What is the main difference between OLAP and OLTP?

OLAP is for complex analysis of historical data, while OLTP is for processing many fast, routine transactions like orders and payments.

Which is better, OLAP or OLTP?

Neither is better; OLTP handles real-time business operations, whereas OLAP analyzes that data for strategic decisions, so you typically need both.

Is OLAP faster than OLTP for queries?

OLAP is faster for large, complex analytical queries, but OLTP is faster for simple, single-row operations because it minimizes latency per transaction.

What is the cost difference between OLAP and OLTP systems?

OLAP systems are usually more expensive due to high-end hardware and storage for massive datasets, while OLTP systems cost less per transaction.

What are the risks of using OLTP for reporting?

Running heavy reports on OLTP slows down live transactions and risks performance degradation, so you should use OLAP for analysis instead.

Are OLAP and OLTP compatible with the same database?

They are compatible in one database, but separating them is best because OLTP needs fast writes and OLAP needs fast reads.

What is a common beginner mistake with OLAP and OLTP?

A common mistake is using OLTP for data warehousing, which causes slow queries and locks, instead of using OLAP for aggregated analysis.

Can OLAP and OLTP be used interchangeably?

No, they cannot be used interchangeably because OLTP is optimized for insert/update operations and OLAP is optimized for read-heavy, complex aggregations.

What is a real-world use case for OLAP and OLTP together?

A retailer uses OLTP to record each sale instantly and OLAP to analyze yearly sales trends across regions for inventory planning.

Can I switch my system from OLTP to OLAP?

You cannot directly switch a system because OLTP and OLAP have different data models, but you can build an OLAP warehouse that extracts data from OLTP.

Difference Between

Difference Between Olap and Oltp

Written byNex Virox Team

Editorial Team

Reviewed byVarshal Nirbhavane

Senior SEO & Organic Growth Professional · 5+ years

Published: September 5, 202618 min read

Quick answer

The main difference between Olap and Oltp is that Olap analyzes historical data for complex queries and decision-making, while Oltp processes real-time transactions. Olap is a system for multidimensional reporting and business intelligence, while Oltp is a system for managing high-volume, day-to-day operational data.

Key takeaways

Core distinction: OLAP analyzes historical data for decisions, while OLTP processes real-time transactions.
How they work: OLAP uses complex columnar queries on aggregated snapshots, whereas OLTP executes simple row-level inserts.
Performance trade-off: OLTP prioritizes millisecond writes and high concurrency, while OLAP favors fast read-heavy analytical scans.
Best-fit use case: Choose OLAP for sales trend reporting, but select OLTP for order processing or bank withdrawals.
Common decision mistake: Using one database for both workloads causes locking conflicts and slow query responses.

Table of Contents18 sections

Difference Between Olap and Oltp: Comparison Table

Aspect	Olap	Oltp
Definition	Online Analytical Processing system for complex queries and multidimensional data analysis.	Online Transaction Processing system for managing real-time, high-volume transactional data.
Purpose	Supports business intelligence, trend analysis, forecasting, and strategic decision-making.	Captures, stores, and processes daily operational transactions like orders, payments, and bookings.
Core Mechanism	Uses star or snowflake schemas with facts and dimensions to enable rapid slicing and dicing.	Uses normalized relational tables with ACID transactions to ensure data integrity during writes.
Query Type	Executes complex, read-heavy queries that aggregate millions of rows across multiple dimensions.	Executes simple, short, repetitive insert, update, and delete operations on few rows.
Response Time	Typically ranges from seconds to minutes depending on data volume and query complexity.	Typically completes in milliseconds, often under 100 ms, to support real-time user interactions.
Data Volume	Stores terabytes to petabytes of historical data accumulated over years for analysis.	Stores gigabytes to terabytes of current, operational data, often purged or archived regularly.
Data Freshness	Loads data in batches, often daily or hourly, so analysis reflects past snapshots.	Updates data immediately per transaction, providing a real-time view of current state.
Data Model	Uses denormalized, dimensional modeling with pre-aggregated summaries for faster reads.	Uses normalized, entity-relationship modeling to eliminate redundancy and preserve integrity.
Read vs Write	Read-dominated workload, with writes occurring only during periodic data loads.	Write-dominated workload, with frequent small writes balanced by quick point reads.
Concurrency	Supports fewer concurrent users, typically dozens to hundreds of analysts running heavy queries.	Supports thousands to millions of concurrent users performing short transactions simultaneously.
Latency	High latency is acceptable; queries can take minutes without impacting business outcomes.	Low latency is critical; sub-second response is mandatory for user-facing applications.
Indexing	Uses bitmap and join indexes optimized for complex filtering and aggregation on dimensions.	Uses B-tree and hash indexes optimized for fast lookups and row-level operations.
Normalization	Deliberately denormalized to reduce joins and speed up analytical query performance.	Highly normalized, often third normal form, to minimize data duplication and update anomalies.
Backup Strategy	Backs up less frequently, often weekly or monthly, due to large data volumes and batch loads.	Backs up continuously or daily with point-in-time recovery to prevent transaction loss.
Hardware	Requires high RAM, fast SSDs, and powerful CPUs for heavy parallel scanning and sorting.	Requires high IOPS storage and robust CPU for handling many small, rapid transactions.
Scalability	Scales horizontally by partitioning data across multiple servers or using columnar storage.	Scales vertically with stronger hardware, or horizontally via sharding and read replicas.
Data Integrity	Prioritizes consistency for analysis; eventual consistency is often acceptable across loads.	Enforces strict ACID properties, guaranteeing atomicity, consistency, isolation, and durability.
Availability	Requires high availability for reporting windows, but brief downtime is tolerable.	Requires 99.99% uptime as outages directly halt revenue-generating operations.
Cost	Higher per-query cost due to massive scans, but lower transaction volume keeps total spend moderate.	Lower per-operation cost, but high transaction volume drives significant infrastructure spending.
Time Horizon	Analyzes historical data spanning months or years to identify long-term patterns.	Processes current, moment-to-moment data, typically retaining only days or weeks of detail.
User Type	Serves data analysts, data scientists, and executives who need strategic insights.	Serves operational staff, customer service agents, and end customers performing daily tasks.
Typical Examples	Amazon Redshift, Google BigQuery, Snowflake, and SAP BW for enterprise reporting.	MySQL, PostgreSQL, Oracle OLTP, and Microsoft SQL Server for order processing systems.
Use Case	Annual sales trend analysis, customer churn prediction, and product profitability modeling.	Bank ATM withdrawals, airline seat booking, e-commerce checkout, and inventory updates.
Data Granularity	Stores aggregated and summarized data, often pre-rolled to daily, monthly, or yearly levels.	Stores atomic, transaction-level detail with every individual event captured.
Query Complexity	Handles multi-table joins, subqueries, and window functions across millions of rows.	Handles simple point queries and small transactions touching fewer than 10 rows.
Storage Format	Often uses columnar storage that compresses data and speeds up column-based aggregation.	Uses row-based storage to quickly access complete records for updates and inserts.
Data Loading	Loads via ETL or ELT pipelines that transform data before or after bulk insertion.	Inserts data directly from application code in real time without transformation.
Failure Impact	Failure delays reports and decisions, but historical data remains intact and recoverable.	Failure causes immediate transaction loss, revenue interruption, and potential data corruption.
Maintenance	Requires periodic index rebuilds, partition management, and query tuning for slow aggregations.	Requires constant monitoring of lock contention, deadlocks, and transaction log growth.
Best-Fit Scenario	Choose for data warehousing, business intelligence, and complex analytical reporting workloads.	Choose for order entry, banking, booking systems, and any real-time operational application.

What Is Olap?

Olap is a computing approach for fast, multidimensional analysis of large data volumes. It organizes data into cubes with dimensions and measures so users can query, summarize, and explore business trends interactively. Olap exists to deliver instant answers to complex analytical questions that standard reporting tools answer too slowly.

Definition of Olap

Olap, or Online Analytical Processing, is a software category that enables users to analyze business data from multiple database systems simultaneously through pre-aggregated, multidimensional structures called cubes. These cubes store calculated totals, averages, and counts across dimensions like time, product, and region, allowing sub-second response times for complex analytical queries without taxing operational systems.

Key Characteristics of Olap

Characteristic	What It Means in Practice
Multidimensional model	Data is stored in cubes with axes like time, product, and region for rapid slicing.
Pre-aggregated results	Totals and averages are computed in advance, so queries return in milliseconds.
Read-optimized design	Built for heavy reading and analysis, not for frequent data writes or updates.
Historical data focus	Analyzes past performance trends over months or years, not live transaction states.
Drill-down capability	Users can navigate from summary totals to granular detail levels without new queries.
Slice and dice	Users can rotate dimensions to view the same measure from different business angles.
Complex calculations	Supports advanced formulas like period-over-period growth and share-of-total percentages.
Consolidated sources	Pulls and merges data from multiple operational databases into one unified view.
User-driven exploration	Business users create ad-hoc reports without waiting for IT or developer support.
Data warehouse dependency	Typically queries a cleansed, transformed data warehouse rather than raw source systems.

Common Examples of Olap

Power BI Analysis Services – Microsoft's cube engine powers tabular models for enterprise dashboard reporting.
SAP BusinessObjects – Offers OLAP intelligence for financial planning and consolidated corporate reporting.
Oracle OLAP Option – An embedded multidimensional engine within Oracle Database for analytic workloads.
IBM Cognos TM1 – A real-time OLAP server used heavily for budgeting, forecasting, and financial close.
Essbase – Oracle's classic multidimensional cube product still standard in banking and insurance.
Google BigQuery BI Engine – Provides in-memory OLAP-style acceleration for sub-second interactive analytics.
ClickHouse – An open-source columnar OLAP database built for high-speed aggregation on massive datasets.
Tableau Data Engine – Uses OLAP-style extracts to speed up visual exploration of large local data sources.
Apache Kylin – An open-source OLAP system that pre-computes cubes on top of Hadoop data lakes.
QlikView – Uses an associative in-memory OLAP model that links all data points for instant exploration.

Advantages and Limitations of Olap

Advantages	Limitations
Delivers near-instant answers to complex multi-dimensional queries with pre-calculated aggregations.	Requires substantial storage overhead because pre-aggregated cubes duplicate source data many times over.
Empowers business users to explore data freely without writing SQL or waiting on IT queues.	Cube refresh cycles delay data availability, so analysis never reflects real-time operational state.
Handles massive data volumes gracefully through columnar storage and compression techniques.	Initial cube build and processing times can stretch to hours for large enterprise datasets.
Delivers consistent, standardized metrics across departments because calculations are centrally defined.	Rigid cube schemas make it difficult to add new dimensions or measures once production starts.
Supports complex financial calculations like allocations, rankings, and time intelligence with ease.	Licensing costs for commercial OLAP engines are high, often exceeding six figures annually.
Provides fast drill-down from summary to detail for root-cause analysis of business anomalies.	Poorly designed cubes can mislead users when aggregation logic or hierarchies are configured incorrectly.
Reduces load on operational transaction systems by moving analytical queries to a separate platform.	Requires specialized skills in cube design and MDX or DAX query languages that are hard to hire for.
Enables what-if scenario modeling by letting analysts adjust dimensions and instantly see impacts.	Data freshness lags behind source systems, making Olap unsuitable for time-sensitive operational decisions.
Improves query performance by caching hot results and partitioning data across multiple storage tiers.	Maintaining multiple cubes for different departments leads to duplicated effort and inconsistent definitions.
Scales horizontally by distributing cube partitions across clustered servers for parallel processing.	Security management becomes complex when row-level permissions must be enforced across many dimensions.

What Is Oltp?

Oltp, or Online Transaction Processing, is a system that records and manages daily business transactions instantly. It handles high volumes of small, fast operations like orders, payments, and bookings. Oltp exists to keep live databases accurate, current, and available for immediate use.

Definition of Oltp

Oltp is a class of database software that processes many short, concurrent transactions in real time. Each transaction inserts, updates, or deletes small amounts of data while guaranteeing atomicity, consistency, isolation, and durability. The system prioritizes speed, data integrity, and rapid responses for operational workloads.

Key Characteristics of Oltp

Characteristic	What It Means in Practice
High concurrency	Thousands of users submit transactions simultaneously without locking each other out.
Fast response time	Queries typically return results in milliseconds to support real-time operations.
Atomic transactions	Each operation fully completes or fully rolls back, so partial updates never occur.
Row-level operations	Queries read or modify only a few rows at a time, not entire datasets.
ACID compliance	Atomicity, consistency, isolation, and durability protect data integrity under load.
Write-heavy workload	Systems constantly insert and update records rather than performing complex reads.
Normalized schema	Data is split into many related tables to reduce redundancy and update errors.
High availability	Uptime requirements are strict because downtime halts core business operations.
Indexed lookups	Primary keys and indexes enable quick retrieval of individual customer or order records.
Small data footprint	Each transaction touches limited data, keeping storage needs focused on current activity.

Common Examples of Oltp

Amazon checkout – processes millions of order placements and payment confirmations daily.
Bank ATM withdrawal – debits an account and records the cash dispense in one atomic step.
Airline booking system – reserves a seat and updates inventory instantly to prevent double sales.
Point-of-sale terminal – records each retail purchase and adjusts stock levels immediately.
Uber ride request – matches a driver and logs the trip start in real time.
Hospital patient admission – registers a patient and assigns a bed within seconds.
Online ticket purchase – allocates a specific concert seat and marks it sold immediately.
Hotel reservation engine – holds a room for a guest and updates availability across channels.
Social media post – inserts a new status update and pushes it to followers instantly.
Payroll processing – records each employee's hours and calculates deductions per pay period.

Advantages and Limitations of Oltp

Advantages	Limitations
Delivers sub-second response times for routine operational queries.	Performs poorly on large analytical scans that aggregate millions of rows.
Guarantees data integrity through strict ACID transaction compliance.	Normalization forces many joins, which slows complex reporting queries.
Handles thousands of simultaneous users without data corruption.	Historical data grows large and degrades performance without frequent archiving.
Provides immediate visibility into current business state.	Scaling horizontally is difficult because transactions often depend on single records.
Reduces data redundancy through normalized table structures.	Write locks can cause contention and bottlenecks during peak usage spikes.
Supports real-time decision making for frontline staff.	Backup and recovery windows are tight, risking data loss during failures.
Maintains a complete audit trail of every individual operation.	Schema changes require downtime and careful migration planning.
Optimizes for frequent small updates rather than bulk loads.	Storing years of detail makes queries slower and storage costs climb.
Enables transactional rollback to undo erroneous operations.	Not designed for trend analysis, forecasting, or business intelligence workloads.
Works reliably across distributed systems with proper coordination.	Index maintenance adds overhead that slows write performance as tables expand.

Similarities Between Olap and Oltp

Shared Aspect	How Olap and Oltp Are Alike
Data Foundation	Both Olap and Oltp systems store data in structured formats within database management systems.
Core Purpose	Olap and Oltp both manage data to support business operations and decision-making processes.
Primary Input	Olap and Oltp both accept raw transactional data as their fundamental input source.
Data Integrity	Olap and Oltp both enforce data accuracy and consistency to maintain reliable information.
SQL Support	Olap and Oltp both use SQL for querying and manipulating their underlying data stores.
Schema Design	Olap and Oltp both rely on predefined schemas to organize tables and relationships.
Hardware Dependence	Olap and Oltp both require servers with adequate CPU, memory, and storage capacity.
Security Needs	Olap and Oltp both implement user authentication and access controls to protect data.
Backup Strategy	Olap and Oltp both require regular backups to prevent data loss from failures.
Recovery Planning	Olap and Oltp both need disaster recovery plans to restore operations after outages.
Admin Oversight	Olap and Oltp both require database administrators to manage performance and configuration.
Query Language	Olap and Oltp both execute queries using standard SQL syntax for data retrieval.
Index Usage	Olap and Oltp both use indexes to accelerate data access and query execution speed.
Concurrency Control	Olap and Oltp both manage simultaneous user access to prevent data corruption.
Data Types	Olap and Oltp both handle numeric, text, and date data types in their tables.
Transaction Logs	Olap and Oltp both maintain logs to track changes and support audit trails.
Business Value	Olap and Oltp both deliver critical value by enabling data-driven business activities.
User Training	Olap and Oltp both require users to learn query tools and understand data structures.
Monitoring Tools	Olap and Oltp both use performance monitoring software to track system health metrics.
Scalability Needs	Olap and Oltp both must scale vertically or horizontally to handle growing data volumes.
Cost Factors	Olap and Oltp both incur costs for software licenses, hardware, and ongoing maintenance.
Risk Exposure	Olap and Oltp both face risks from hardware failure, cyberattacks, and human error.
Compliance Rules	Olap and Oltp both must comply with data governance and regulatory requirements.
Data Quality	Olap and Oltp both depend on clean, accurate data to produce meaningful results.
Team Collaboration	Olap and Oltp both involve collaboration between IT teams and business stakeholders.
Documentation	Olap and Oltp both require documentation of schemas, processes, and system configurations.
Performance Tuning	Olap and Oltp both require ongoing tuning to optimize query response times.
Lifecycle Management	Olap and Oltp both undergo regular upgrades, patches, and version migrations.
Long-term Storage	Olap and Oltp both retain historical data for operational reference and future analysis.
Measurable Output	Olap and Oltp both produce measurable outputs that stakeholders use to evaluate success.

Olap or Oltp: Which Should You Choose?

Your choice between Olap and Oltp comes down to one variable: whether you process transactions or analyze history. If you record orders, payments, or inventory movements, you need Oltp. If you summarize those records for reports, trends, or forecasts, you need Olap.

When to Use Olap

Choose Olap when your goal is analysis, reporting, or business intelligence over millions of historical records. Use it for complex aggregations, year-over-year comparisons, or executive dashboards. Olap suits read-heavy workloads where query speed on large datasets matters more than write speed, and where data updates occur in scheduled batches.

When to Use Oltp

Choose Oltp when your priority is capturing transactions in real time, such as sales, bookings, or logins. Use it for high-frequency inserts and updates where data integrity and sub-second response times are critical. Oltp fits customer-facing applications with thousands of concurrent users, where losing a single transaction is unacceptable.

Common Misconceptions About Olap and Oltp

Common Myth	The Reality
OLTP is just for simple data entry tasks.	OLTP handles complex, high-volume transactions like banking transfers and airline bookings, processing thousands of concurrent operations per second.
OLAP databases are always slower than OLTP systems.	OLAP systems use columnar storage and pre-aggregation to answer complex analytical queries far faster than OLTP can manage them.
You can use one database for both OLAP and OLTP workloads.	Mixing OLAP and OLTP in one database causes performance degradation; the conflicting access patterns force compromises that hurt both workloads.
OLAP only works with data warehouses, never with raw data.	OLAP can query raw, unaggregated data directly using MOLAP, ROLAP, or hybrid HOLAP architectures, not just pre-built cubes.
OLTP systems never need to be optimized for read speed.	OLTP read performance is critical; most transactions involve frequent lookups of customer records and inventory status that must return in milliseconds.
OLAP is a type of database software, not an approach.	OLAP is a processing method for multidimensional analysis; it is implemented via tools like Microsoft Analysis Services or Oracle OLAP, not a standalone product.
OLTP data is always normalized to third normal form.	Many OLTP systems use denormalized tables or star schemas for performance, sacrificing strict normalization to reduce join overhead.
OLAP queries always return results in under one second.	Complex OLAP queries on massive datasets can take minutes; sub-second response times only apply to pre-aggregated or cached dimensions.
OLTP transactions are always short and simple.	OLTP transactions can be long-running and complex, such as multi-step order processing with inventory updates and payment authorization in one unit.
OLAP is only useful for financial reporting and budgeting.	OLAP powers marketing analysis, supply chain optimization, healthcare outcome tracking, and scientific research, not just finance departments.
OLTP systems cannot handle analytical queries at all.	OLTP can run simple analytical queries like daily sales totals, but complex aggregations cause lock contention and slow down transaction processing.
OLAP cubes are the only way to perform multidimensional analysis.	Relational OLAP (ROLAP) uses standard SQL on normalized or star-schema tables, eliminating the need for physical cube storage entirely.
OLTP databases are always smaller than OLAP databases.	OLTP databases can be massive, like social media platforms storing billions of user posts, while OLAP datasets may be smaller but highly denormalized.
OLAP and OLTP are mutually exclusive; you cannot have both.	Modern architectures use HTAP (Hybrid Transactional/Analytical Processing) systems like TiDB or SAP HANA that handle both OLAP and OLTP simultaneously.
OLTP requires ACID compliance, but OLAP does not need it.	OLAP systems use eventual consistency or snapshot isolation; they sacrifice strict ACID for faster reads, but still require data integrity for accurate analysis.
OLAP is only for large enterprises with huge budgets.	Open-source OLAP tools like Apache Druid, ClickHouse, and DuckDB bring multidimensional analysis to startups and individual analysts at zero cost.
OLTP systems use only row-based storage, never columnar.	Some OLTP databases like MemSQL and SingleStore use columnar storage for analytical queries while maintaining row-based storage for transactions.
OLAP queries are always written in SQL.	OLAP supports MDX (Multidimensional Expressions) and DAX (Data Analysis Expressions) for cube queries, plus specialized APIs for pivot operations.
OLTP is outdated and being replaced by OLAP technology.	OLTP remains essential for e-commerce, banking, and booking systems; OLAP complements it by analyzing the transaction data OLTP generates.
OLAP data is always historical, never real-time.	Real-time OLAP systems like Apache Pinot and Druid ingest streaming data from Kafka, enabling live dashboards with sub-second latency.
OLTP databases do not use indexes because they slow down writes.	OLTP systems rely heavily on B-tree and hash indexes to speed up point lookups; the write overhead is a trade-off for faster reads.
OLAP is the same as business intelligence (BI) reporting.	OLAP is the analytical engine beneath BI tools; BI adds dashboards, visualizations, and storytelling on top of OLAP's multidimensional queries.
OLTP cannot handle big data volumes beyond a few gigabytes.	Modern OLTP systems like PostgreSQL and MySQL scale to terabytes using sharding, partitioning, and read replicas for high-throughput workloads.
OLAP requires a separate server from OLTP to function properly.	OLAP and OLTP can share a server using resource isolation or containerization, though dedicated hardware often improves performance for both.
OLTP transactions are always committed immediately without delay.	OLTP transactions can experience latency from network round-trips, disk fsync, and lock waits, especially under high concurrency pressure.
OLAP is only about slicing and dicing data into pivot tables.	OLAP includes advanced operations like drill-down, roll-up, dice, slice, and pivot, plus statistical forecasting and what-if scenario modeling.
OLTP is not relevant for analytics or decision-making.	OLTP generates the raw transaction logs and operational metrics that feed OLAP systems, making it the foundation of all data-driven decisions.
OLAP databases cannot handle updates or inserts efficiently.	OLAP systems like ClickHouse and Druid support batch inserts and updates, though they optimize for append-heavy loads over frequent single-row changes.
OLTP and OLAP are just different names for the same thing.	OLTP optimizes for fast, atomic transactions with high concurrency; OLAP optimizes for complex read-only queries over large historical datasets.
OLAP is only for analysts, not for application developers.	OLAP APIs and embedded analytics libraries let developers integrate multidimensional querying directly into customer-facing applications and dashboards.

Conclusion

Difference Between Olap and Oltp is simple: OLAP analyzes historical data for decisions, while OLTP processes live transactions. Choose OLAP for reporting, trends, and business intelligence. Choose OLTP for daily operations like orders, payments, or inventory updates. Your workload determines the correct system.

FAQs on Difference Between Olap and Oltp

What is the main difference between OLAP and OLTP?: OLAP is for complex analysis of historical data, while OLTP is for processing many fast, routine transactions like orders and payments.
Which is better, OLAP or OLTP?: Neither is better; OLTP handles real-time business operations, whereas OLAP analyzes that data for strategic decisions, so you typically need both.
Is OLAP faster than OLTP for queries?: OLAP is faster for large, complex analytical queries, but OLTP is faster for simple, single-row operations because it minimizes latency per transaction.
What is the cost difference between OLAP and OLTP systems?: OLAP systems are usually more expensive due to high-end hardware and storage for massive datasets, while OLTP systems cost less per transaction.
What are the risks of using OLTP for reporting?: Running heavy reports on OLTP slows down live transactions and risks performance degradation, so you should use OLAP for analysis instead.
Are OLAP and OLTP compatible with the same database?: They are compatible in one database, but separating them is best because OLTP needs fast writes and OLAP needs fast reads.
What is a common beginner mistake with OLAP and OLTP?: A common mistake is using OLTP for data warehousing, which causes slow queries and locks, instead of using OLAP for aggregated analysis.
Can OLAP and OLTP be used interchangeably?: No, they cannot be used interchangeably because OLTP is optimized for insert/update operations and OLAP is optimized for read-heavy, complex aggregations.
What is a real-world use case for OLAP and OLTP together?: A retailer uses OLTP to record each sale instantly and OLAP to analyze yearly sales trends across regions for inventory planning.
Can I switch my system from OLTP to OLAP?: You cannot directly switch a system because OLTP and OLAP have different data models, but you can build an OLAP warehouse that extracts data from OLTP.