OLTP vs OLAP

Key Takeaways

• OLTP handles operational work such as purchases and inventory updates; OLAP analyzes data across many transactions.
• The useful distinction is the workload: selective reads and small writes versus scans, joins, and aggregations.
• ACID transactions are not exclusive to OLTP, and OLAP is not synonymous with batch processing.
• Separate databases commonly complement one another, while hybrid transactional/analytical processing (HTAP) supports both workloads within one platform.

OLTP vs OLAP is a comparison of workload patterns: online transaction processing supports day-to-day operations, while online analytical processing supports analysis across larger datasets, often including historical data. A checkout updates one customer's order and inventory; a sales report aggregates purchases across products, regions, and months.

These workloads commonly work together, as AWS's OLTP and OLAP comparison explains. They're not mutually exclusive database capabilities, and neither label alone tells you the system's consistency guarantees.

What Is OLTP?

OLTP stands for online transaction processing. It supports operational requests such as placing an order, changing an address, or checking an account balance. Typical requests access a small, identifiable set of records, with many requests running concurrently.

A transaction is a logical unit of work that can contain multiple database operations, including reads and writes. It's not simply another word for an individual query or update.

Common OLTP designs use normalized tables, which reduce duplicated data, and indexes for selective lookups. These are common patterns, not requirements; Microsoft's OLTP architecture guide describes their purpose and trade-offs.

ACID Does Not Mean Transactions Run One at a Time

Operational transactions commonly depend on ACID properties:

• Atomicity: the transaction's changes commit together or are rolled back.
• Consistency: correct transaction logic and database constraints preserve rules such as nonnegative inventory.
• Isolation: the isolation level determines which effects of concurrent transactions are visible and which anomalies are prevented.
• Durability: committed changes survive failures according to the database's configured durability guarantees.

PostgreSQL's transaction tutorial illustrates atomic changes across multiple operations. Concurrent transactions can overlap; even serializable isolation describes an outcome equivalent to serial execution, not a requirement to execute everything sequentially.

A query also doesn't necessarily see the latest committed value. Its view depends on transaction boundaries, isolation level, and whether it reads a lagging replica. Check the actual guarantees, starting with the database's transaction isolation documentation.

OLTP Example: Preventing Oversold Inventory

Suppose two customers try to buy the last pair of shoes. Reading the stock count and later writing a new count without concurrency protection can let both checkouts succeed. Instead, make the stock check and decrement one conditional update, and record the order in the same transaction.

This example runs once in an empty PostgreSQL schema. It models only the database portion of a single-item purchase:

CREATE TABLE inventory (
    sku text PRIMARY KEY,
    available integer NOT NULL CHECK (available >= 0)
);

CREATE TABLE orders (
    order_id text PRIMARY KEY,
    sku text NOT NULL REFERENCES inventory (sku),
    quantity integer NOT NULL CHECK (quantity > 0)
);

INSERT INTO inventory (sku, available)
VALUES ('SHOE-42', 1);

BEGIN ISOLATION LEVEL READ COMMITTED;

WITH reserved AS (
    UPDATE inventory
    SET available = available - 1
    WHERE sku = 'SHOE-42'
      AND available >= 1
    RETURNING sku
)
INSERT INTO orders (order_id, sku, quantity)
SELECT 'order-1001', sku, 1
FROM reserved
RETURNING order_id;

COMMIT;

The data-modifying common table expression, named reserved, passes the updated SKU to the order insert through RETURNING. If no inventory row qualifies, no order is inserted. If the insert fails, the inventory change cannot commit on its own.

At PostgreSQL's Read Committed isolation level, a competing update waits when necessary and rechecks its condition against the updated row. With one item available, only one competing purchase can decrement it successfully. Stricter isolation levels can instead produce a serialization failure that requires retrying the whole transaction.

What Is OLAP?

OLAP stands for online analytical processing. It answers questions across many records: which products are growing, how revenue varies by region, or what changed compared with last year. Analytical queries can be interactive or near-real-time; they don't have to be slow or batch-only.

Many analytical databases use columnar storage, which groups values by column rather than storing every field of a row together. A query calculating revenue can read the relevant columns without loading unrelated fields. Amazon Redshift's columnar storage documentation explains why this suits broad analytical reads.

A common analytical model is a star schema: a fact table stores events and numeric measures, while dimension tables describe products, customers, or dates. Microsoft's star schema guide explains how these tables support filtering and grouping. For file-based analytical storage, see our introduction to Apache Parquet.

ACID isn't an OLTP-only feature. For example, BigQuery supports multi-statement ACID transactions with snapshot isolation. Transaction guarantees and analytical query optimization answer different design questions.

OLAP Example: Analyzing Retail Sales

A warehouse combining checkout and ERP sales data is an OLAP example. The ERP itself supports operational processes such as sales, procurement, and finance, as SAP's ERP overview describes; calling ERP software a classic OLAP system is misleading.

The following PostgreSQL-dialect fragment summarizes sales by product and month. It assumes a warehouse table with one row per completed order line:

-- Fragment: fact_sales is an existing warehouse table.
-- sold_at: timestamp without time zone, stored in UTC
-- product_id: text; quantity: integer
-- net_amount: numeric, in one reporting currency

SELECT
    date_trunc('month', sold_at) AS sale_month,
    product_id,
    SUM(quantity) AS units_sold,
    SUM(net_amount) AS revenue
FROM fact_sales
WHERE sold_at >= TIMESTAMP '2026-01-01 00:00:00'
  AND sold_at < TIMESTAMP '2026-10-01 00:00:00'
GROUP BY date_trunc('month', sold_at), product_id
ORDER BY sale_month, revenue DESC;

PostgreSQL's date_trunc function creates the monthly grouping key. Unlike the purchase, this query reads and aggregates a date range rather than updating a particular order. The results can inform inventory forecasting and marketing campaigns; define currency, returns, discounts, and reporting time zones before treating revenue as a business metric.

OLTP vs OLAP Workloads Compared

These are typical design priorities, not hard boundaries. Dataset size or a single query's duration isn't enough to classify a workload.

A read-heavy customer application can still be OLTP. A dashboard refreshing frequently can still be OLAP. The access pattern and business purpose matter more than whether a request is a read or whether its data is recent.

How OLTP Data Reaches an OLAP System

A common architecture separates operational storage from analytical storage so each can be tuned and scaled for its workload:

Application --transactions--> OLTP database
                                    |
                         CDC / replication / batch
                                    |
                 [freshness boundary: pipeline lag]
                                    |
                         Load and transform
                                    |
                         Analytical warehouse
                                    |
                           Reports and analysis

• Batch ingestion copies data on a schedule.
• Change data capture (CDC) captures ongoing changes; log-based CDC reads database change logs.
• Replication maintains another copy, but copying tables alone doesn't create a business-ready analytical model.
• Transformations clean and combine data, standardize currencies, and build fact and dimension tables.

CDC does not inherently mean zero lag. AWS DMS's ongoing replication documentation explicitly distinguishes CDC from guaranteed real-time replication. Measure freshness through to query-ready data, not just arrival at the ingestion service. Our guide to change data capture covers the underlying pattern.

Define how the pipeline handles deletes, schema changes, replayed events, and related updates across tables. A warehouse can be transactionally consistent internally while still representing an older state of the source.

Can One Database Handle Both? HTAP and Mixed Workloads

Yes. A transactional database can run analytical SQL, and hybrid transactional/analytical processing (HTAP) platforms are designed to serve both workload types. For example, TiDB combines row-based TiKV storage and columnar TiFlash storage within one platform.

Combining workloads doesn't eliminate contention. Large scans and aggregations can compete with checkout traffic for CPU, memory, and storage bandwidth. Evaluate resource isolation, analytical freshness, and transaction latency under concurrent load—not separate benchmark runs.

Keep modest reporting on the operational database when it meets your targets. Consider separate analytical storage when reporting needs broader history, data from multiple sources, or independent resource allocation. The same decision applies in the cloud: choose workload-appropriate managed services, budget for ingestion, storage, compute, and data transfer, and verify backup and recovery requirements.

Common Mistakes

• Checking stock only in application code. Enforce the purchase condition in the database transaction.
• Assuming every read sees the newest value. Account for snapshots, isolation, and replication lag.
• Calling every analytical query OLAP infrastructure. Writing GROUP BY doesn't change the database's storage or resource isolation.
• Treating fresh data as correct data. Validate transformations and business definitions as well as pipeline latency.

If SQL terminology is unfamiliar, our overview of SQL views, functions, triggers, and stored procedures provides related background.

Conclusion

OLTP runs the business; OLAP helps you understand it. They commonly complement one another, with analytics using data produced by operational transactions. Choose the architecture around access patterns, correctness, freshness, and resource isolation—not a rigid split between transactional and analytical products.

FAQ

What is the difference between OLTP and OLAP?

OLTP supports operational requests such as purchases and account updates, usually touching selected records. OLAP supports analysis across many records through scans, joins, and aggregations; it can analyze historical or recently ingested data.

Is an ERP system OLTP or OLAP?

An ERP's operational functions, such as order entry and inventory updates, are OLTP workloads. Reports and analytical models built from ERP data are OLAP workloads, whether they run inside the same platform or in a separate warehouse.

Can OLTP and OLAP run on the same database?

Yes, but analytical queries can compete with application transactions for resources. HTAP platforms are designed for mixed workloads; evaluate their isolation and performance with both workloads running concurrently.

How is OLTP data copied to an OLAP system?

Common approaches include scheduled batch ingestion, replication, and change data capture, followed by transformations into an analytical model. Freshness depends on the entire pipeline, and CDC does not automatically guarantee zero lag or consistent visibility across related tables.

How do cloud solutions affect OLTP and OLAP?

Cloud platforms offer managed transactional databases and analytical services, but deployment location doesn't change the workload distinction. Compare scaling behavior, ingestion and query costs, network charges, availability, and recovery requirements rather than assuming cloud hosting makes queries faster.

Further Reading

• AWS: OLTP and OLAP compared — workload purposes and examples.
• PostgreSQL: Transactions — grouping operations and committing or rolling back changes.
• PostgreSQL: Transaction isolation — snapshots, concurrent updates, and retries.
• PostgreSQL: WITH queries — common table expressions and data-modifying statements.
• Amazon Redshift: Columnar storage — analytical access patterns and storage layout.
• Microsoft: Star schema guidance — fact tables, dimensions, and aggregation.
• AWS DMS: Ongoing replication — log-based CDC and freshness considerations.
• TiDB: Explore HTAP — combining transactional and analytical storage engines.