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1. OBIEE 11g Training Course
OLAP Vs OLTP

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3. OLTP (ON-LINE TRANSACTION PROCESSING)
• OLTP is characterized by a large number of short on-
line transactions (INSERT, UPDATE, DELETE).
• The main emphasis for OLTP systems is put on very
fast query processing, maintaining data integrity in
multi-access environments and an effectiveness
measured by number of transactions per second.
• In OLTP database there is detailed and current
data, and schema used to store transactional
databases is the entity model (usually 3NF).
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4. OLAP (ON-LINE ANALYTICAL PROCESSING)
• OLAP is characterized by relatively low volume of
transactions.
• Queries are often very complex and involve aggregations.
• For OLAP systems a response time is an effectiveness
measure.
• OLAP applications are widely used by Data Mining
techniques.
• In OLAP database there is aggregated, historical data, stored
in multi-dimensional schemas (usually star schema).
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5. OLTP Vs OLAP
• OLTP:
On-line
Transaction
Processing
• OLAP:
Online
Analytical
Processing
• Many
short
(queries+updates).
transactions
Eg: Add a book to shopping Cart
• Long
transactions,
queries.
complex
Eg: Identifying Top selling books
• Queries touch small amounts of
data (one record or a few records)
• Queries touch large amounts of
data
• Updates are frequent.
• Updates are infrequent.
• Concurrency
is
performance Concern.
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biggest
• Individual queries can require lots
of resources.
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6. Why OLAP and OLTP don’t mix
Different performance requirements
• Transaction processing (OLTP)
− Fast response time important (< 1 sec)
− Data must be up-to-date, consistent at all times
• Data analysis (OLAP)
− Queries can consume lots of resources
− Can saturate CPUs and disk bandwidth
− Operating on static “snapshot” of data usually OK
• OLAP can “crowd out” OLTP transactions
− Transactions are slow  unhappy users
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7. Why OLAP and OLTP don’t mix
Different performance requirements
• Transaction processing (OLTP)
− Normalized schema for consistency.
− Complex data models, many tables
− Limited number of standardized queries and updates
• Data analysis (OLAP):
− Simplicity of data model is important
• Allow semi-technical users to formulate ad hoc queries
− De-normalized schemas are common
• Fewer Joins  improved query performance
• Fewer tables  schema is easier to understand
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8. Why OLAP and OLTP don’t mix
Analysis requires data from many sources:
• An OLTP system targets one specific process
− For eg: ordering from an online store
• OLAP integrates data from different processes
− Combine sales, inventory, and purchasing data
− Analyze experiments conducted by different labs
• OLAP often makes use of historical data
− Identify long-term patterns
− Notice changes in behavior over time
• Terminology, Schemas vary across data sources
• Integrating data from disparate sources is a major
challenge
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