OLAP
Definition
Online Analytical Processing - database systems optimized for complex queries, enabling efficient data analysis and reporting for business intelligence
Use Cases
- The Home Depot: Enterprise analytics to understand customer behavior and improve operations across stores and digital channels. — Adopted Google BigQuery as a cloud data warehouse to centralize large datasets and run complex analytical queries for reporting and analysis. (Improved ability to analyze large datasets and support data-driven decision-making with scalable analytics.)
- Spotify: Analytics on user engagement and listening behavior to support reporting and insights. — Uses Google BigQuery for large-scale analytics workloads, enabling teams to run complex SQL queries over very large datasets. (Supports scalable analytics and faster insight generation for business and product analysis.)
- Capital One: Enterprise data analytics to support business intelligence and decision-making. — Has used Amazon Redshift as part of its AWS-based data platform for analytical querying and reporting workloads. (Enables scalable analytics on cloud infrastructure and supports broad BI use cases.)
Provider Equivalents
- AWS: Amazon Redshift
- Azure: Azure Synapse Analytics
- GCP: BigQuery
- OCI: Oracle Autonomous Data Warehouse
Frequently Asked Questions
- What's the difference between OLAP and OLTP?
- OLTP (Online Transaction Processing) handles day-to-day transactions like orders, payments, and account updates—many small reads/writes with strict consistency. OLAP (Online Analytical Processing) is for analysis—fewer but much heavier queries that scan lots of data and compute aggregates (e.g., revenue by region by month). In practice, OLTP powers the app; OLAP powers reporting, dashboards, and analytics.
- When should I use OLAP?
- Use OLAP when you need to analyze historical or large-scale data with complex queries—dashboards, KPI reporting, trend analysis, cohort analysis, forecasting inputs, and ad-hoc exploration. It’s a good fit when queries involve joins and aggregations across many rows (millions to billions) and you want fast results for analysts without impacting your production transaction database.
- How much does OLAP cost?
- Cost depends on (1) data storage size, (2) compute used to run queries, (3) concurrency (how many users run queries at once), (4) data ingestion/ETL and data transfer, and (5) features like backups, replication, and governance. Some platforms charge per query/bytes scanned (common in serverless warehouses), while others charge for provisioned or reserved compute (common in cluster-based MPP systems). You can control cost with partitioning, clustering/sort keys, materialized views, caching, workload management, and scheduling heavy jobs off-peak.
Category: data
Difficulty: advanced
Related Terms
See Also