Big Data
Big Data refers to datasets so large or complex that traditional data processing tools can't handle them efficiently. Characterised by the "3 Vs" — Volume (amount), Velocity (speed), and Variety (type…
Last updated:
Definition
Big Data refers to datasets so large or complex that traditional data processing tools can't handle them efficiently. Characterised by the "3 Vs" — Volume (amount), Velocity (speed), and Variety (types) — big data requires specialised tools and techniques. Indian companies generate massive data from mobile users, digital payments, and IoT devices.
Key Points
- 3 Vs: Volume (terabytes+), Velocity (real-time streams), Variety (structured + unstructured)
- Tools: Hadoop, Spark, Kafka, data warehouses (BigQuery, Redshift, Snowflake)
- Indian data sources: UPI transactions, Aadhaar, telecom, e-commerce
- Enables insights impossible with traditional databases
Related Terms
Frequently Asked Questions
There's no exact threshold. Practically, when your data can't be processed efficiently on a single machine with traditional tools (Excel, SQL on standard databases), you're in big data territory. This typically starts at tens of millions of rows or when you need real-time processing of continuous data streams.
Essential: Apache Spark (processing), a cloud data warehouse (BigQuery or Redshift), and SQL. Important: Apache Kafka (streaming), Airflow (orchestration). For analytics: dbt for data transformation. Most Indian companies don't need Hadoop anymore — cloud-based tools like Spark on Databricks or BigQuery handle most needs more efficiently.
Need Help With Big Data?
Sparks AI can help you leverage big data for your business. Let's talk.