Download What is a Transaction?

What is a Transaction?
• When an event in the real world changes
the state of the enterprise, a transaction is
executed to cause the corresponding
change in the database state.
• A transaction is an application program
with special properties to guarantee it
maintains database correctness
What is a Transaction Processing
System?
• A transaction process system (TPS) is an
information processing system for business
transactions
involving
the
collection,
modification and retrieval of all transaction
data.
• A Transaction Processing System consists of TP
monitor, databases, and transactions
transactions
Transaction Processing System
DBMS
database
DBMS
database
TP Monitor
System Requirements
• High Availability: on-line => must be
operational while enterprise is functioning
• High Reliability: correctly tracks state, does
not lose data, controlled concurrency
• High Throughput: many users => many
transactions/sec
• Low Response Time: on-line => users are
waiting
System Requirements (con’t)
• Long Lifetime: complex systems are not easily
replaced
– Must be designed so they can be easily extended
as the needs of the enterprise change
• Security: sensitive information must be
carefully protected since system is accessible
to many users
– Authentication, authorization, encryption
Roles in Design, Implementation, and
Maintenance of a TPS
• System Analyst - specifies system using input from
customer; provides complete description of
functionality from customer’s and user’s point of
view
• Database Designer - specifies structure of data that
will be stored in database
• Application Programmer - implements application
programs (transactions) that access data and support
enterprise rules
Roles in Design, Implementation and
Maintenance of a TPS (con’t)
• Database Administrator - maintains database
once system is operational: space allocation,
performance optimization, database security
• System Administrator - maintains transaction
processing system: monitors interconnection
of HW and SW modules, deals with failures
and congestion
Online transaction processing (OLTP)
• Online transaction processing, or OLTP, is a
class of information systems that facilitate and
manage transaction-oriented applications,
typically
for
data
entry
and
retrieval transaction processing.
Examples : OLTP
• OLTP system is a popular data processing
system in today's enterprises.
• Example: In a banking System, you withdraw
amount from your account. Then Account
Number, Withdrawal amount, Available
Amount, Balance Amount, Transaction
Number etc are operational data elements.
Advantages : OLTP
• Online transaction processing applications are
high throughput and insert or update-intensive in
database management.
• These applications are used concurrently by
hundreds of users.
• The key goals of OLTP applications are availability,
speed, concurrency and recoverability.
• Reduced paper trails and the faster, more
accurate forecast for revenues and expenses are
both examples of how OLTP makes things simpler
for businesses.
OLAP
• OLAP deals with Historical Data or Archival
Data. Historical data are those data that are
archived over a long period of time.
• Online Analytical Processing Server (OLAP) is
based on the multidimensional data model. It
allows managers, and analysts to get an
insight of the information through fast,
consistent, and interactive access to
information.
OLAP Example
• If we collect last 10 years data about flight
reservation, The data can give us many
meaningful information such as the trends in
reservation. This may give useful information
like peak time of travel, what kinds of people
are traveling in various classes
(Economy/Business)etc.
Types of OLAP Servers
We have four types of OLAP servers:
• Relational OLAP (ROLAP)
• Multidimensional OLAP (MOLAP)
• Hybrid OLAP (HOLAP)
• Specialized SQL Servers
Relational OLAP
ROLAP servers are placed between relational
back-end server and client front-end tools. To
store and manage warehouse data, ROLAP
uses relational or extended-relational DBMS.
ROLAP includes the following:
• Implementation of aggregation navigation
logic.
• Optimization for each DBMS back end.
• Additional tools and services.
Multidimensional OLAP
• MOLAP uses array-based multidimensional
storage engines for multidimensional views of
data. With multidimensional data stores, the
storage utilization may be low if the data set is
sparse.
Hybrid OLAP (HOLAP)
• Hybrid OLAP is a combination of both ROLAP
and MOLAP. It offers higher scalability of
ROLAP and faster computation of MOLAP.
HOLAP servers allows to store the large data
volumes of detailed information. The
aggregations are stored separately in MOLAP
store.
Specialized SQL Servers
• Specialized SQL servers provide advanced
query language and query processing support
for SQL queries over star and schemas in a
read-only environment.
OLTP vs. OLAP
• On-line Transaction Processing (OLTP)
– Day-to-day handling of transactions that result
from enterprise operation
– Maintains correspondence between database
state and enterprise state
• On-line Analytic Processing (OLAP)
– Analysis of information in a database for the
purpose of making management decisions
OLTP (On-line Transaction Processing)
• 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 multiaccess 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.
OLAP (On-line Analytical Processing)
• 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).
Data Mart
• A data mart is a simple form of a data
warehouse that is focused on a single subject
(or functional area), such as Sales, Finance, or
Marketing. Data marts are often built and
controlled by a single department within an
organization.
Features of Data Mart
• A data mart is a miniature data warehouse.
• It is just one segment of the data warehouse.
• Data marts receive data from a master data
warehouse through periodic updates.
• A data mart is the access layer of the data
warehouse environment that is used to get data
out to the users.
• A data mart is basically a condensed and more
focused version of a data warehouse that reflects
the regulations and process specifications of each
business unit within an organization.
Importance of Data Mart
It is easy to access frequently needed data from the database when
required by the client.
We can give access to group of users to view the Data mart when it is
required. Of course performance will be good.
It is easy to maintain and to create the data mart. It will be related to
specific business.
And It is low cost to create a data mart rather than creating data
warehouse with a huge space.
Data mart vs data warehouse
•
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Data warehouse:
Holds multiple subject areas
Holds very detailed information
Works to integrate all data sources
Does not necessarily use a dimensional model but feeds
dimensional models.
Data mart:
Often holds only one subject area- for example, Finance, or Sales
May hold more summarized data.
Concentrates on integrating information from a given subject area
or set of source systems
Is built focused on a dimensional model.
Types Of Data Mart
• Dependent data marts
• Independent data marts
• Hybrid data marts
Dependent data marts
• A dependent data mart allows you to unite
your organization's data in one data
warehouse. This gives you the usual
advantages of centralization.
Independent data marts
• An independent data mart is created without
the use of a central data warehouse. This
could be desirable for smaller groups within
an organization.
Hybrid data marts
• A hybrid data mart allows you to combine
input from sources other than a data
warehouse. This could be useful for many
situations, especially when you need ad hoc
integration, such as after a new group or
product is added to the organization.
Queries ?