The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling.
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Smoking Cigarette kills you day by day. Tobacco, the major component of cigarette contains many chemicals that are found in various industrial & household products. These chemicals cause various diseases including cancer and leading to early death.
Cigarette Smoking harmfully affects almost all organs of the body. In these slides we have included how smoking affects body, major diseases caused by smoking, the methods to quit smoking, health & other benefits of quitting smoking. Quit Smoking Now Itself!
OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling.
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Smoking Cigarette kills you day by day. Tobacco, the major component of cigarette contains many chemicals that are found in various industrial & household products. These chemicals cause various diseases including cancer and leading to early death.
Cigarette Smoking harmfully affects almost all organs of the body. In these slides we have included how smoking affects body, major diseases caused by smoking, the methods to quit smoking, health & other benefits of quitting smoking. Quit Smoking Now Itself!
Web Metrics vs Web Behavioral Analytics and Why You Need to Know the DifferenceAlterian
An overview of the web analytics ecosystem and uncover how web behavior analytics can free you from the status quo of just counting page views. More importantly, you will discover what you need to do to truly leverage the data that is available to you from the website.
Ultimately, you will walk away with:
• An understanding of the differences between available tools
• Insight on what data to collect on your site
• Tips to help get your manager to embrace web behavior analytics
• Checklist of next steps
Business Metrics and Web Marketing
What is "business metrics"? Type of metrics in business and aviation examples.
How to distinguish traditional and dynamic metrics?
What is Ad Words
What is Acquisition Cost
What is Bounce Rate?
Most Importantly what is "Conversion Rate"?
The number of possible Web Metrics is large and increasing. Multiply by the number of Dimensions, and there is nearly an infinite number of things an analyst can look at. Get your basics down in Web Analytics 101 – Web Metrics.
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting (1) and data analysis (2). Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
This presentation contains following slides,
Introduction To OLAP
Data Warehousing Architecture
The OLAP Cube
OLTP Vs. OLAP
Types Of OLAP
ROLAP V/s MOLAP
Benefits Of OLAP
Introduction - Apache Kylin
Kylin - Architecture
Kylin - Advantages and Limitations
Introduction - Druid
Druid - Architecture
Druid vs Apache Kylin
References
For any queries
Contact Us:- argonauts007@gmail.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
2. OVERVIEW
INTRODUCTION
OLAP CUBE
HISTORY OF OLAP
OLAP OPERATIONS
DATAWAREHOUSE
DATAWAREHOUSE
ARCHITECHTURE
DIFFERENCE BETWEEN OLAP &
OLTP
TYPES OF OLAP
APPLICATIONS OF OLAP
3. INTRODUCTION TO OLAP
OLAP (online analytical processing) is
computer processing that enables a
user to easily and selectively extract and
view data from different points of view.
OLAP allows users to analyze database
information from multiple database
systems at one time.
OLAP data is stored in multidimensional
databases.
5. Some popular OLAP server software
programs include:
Oracle Express Server
Hyperion Solutions Essbase
OLAP processing is often used for data
mining.
OLAP products are typically designed for
multiple-user environments, with the cost of
the software based on the number of users.
6.
7. THE OLAP CUBE
An OLAP Cube is a data structure that allows
fast analysis of data.
The arrangement of data into cubes overcomes a
limitation of relational databases.
It consists of numeric facts called measures which
are categorized by dimensions.
The OLAP cube consists of numeric facts called
measures which are categorized by dimensions.
8. A multidimensional cube can combine
data from disparate data sources and
store the information in a fashion that is
logical for business users.
10. HISTORY OF OLAP
The term OLAP was created as a slight modification
of the traditional database term OLTP (Online
Transaction Processing).
Databases configured for OLAP employ a
multidimensional data model, allowing for complex
analytical and ad-hoc queries with a rapid execution
time.
They borrow aspects of navigational databases and
hierarchical databases that are speedier than their
relational kind.
11. /Contd…
Nigel Pendse has suggested that an alternative
and perhaps more descriptive term to describe
the concept of OLAP is Fast Analysis of
Shared Multidimensional Information
(FASMI).
The first product that performed OLAP queries
was Express, which was released in 1970 (and
acquired by Oracle in 1995 from Information
Resources). However, the term did not appear
until 1993 when it was coined by Ted Codd,
who has been described as "the father of the
relational database".
12. OLAP OPERATIONS
The user-initiated process of navigating by calling
for page displays interactively, through the
specification of slices via rotations and drill
down/up is sometimes called "slice and dice".
Slice: A slice is a subset of a multi-dimensional
array corresponding to a single value for one or
more members of the dimensions not in the
subset.
Dice: The dice operation is a slice on more than
two dimensions of a data cube (or more than two
consecutive slices).
13. Drill Down/Up: Drilling down or up is a specific
analytical technique whereby the user navigates among
levels of data ranging from the most summarized (up) to
the most detailed (down).
Roll-up: A roll-up involves computing all of the data
relationships for one or more dimensions. To do this, a
computational relationship or formula might be defined.
Pivot: To change the dimensional orientation of a report
or page display.
The output of an OLAP query is typically displayed in a
matrix (or pivot) format. The dimensions form the row
and column of the matrix; the measures, the values.
14. DATA WAREHOUSE
A data warehouse is a repository of an organization's
electronically stored data.
A data warehouse is a
o subject-oriented,
o integrated,
o time-varying,
o non-volatile
collection of data that is used primarily in organizational
decision making.
The essential components of a data warehousing system are
the means to:
Retrieve & Analyze data
Extract, Transform & Load data
Manage the data dictionary.
15. Data warehouse is a collection
of data designed to support management
decision making.
Data warehouses contain a wide variety of
data that present a coherent picture of
business conditions at a single point in time.
The term data warehousing generally refers
to the combination of many different
databases across an entire enterprise.
16.
17. BENEFITS
A data warehouse provides a common data
model for all data of interest regardless of the
data's source.
Priorto loading data into the data warehouse,
inconsistencies are identified and resolved. This
greatly simplifies reporting and analysis.
Information in the data warehouse is under the
control of data warehouse users so that, even if
the source system data is cleared over time, the
information in the warehouse can be stored
safely for extended periods of time.
18. Because they are separate from operational
systems, data warehouses provide retrieval
of data without slowing down operational
systems.
Datawarehouses facilitate decision support
system applications such as trend reports,
exception reports, and reports that show
actual performance versus goals.
Data warehouses can work in conjunction
with and, hence, enhance the value of
operational business applications, notably
customer relationship management (CRM)
systems.
19. DATA WAREHOUSE ARCHITECHTURE
Architechture is a conceptualization of how the data
warehouse is built.
One possible simple conceptualization of a data
warehouse architecture consists of the following
interconnected layers:
Operational database layer: The source data for the
data warehouse - An organization's ERP systems fall
into this layer.
Informational access layer: The data accessed for
reporting and analyzing and the tools for reporting and
analyzing data - Business intelligence tools fall into this
layer. And the Inmon-Kimball differences about design
methodology, discussed later in this article, have to do
20. Dataaccess layer: The interface between the
operational and informational access layer -
Tools to extract, transform, load data into the
warehouse fall into this layer.
Metadata layer: The data directory - This is
often usually more detailed than an operational
system data directory. There are dictionaries for
the entire warehouse and sometimes
dictionaries for the data that can be accessed by
a particular reporting and analysis tool.
21. DATA WAREHOUSING
ARCHITECHURE
Monitoring & Administration
OLAP servers
Metadata
Repository
Analysis
DATA
Extract
Query/
External WAREHOUSE
Transform Serv Reporting
Sources
Load e
Operational Refresh Data
databases Mining
22. APPLICATIONS OF
DATA WAREHOUSES
Data Mining
Web Mining
Decision Support Systems (DSS)
23. TWO TYPES OF
DATABASE ACTIVITY
OLTP (Online-Transaction
Processing)
OLAP (Online-Analytical
Processing)
24. AT A GLANCE…
OLTP: On-Line OLAP: On-Line
Transaction Processing Analytical Processing
Short Transaction both Long transactions,
query and updates usually Complex
(e.g., update account queries.
balance, enroll is
(e.g., all statistics about
courses)
sales, grouped by
Queries are Simple department and month)
(e.g., find account “Data mining”
balance, find grade in
operations.
courses)
Infrequent Updates.
Updates are frequent
(e.g., Concert tickets,
seat reservations,
shopping carts)
25. DIFFERENCE BETWEEN
OLTP & OLAP
Item OLTP OLAP
User IT Professional Knowledge worker
Functional Daily task Decision Making
DB Design Application oriented Subject oriented
Historical,
Up to date, detail,
Data multidimensional,
relational
integrated
Access Read/write Read only
DB Size 100 MB-GB 100 GB-TB
26. TYPES OF OLAP
Relational OLAP(ROLAP):
Extended RDBMS with multidimensional data
mapping to standard relational operation.
Multidimensional OLAP(MOLAP): Implemented
operation in multidimensional data.
Hybrid OnlineAnalytical Processing (HOLAP)
is a hybrid approach to the solution where the
aggregated totals are stored in a
multidimensional database while thedetail data
is stored in the relational database. This is the
balance between the data efficiency of the
ROLAP model and the performance of the
MOLAP model.
27. Relational OLAP
Provides functionality by using relational
databases and relational query tools to
store and analyze multidimensional data.
Build on existing relational technologies
and represent extension to all those
companies who already used RDBMS.
Multidimensional data schema support
within the RDBMS.
Data access language and query
performance are optimized for
multidimensional data.
Support for very large databases.
28. Multidimensional OLAP
MOLAP extends OLAP functionality to
MDBMS.
Best suited to manage, store and analyze
multidimensional data.
Proprietary techniques used in MDBMS.
MDBMS and users visualize the stored
data as a 3-Dimensional Cube i.e Data
Cube.
MOLAP Databases are known to be much
faster than the ROLAP counter parts.
Data cubes are held in memory called
“Cube Cache”
29. ROLAP v/s MOLAP
Characteristics ROLAP MOLAP
SCHEMA User star Schema User Data cubes
•Additional •Addition dimensions
dimensions can be require recreation of
added dynamically. data cube.
Database Size Medium to large Small to medium
Architecture Client/Server Client/Server
Access Support ad-hoc Limited to pre-defined
requests dimensions
30. Characteristics ROLAP MOLAP
Resources HIGH VERY HIGH
Flexibility HIGH LOW
Scalability HIGH LOW
Speed •Good with small data •Faster for small to
sets. medium data sets.
•Average for medium •Average for large
to large data set. data sets.
31. Implementation of OLAP
server
ROLAP:
Data is stored in tables in relational
database or extended relational databases.
They use an RDBMS to manage the
warehouse data and aggregations using
often a star schema.
Advantage:
Scalable
Disadvantage:
Direct access to cells.
32. MOLAP:
Implements the multidimensional view
by storing data in special
multidimensional data structures.
Advantages:
Fast indexing to pre-computed
aggregations.
Only values are stored.
Disadvantage:
Not very Scalable
33. APPLICATIONS OF OLAP
OLE DB for OLAP
OLE DB for OLAP (abbreviated ODBO) is
a Microsoft published specification and an industry
standard for multi-dimensional data processing.
ODBO is the standard application
programming interface (API) for exchanging
metadata and data between an OLAP server and a
client on a Windows platform.
ODBO was specifically designed for Online
Analytical Processing (OLAP) systems by
Microsoft as an extension to Object Linking and
Embedding Database (OLE DB).
34. /Contd…
Marketing and sales analysis
Consumer goods industries
Financial services industry
(insurance, banks etc)
Database Marketing
35. BENEFITS OF OLAP
One main benefit of OLAP is consistency of
information and calculations.
"What if" scenarios are some of the most popular uses
of OLAP software and are made eminently more possible
by multidimensional processing.
It allows a manager to pull down data from an OLAP
database in broad or specific terms.
OLAP creates a single platform for all the information
and business needs, planning, budgeting,
forecasting, reporting and analysis.