variety: Structured tables, semi-structured JSON, unstructured text and images.
Some add Veracity (quality) and Value (business impact).
Core Architecture
- Data lake: Low-cost object storage for raw and curated data.
- Warehouse: Columnar, SQL-friendly engine for analytics and BI.
- Pipelines: Ingest, validate, transform (ETL/ELT) and serve to downstream tools.
- Batch vs streaming: Batch for daily reports; streaming for near-real-time metrics and alerts.
Tooling
- Processing: Apache Spark, Flink; serverless engines like BigQuery or Snowflake.
- Messaging: Kafka or cloud pub/sub for event streams.
- Orchestration: Airflow, Dagster; dbt for SQL transformations.
- Metadata and governance: Data catalogs, lineage, PII tagging, access policies.
Designing Reliable Pipelines
1. Model data contracts between producers and consumers.
2. Add schema validation and quality checks (nulls, outliers, referential integrity).
3. Partition and cluster tables for performance.
4. Version datasets and transformations; make runs reproducible.
5. Expose metrics and SLAs; alert on late or failed loads.
Analytics to Action
Join product, marketing, and finance data to build funnels, cohorts, and LTV models. Deliver dashboards for operators and lightweight datasets for data scientists.
Pitfalls
Hoarding data without a use case, ignoring governance, and underestimating data cleaning. Start with the questions the business needs answered, then collect the data required.
Summary
Big Data succeeds when pipelines are trustworthy, documentation is clear, and teams focus on measurable decisions rather than raw storage.
Big Data describes datasets whose size or complexity exceed the limits of traditional databases. The goal is not just to store data, but to transform it into insights quickly and reliably.
The 3Vs (and beyond)
- Volume: Terabytes to petabytes of logs, clicks, and sensor data.
- Velocity: Continuous arrival that requires streaming or micro-batch.
- Variety: Structured tables, semi-structured JSON, unstructured text and images.
Some add Veracity (quality) and Value (business impact).
Core Architecture
- Data lake: Low-cost object storage for raw and curated data.
- Warehouse: Columnar, SQL-friendly engine for analytics and BI.
- Pipelines: Ingest, validate, transform (ETL/ELT) and serve to downstream tools.
- Batch vs streaming: Batch for daily reports; streaming for near-real-time metrics and alerts.
Tooling
- Processing: Apache Spark, Flink; serverless engines like BigQuery or Snowflake.
- Messaging: Kafka or cloud pub/sub for event streams.
- Orchestration: Airflow, Dagster; dbt for SQL transformations.
- Metadata and governance: Data catalogs, lineage, PII tagging, access policies.
Designing Reliable Pipelines
1. Model data contracts between producers and consumers.
2. Add schema validation and quality checks (nulls, outliers, referential integrity).
3. Partition and cluster tables for performance.
4. Version datasets and transformations; make runs reproducible.
5. Expose metrics and SLAs; alert on late or failed loads.
Analytics to Action
Join product, marketing, and finance data to build funnels, cohorts, and LTV models. Deliver dashboards for operators and lightweight datasets for data scientists.
Pitfalls
Hoarding data without a use case, ignoring governance, and underestimating data cleaning. Start with the questions the business needs answered, then collect the data required.
Summary
Big Data succeeds when pipelines are trustworthy, documentation is clear, and teams focus on measurable decisions rather than raw storage.