OLTP vs OLAP: Key Differences and When to Use Each
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Architecture / Concept Overview: OLTP vs OLAP: Key Differences and When to Use Each
OLTP and OLAP serve fundamentally different access patterns. OLTP optimizes for low-latency row-level operations, while OLAP optimizes for high-throughput columnar scans.
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A[User Action] -->|Single Row Write| B[OLTP System / Lakebase]
B -->|Sync| C[Delta Lake]
C -->|Columnar Scan| D[OLAP System / Databricks SQL]
D -->|Dashboard| E[BI Tools]
B -->|Low Latency Response| F[Application UI]
A:::source
B:::processing
C:::storage
D:::serving
E:::serving
F:::source
*OLTP handles the transactional write path while OLAP handles the analytical read path, connected through Delta Lake sync.*
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graph TD
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A[Data Access Patterns] --> B[OLTP Characteristics]
A --> C[OLAP Characteristics]
B --> D[Row-oriented storage]
B --> E[Sub-millisecond latency]
B --> F[High concurrency writes]
C --> G[Columnar storage]
C --> H[Seconds-to-minutes latency]
C --> I[Full-table aggregations]
A:::governance
B:::processing
C:::serving
D:::source
E:::source
F:::source
G:::storage
H:::storage
I:::storage
*OLTP and OLAP differ across storage layout, latency targets, and query patterns.*
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A[Lakebase OLTP] -->|Automatic Sync| B[Delta Lake]
B -->|Query Engine| C[Databricks SQL OLAP]
C -->|ML Features| D[Feature Store]
D -->|Serve Predictions| A
A:::processing
B:::storage
C:::serving
D:::ingestion
*On Databricks, Lakebase and Databricks SQL form a unified OLTP+OLAP architecture with Delta Lake as the shared storage layer.*
Key Terms
Prerequisites and Setup
- A Databricks workspace with both Lakebase and Databricks SQL enabled
- Understanding of relational database fundamentals (tables, indexes, joins)
- Familiarity with SQL for both transactional and analytical queries
Step-by-Step Implementation
Configuration Reference
| Dimension | OLTP (Lakebase) | OLAP (Databricks SQL) |
|---|---|---|
| Storage layout | Row-oriented | Columnar (Parquet/Delta) |
| Typical latency | Sub-millisecond to low milliseconds | Seconds to minutes |
| Query pattern | Point lookups, single-row mutations | Full-table scans, aggregations |
| Concurrency | Thousands of concurrent transactions | Tens to hundreds of concurrent queries |
| Schema design | Normalized (3NF) | Denormalized / star schema |
| Indexing | B-tree, hash indexes on keys | Z-order, data skipping, bloom filters |
| Transaction model | Full ACID per statement/transaction | ACID per Delta commit |