DBMS Schemas for Decision Support , Star Schema, Snowflake Schema, Fact Constellation Schema, Schema Definition, Data extraction, clean up and transformation tools.
Database systems that were based on the object data model were known originally as object-oriented databases (OODBs).These are mainly used for complex objects
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
The document summarizes key topics related to transport layer protocols:
- It describes the services provided by the transport layer, including addressing, connection establishment and release, flow control, and multiplexing.
- It provides details on common transport protocols like TCP and UDP, including their packet headers, connection management, congestion control, and performance issues at high speeds.
- It also presents an example transport protocol and uses finite state machines to model its operation and connection management.
Data Link Layer
The main goal of this layer is providing reliability to the layers above it.
3.1 DLL Design Issues
What are the services provided by DLL?
3.2 Error Detection and Correction
Adding redundancy in order to find and correct errors.
3.3 DLL Protocols
xxx
3.4 Sliding Window Protocols
xxx
3.5 Protocol Specification and Verification
xxx.
3.6 Examples
This document summarizes key aspects of data integration and transformation in data mining. It discusses data integration as combining data from multiple sources to provide a unified view. Key issues in data integration include schema integration, redundancy, and resolving data conflicts. Data transformation prepares the data for mining and can include smoothing, aggregation, generalization, normalization, and attribute construction. Specific normalization techniques are also outlined.
This document provides an overview of the 3-tier data warehouse architecture. It discusses the three tiers: the bottom tier contains the data warehouse server which fetches relevant data from various data sources and loads it into the data warehouse using backend tools for extraction, cleaning, transformation and loading. The bottom tier also contains the data marts and metadata repository. The middle tier contains the OLAP server which presents multidimensional data to users from the data warehouse and data marts. The top tier contains the front-end tools like query, reporting and analysis tools that allow users to access and analyze the data.
This document provides an overview of the network layer and some of its key protocols. It begins with an introduction to the network layer and its main responsibilities, including routing packets between subnets that may have different addressing schemes or protocols. It then discusses some of the network layer's main functionalities and features. The remainder of the document defines and describes several important network layer protocols, including EIGRP, ICMP, IGMP, IPv4, and others. It provides high-level explanations of how these protocols function and their roles within the network layer.
An Introduction to Architecture of Object Oriented Database Management System and how it differs from RDBMS means Relational Database Management System
Components of DDBMS, Computer workstations or remote devices,Network hardware and software components,Communications media,transaction processor (TP), data processor (DP),
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
The document discusses query processing techniques used by database management systems. It describes how a DBMS transforms high-level queries into efficient execution plans. The key steps are parsing the query, translating it into algebraic expressions, optimizing the query through rewriting and cost estimation, and generating and executing a query execution plan. The goal is to split complex queries into simple operations and choose an efficient plan that minimizes resources.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
Wireless LANs (IEEE 802.11) allow devices to connect to a local area network using wireless transmission methods, providing flexibility, portability and mobility over wired LANs. The IEEE 802.11 standard published in 1997 defines the physical and data link layer specifications for WLANs. It has benefits like interoperability, fast development and price reductions. Common applications of WLANs include use in medical, education and emergency response situations.
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
Computer Networks Unit 2 UNIT II DATA-LINK LAYER & MEDIA ACCESSDr. SELVAGANESAN S
The document discusses data link layer framing and protocols. It describes:
1) Two main approaches to framing - byte-oriented (using sentinel characters) and bit-oriented (using bit stuffing). Protocols discussed include BISYNC, DDCMP, and HDLC.
2) Features of PPP framing including negotiated field sizes and use of LCP control messages.
3) Functions of data link layer including framing, flow control, error control, and media access control. The relationship between the logical link control and media access control sublayers is also covered.
This document discusses active databases and how they differ from conventional passive databases. Active databases can monitor a database for predefined situations and trigger actions automatically in response. This is accomplished through the use of active rules embedded within the database. The document outlines the key components of active rules, including events, conditions, and actions. It also covers the execution model of active databases and how rules are evaluated and triggered at runtime. Examples are provided of how active databases and triggers can be used for tasks like maintaining derived data values and enforcing integrity constraints.
The document provides information on the OSI reference model and TCP/IP reference model:
1. The OSI model consists of 7 layers that define a standard way for different network components to communicate with each other. Each layer provides services to the layer above it and relies on the layer below.
2. The TCP/IP model has 4 layers - application, transport, internet, and host-to-network. It is a more practical standard used to build the Internet, while the OSI model provides a theoretical framework.
3. Both models separate networking functions into logical layers for modularity and interoperability. However, the TCP/IP model combines some OSI layers and has fewer layers overall.
This document provides an overview of Ethernet and wireless computer networks. It discusses Ethernet standards and protocols including CSMA/CD, frame formats, addressing, and the transmitter algorithm. It also covers wireless networking technologies such as Bluetooth, Wi-Fi (IEEE 802.11), and WiMAX (IEEE 802.16). Key aspects summarized include the use of carrier sensing and collision detection in Ethernet, exponential backoff for retransmission after collisions, and the use of frequency hopping and direct sequence spread spectrum in wireless networks.
Operational database systems are designed to support transaction processing while data warehouses are designed to support analytical processing and report generation. Operational systems focus on business processes, contain current data, and are optimized for fast updates. Data warehouses are subject-oriented, contain historical data that is rarely changed, and are optimized for fast data retrieval. The three main components of a data warehouse architecture are the database server, OLAP server, and client tools. Data is extracted from operational systems, transformed, cleansed, and loaded into fact and dimension tables in the data warehouse using the ETL process. Multidimensional schemas like star, snowflake, and constellation organize this data. Common OLAP operations performed on the data include roll-up,
This document discusses the components and architecture of a data warehouse. It describes the major components as the source data component, data staging component, information delivery component, metadata component, and management/control component. It then discusses each of these components in more detail, specifically covering source data types, the extract-transform-load process in data staging, the data storage repository, and authentication/monitoring in information delivery. Dimensional modeling is also introduced as the preferred approach for data warehouse design compared to entity-relationship modeling.
The document discusses the complexity of implementing a data warehouse. It involves multiple steps such as collecting business requirements, creating a data model and physical design, defining data sources, choosing database and reporting tools, and updating the warehouse. No single tool can handle all data warehouse access needs, so implementations rely on a suite of tools chosen based on the type of access required. Vendors have emerged that focus on fulfilling requirements like extraction, integration, and management of metadata for data warehousing. Solutions discussed include Prism Warehouse Manager, Carleton's PASSPORT, and SAS Institute products.
1. Storage challenges - The exponentially growing volumes of data can overwhelm traditional storage systems and databases.
2. Processing challenges - Analyzing large and diverse datasets in a timely manner requires massively parallel processing across thousands of CPU cores.
3. Skill challenges - There is a shortage of data scientists and engineers with the skills needed to unlock insights from big data. Traditional IT skills are insufficient.
The document discusses data warehouses and their characteristics. A data warehouse integrates data from multiple sources and transforms it into a multidimensional structure to support decision making. It has a complex architecture including source systems, a staging area, operational data stores, and the data warehouse. A data warehouse also has a complex lifecycle as business rules change and new data requirements emerge over time, requiring the architecture to evolve.
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
This document discusses key concepts in data warehousing and modeling. It describes a multitier architecture for data warehousing consisting of a bottom tier warehouse database, middle tier OLAP server, and top tier front-end client tools. It also discusses different data warehouse models including enterprise warehouses, data marts, and virtual warehouses. The document outlines the extraction, transformation, and loading process used to populate data warehouses and the role of metadata repositories.
ETL processes , Datawarehouse and Datamarts.pptxParnalSatle
The document discusses ETL processes, data warehousing, and data marts. It defines ETL as extracting data from source systems, transforming it, and loading it into a data warehouse. Data warehouses integrate data from multiple sources to support business intelligence and analytics. Data marts are focused subsets of data warehouses that serve specific business functions or departments. The document outlines the key components and architecture of data warehousing systems, including source data, data staging, data storage in warehouses and marts, and analytical applications.
The document discusses databases versus data warehousing. It notes that databases are for operational purposes like storage and retrieval for applications, while data warehouses are used for informational purposes like business reporting and analysis. A data warehouse contains integrated, subject-oriented data from multiple sources that is used to support management decisions.
The document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used for decision making. Key aspects covered include multidimensional data modeling using facts, dimensions, and cubes; data warehouse architectures; and efficient cube computation methods such as ROLAP-based algorithms.
This document discusses building a data warehouse. It defines key components of a data warehouse including the data warehouse database, transformation tools, metadata, access tools, and data marts. It describes two common approaches to building a data warehouse - top-down and bottom-up. Top-down involves building a centralized data warehouse first while bottom-up involves building departmental data marts initially. The document also outlines considerations for designing, implementing, and accessing a data warehouse.
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptxshruthisweety4
The document discusses data warehousing and data warehouse architectures. It defines a data warehouse as a system that aggregates data from different sources into a consistent data store to support analysis and machine learning on huge volumes of historical data. It describes three common types of data warehouses and characteristics like being subject-oriented, integrated, and time-variant. It then outlines common data warehouse architectures including single tier, two tier, and three tier architectures and discusses components like the source layer, data staging, data warehouse layer, and analysis layer. Finally, it discusses properties of data warehouse architectures like separation of analytical and transactional processing and scalability.
Unit-IV-Introduction to Data Warehousing .pptxHarsha Patel
Data warehousing combines data from multiple sources to ensure data quality and accuracy. It separates analytics processing from transactional databases. A data warehouse stores historical data and allows fast querying of all data, using OLAP, while a database stores current transactions for online processing using OLTP. A multidimensional data model organizes data into cubes with dimensions and facts to allow analyzing data from different perspectives. Key components of a data warehouse architecture include external data sources, a staging area using ETL, the data warehouse, and data marts containing subsets of warehouse data.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
This document provides an overview of data warehousing and data mining. It begins by defining a data warehouse as a system that contains historical and cumulative data from single or multiple sources for simplifying reporting, analysis, and decision making. It describes three common data warehouse architectures and the key components of a data warehouse, including the database, ETL tools, metadata, query tools, and data marts. The document then defines data mining as extracting usable data from raw data using software to analyze patterns. It outlines descriptive and predictive data mining tasks and techniques like clustering, associations, summarization, prediction, and classification. Finally, it provides examples of data mining applications and discusses how AWS services like Amazon Redshift can provide scalable data warehousing
A data warehouse consists of several key components:
- Current detail data from operational systems of record which is stored for analysis.
- Integration and transformation programs that convert operational data into a common format for the data warehouse.
- Summarized and archived data used for reporting and analysis over time.
- Metadata that describes the structure and meaning of the data.
Data warehouses are used for standard reporting, queries on summarized data, and data mining of patterns in large datasets to gain business insights.
1. The document discusses data warehousing and data mining. Data warehousing involves collecting and integrating data from multiple sources to support analysis and decision making. Data mining involves analyzing large datasets to discover patterns.
2. Web mining is discussed as a type of data mining that analyzes web data. There are three domains of web mining: web content mining, web structure mining, and web usage mining. Common techniques for web mining include clustering, association rules, path analysis, and sequential patterns.
3. Web mining has benefits like addressing ineffective search engines and monitoring user visit habits to improve website design. Data warehousing and data mining can provide useful business intelligence when the right analysis techniques are applied to large amounts of integrated
The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
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Slide 1
Is Email Marketing Really Effective in 2024?
Yes, Email Marketing is still a great method for direct marketing.
Slide 2
In this article we will cover:
- What is Email Marketing?
- Pros and cons of Email Marketing.
- Tools available for Email Marketing.
- Ways to make Email Marketing effective.
Slide 3
What Is Email Marketing?
Using email to contact customers is called Email Marketing. It's a quiet and effective communication method. Mastering it can significantly boost business. In digital marketing, two long-term assets are your website and your email list. Social media apps may change, but your website and email list remain constant.
Slide 4
Types of Email Marketing:
1. Welcome Emails
2. Information Emails
3. Transactional Emails
4. Newsletter Emails
5. Lead Nurturing Emails
6. Sponsorship Emails
7. Sales Letter Emails
8. Re-Engagement Emails
9. Brand Story Emails
10. Review Request Emails
Slide 5
Advantages Of Email Marketing
1. Cost-Effective: Cheaper than other methods.
2. Easy: Simple to learn and use.
3. Targeted Audience: Reach your exact audience.
4. Detailed Messages: Convey clear, detailed messages.
5. Non-Disturbing: Less intrusive than social media.
6. Non-Irritating: Customers are less likely to get annoyed.
7. Long Format: Use detailed text, photos, and videos.
8. Easy to Unsubscribe: Customers can easily opt out.
9. Easy Tracking: Track delivery, open rates, and clicks.
10. Professional: Seen as more professional; customers read carefully.
Slide 6
Disadvantages Of Email Marketing:
1. Irrelevant Emails: Costs can rise with irrelevant emails.
2. Poor Content: Boring emails can lead to disengagement.
3. Easy Unsubscribe: Customers can easily leave your list.
Slide 7
Email Marketing Tools
Choosing a good tool involves considering:
1. Deliverability: Email delivery rate.
2. Inbox Placement: Reaching inbox, not spam or promotions.
3. Ease of Use: Simplicity of use.
4. Cost: Affordability.
5. List Maintenance: Keeping the list clean.
6. Features: Regular features like Broadcast and Sequence.
7. Automation: Better with automation.
Slide 8
Top 5 Email Marketing Tools:
1. ConvertKit
2. Get Response
3. Mailchimp
4. Active Campaign
5. Aweber
Slide 9
Email Marketing Strategy
To get good results, consider:
1. Build your own list.
2. Never buy leads.
3. Respect your customers.
4. Always provide value.
5. Don’t email just to sell.
6. Write heartfelt emails.
7. Stick to a schedule.
8. Use photos and videos.
9. Segment your list.
10. Personalize emails.
11. Ensure mobile-friendliness.
12. Optimize timing.
13. Keep designs clean.
14. Remove cold leads.
Slide 10
Uses of Email Marketing:
1. Affiliate Marketing
2. Blogging
3. Customer Relationship Management (CRM)
4. Newsletter Circulation
5. Transaction Notifications
6. Information Dissemination
7. Gathering Feedback
8. Selling Courses
9. Selling Products/Services
Read Full Article:
https://digitalsamaaj.com/is-email-marketing-effective-in-2024/
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2. DBMS Schemas for Decision Support
Schema is a logical description of the entire database.
It includes the name and description of records of all
record types including all associated data-items and
aggregates.
Much like a database, a data warehouse also requires to
maintain a schema.
A database uses relational model, while a data warehouse
uses Star, Snowflake, and Fact Constellation schema.
3. Star Schema
• Each dimension in a star schema is represented with
only one-dimension table.
• This dimension table contains the set of attributes.
• The following diagram shows the sales data of a
company with respect to the four dimensions,
namely time, item, branch, and location.
• There is a fact table at the center. It contains the keys
to each of four dimensions.
• The fact table also contains the attributes, namely
dollars sold and units sold.
5. Snowflake Schema
• Some dimension tables in the Snowflake schema are normalized.
• The normalization splits up the data into additional tables.
• Unlike Star schema, the dimensions table in a snowflake schema are
normalized.
• For example, the item dimension table in star schema is normalized
and split into two dimension tables, namely item and supplier table.
• Now the item dimension table contains the attributes item_key,
item_name, type, brand, and supplier-key.
• The supplier key is linked to the supplier dimension table. The
supplier dimension table contains the attributes supplier_key and
supplier_type.
7. Fact Constellation Schema
A fact constellation has multiple fact tables. It is also known as galaxy
schema.
The following diagram shows two fact tables, namely sales and shipping.
The sales fact table is same as that in the star schema.
The shipping fact table has the five dimensions, namely item_key, time_key,
shipper_key, from_location, to_location.
The shipping fact table also contains two measures, namely dollars sold and
units sold.
It is also possible to share dimension tables between fact tables. For example,
time, item, and location dimension tables are shared between the sales and
shipping fact table.
9. Schema Definition
Multidimensional schema is defined using Data Mining
Query Language (DMQL).
The two primitives, cube definition and dimension
definition, can be used for defining the data warehouses
and data marts.
10. Data extraction, clean up and
transformation tools
1. Tools requirements:
The tools enable sourcing of the proper data contents and formats
from operational and external data stores into the data warehouse.
The task includes:
Data transformation from one format to another
Data transformation and calculation based on the application of the
business rules. Eg : age from date of birth.
Data consolidation (several source records into single records) and
integration
11. Data extraction, clean up and
transformation tools
Meta data synchronizations and management include storing or updating
metadata definitions.
When implementing datawarehouse, several selections criteria that affect the
tools ability to transform, integrate and repair the data should be considered.
The ability to identify the data source
Support for flat files, Indexed files
Ability to merge the data from multiple data source
Ability to read information from data dictionaries
The code generated tool should be maintained in the development
environment
The ability to perform data type and character set translation is requirement
when moving data between incompatible systems.
12. Data extraction, clean up and
transformation tools
The ability to summarization and aggregations of records
The data warehouse database management system should be able to perform
the load directly from the tool using the native API.
2. Vendor approaches:
The tasks of capturing data from a source data system, cleaning transforming
it and the loading the result into a target data system.
It can be a carried out either by separate product or by single integrated
solutions. the integrated solutions are described below:
Code generators:
Create tailored 3GL/4GL transformation program based on source and target
data definitions.
The data transformations and enhancement rules defined by developer and it
employ data manipulation language.
13. Data extraction, clean up and
transformation tools
Database data Replication tools:
It employs changes to a single data source on one system and apply the
changes to a copy of the source data that are loaded on a different systems.
Rule driven dynamic transformations engines (also known as data mart
builders)
Capture the data from a source system at user defined interval, transforms the
data, then send and load the result in to a target systems.
Data Transformation and enhancement is based on a script or function logic
defined to the tool.
14. Data extraction, clean up and
transformation tools
3. Access to legacy data:
Today many businesses are adopting client/server technologies and data
warehousing to meet customer demand for new products and services to
obtain competitive advantages.
Majority of information required supporting business application and
analytical power of data warehousing is located behind mainframe based
legacy systems.
While many organizations protecting their heavy financial investment in
hardware and software to meet this goal many organization turn to
middleware solutions.
Middleware strategy is the foundation for the enterprise/access. it is designed
for scalability and manageability in a data warehousing environment.
15. Data extraction, clean up and
transformation tools
4. Vendor solutions :
4.1 Prism solutions:
Prism manager provides a solution for data warehousing by mapping source
data to target database management system.
The prism warehouse manager generates code to extract and integrate data,
create and manage metadata and create subject oriented historical database.
It extracts data from multiple sources –DB2, IMS, VSAM, RMS &sequential
files.
16. Data extraction, clean up and
transformation tools
4.2 SAS institute:
SAS data access engines serve as a extraction tools to combine common
variables, transform data
Representations forms for consistency.
it support for decision reporting ,graphing .so it act as the front end.
4.3 Carleton corporations PASSPORT and metacenter:
Carleton’s PASSPORT and the MetaCenter fulfill the data extraction and
transformation need of data warehousing.
17. Metadata
1.Metadata defined
Data about data, It contains Location and description of dw.
Names, definition, structure and content of the dw.
Identification of data sources.
Integration and transformation rules to populate dw and end user.
Information delivery information
Data warehouse operational information
Security authorization
Metadata interchange initiative
It is used for develop the standard specifications to exchange metadata
18. Metadata
2. Metadata Interchange initiative
It used for develop the standard specifications for metadata interchange
format it will allow Vendors to exchange common metadata for avoid
difficulties of exchanging, sharing and Managing metadata
The initial goals include
Creating a vendor-independent, industry defined and maintained standard access
mechanism and standard API
Enabling individual tools to satisfy their specific metadata for access
requirements, freely and easily within the context of an interchange model.
Defining a clean simple, interchange implementation infrastructure.
Creating a process and procedures for extending and updating.
19. Metadata
Metadata Interchange initiative have define two distinct Meta models
The application Metamodel- it holds the metadata for particular application
The metadata Metamodel- set of objects that the metadata interchange
standard can be used to describe
The above models represented by one or more classes of tools (data extraction,
cleanup, replication)
Metadata interchange standard framework
Metadata itself store any type of storage facility or format such as relational tables,
ASCII files ,fixed format or customized formats the Metadata interchange standard
framework will translate the an access request into interchange standard syntax and
format
20. Metadata
Metadata interchange standard framework - Accomplish following
approach
Procedural approach-
ASCII batch approach-ASCII file containing metadata standard schema
and access parameters is reloads when over a tool access metadata
through API
Hybrid approach-it follow a data driven model by implementing table
driven API, that would support only fully qualified references for each
metadata
The Components of the metadata interchange standard frame work.
The standard metadata model-which refer the ASCII file format used to
represent the metadata
21. Metadata
The standard access framework-describe the minimum number of API
function for communicate metadata.
Tool profile-the tool profile is a file that describes what aspects of the
interchange standard metamodel a particular tool supports.
The user configuration-which is a file describing the legal interchange
paths for metadata in the users environment.
22. Metadata
3. Metadata Repository
It is implemented as a part of the data warehouse frame work it following
benefits
It provides a enterprise wide metadata management.
It reduces and eliminates information redundancy, inconsistency
It simplifies management and improves organization control
It increase flexibility, control, and reliability of application development
Ability to utilize existing applications
It eliminates redundancy with ability to share and reduce metadata
23. Metadata
4. Metadata Management
The collecting, maintain and distributing metadata is needed for a successful
data warehouse implementation so these tool need to be carefully evaluated
before any purchasing decision is made
5. Implementation Example
Implementation approaches adopted by
platinum technology,
R&O,
prism solutions, and
logical works
24. Metadata
6. Metadata trends
The process of integrating external and external data into the warehouse faces
a number of challenges
Inconsistent data formats
Missing or invalid data
Different level of aggregation
Semantic inconsistency
Different types of database (text, audio, full-motion, images, temporal
databases, etc..)
The above issues put an additional burden on the collection and management
of common metadata definition this is addressed by Metadata Coalition’s
metadata interchange specification
25. Reporting, Query Tools and
Applications
Tool Categories: There are five categories of decision support tools
Reporting
Managed query
Executive information system
OLAP
Data Mining
Reporting Tools
Production Reporting Tools
Companies generate regular operational reports or support high volume batch
jobs, such as calculating and printing pay checks
Report writers
Crystal Reports/Accurate reporting system
User design and run reports without having to rely on the IS department
26. Reporting, Query Tools and
Applications
Managed query tools
Managed query tools shield end user from the Complexities of SQL and database
structures by inserting a metalayer between user and the database
Metalayer :Software that provides subject oriented views of a database and
supports point and click creation of SQL
Executive information system
First deployed on main frame system
Predate report writer and managed query tools
Build customized, graphical decision support apps or briefing books
Provides high level view of the business and access to external sources eg
custom, on-line news feed
EIS Apps highlight exceptions to business activity or rules by using color-coded
graphics
27. Reporting, Query Tools and
Applications
OLAP Tools
Provide an intuitive way to view corporate data
Provide navigation through the hierarchies and dimensions with the single click
Aggregate data along common business subjects or dimensions
Users can drill down across ,or up levels
Data mining Tools
Provide insights into corporate data that are nor easily discerned with managed
query or OLAP tools
Use variety of statistical and AI algorithm to analyze the correlation of variables
in data
28. Data Warehousing - OLAP
OLAP stands for Online Analytical Processing.
It uses database tables (fact and dimension tables) to enable multidimensional
viewing, analysis and querying of large amounts of data.
E.g. OLAP technology could provide management with fast answers to complex
queries on their operational data or enable them to analyze their company’s
historical data for trends and patterns.
Online Analytical Processing (OLAP) applications and tools are those that are
designed to ask ―complex queries of large multidimensional collections of
data. Due to that OLAP is accompanied with data warehousing.
29. Data Warehousing - OLAP
Need
The key driver of OLAP is the multidimensional nature of the business
problem.
These problems are characterized by retrieving a very large number of
records that can reach gigabytes and terabytes and summarizing this data into
a form information that can by used by business analysts.
One of the limitations that SQL has, it cannot represent these complex
problems.
A query will be translated in to several SQL statements. These SQL
statements will involve multiple joins, intermediate tables, sorting,
aggregations and a huge temporary memory to store these tables.
30. Data Warehousing - OLAP
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.
Provide an intuitive way to view corporate data.
Types of OLAP Servers:
We have four types of OLAP servers −
Relational OLAP (ROLAP)
Multidimensional OLAP (MOLAP)
Hybrid OLAP (HOLAP)
Specialized SQL Servers
31. OLAP Vs OLTP
Sr.No. Data Warehouse (OLAP) Operational Database (OLTP)
1 Involves historical processing of
information.
Involves day-to-day processing.
2 OLAP systems are used by knowledge
workers such as executives, managers
and analysts.
OLTP systems are used by clerks, DBAs,
or database professionals.
3 Useful in analyzing the business. Useful in running the business.
4 It focuses on Information out. It focuses on Data in.
5 Based on Star Schema, Snowflake,
Schema and Fact Constellation Schema.
Based on Entity Relationship Model.
6 Contains historical data. Contains current data.
32. OLAP Vs OLTP
Sr.No. Data Warehouse (OLAP) Operational Database (OLTP)
7 Provides summarized and
consolidated data.
Provides primitive and highly detailed
data.
8 Provides summarized and
multidimensional view of data.
Provides detailed and flat relational
view of data.
9 Number or users is in hundreds. Number of users is in thousands.
10 Number of records accessed is in
millions.
Number of records accessed is in tens.
11 Database size is from 100 GB to 1 TB Database size is from 100 MB to 1 GB.
12 Highly flexible. Provides high performance.
33. Multidimensional Data Model
The multidimensional data model is an integral part of On-Line Analytical
Processing, or OLAP.
Multidimensional data model is to view it as a cube. The cable at the left
contains detailed sales data by product, market and time. The cube on the
right associates sales number (unit sold) with dimensions-product type,
market and time with the unit variables organized as cell in an array.
This cube can be expended to include another array-price-which can be
associates with all or only some dimensions. As number of dimensions
increases number of cubes cell increase exponentially.
34. ETL Process in Data Warehouse
ETL stands for Extract, Transform, Load and it is a process used in data
warehousing to extract data from various sources, transform it into a format
suitable for loading into a data warehouse, and then load it into the
warehouse. The process of ETL can be broken down into the following three
stages:
Extract: The first stage in the ETL process is to extract data from various
sources such as transactional systems, spreadsheets, and flat files. This step
involves reading data from the source systems and storing it in a staging area.
Transform: In this stage, the extracted data is transformed into a format that is
suitable for loading into the data warehouse. This may involve cleaning and
validating the data, converting data types, combining data from multiple
sources, and creating new data fields.
35. ETL Process in Data Warehouse
Load: After the data is transformed, it is loaded into the data warehouse. This
step involves creating the physical data structures and loading the data into
the warehouse.
The ETL process is an iterative process that is repeated as new data is added
to the warehouse. The process is important because it ensures that the data in
the data warehouse is accurate, complete, and up-to-date. It also helps to
ensure that the data is in the format required for data mining and reporting.
Additionally, there are many different ETL tools and technologies available,
such as Informatica, Talend, DataStage, and others, that can automate and
simplify the ETL process.
ETL is a process in Data Warehousing and it stands for Extract, Transform
and Load. It is a process in which an ETL tool extracts the data from various
data source systems, transforms it in the staging area, and then finally, loads it
into the Data Warehouse system.
36. ETL Process in Data Warehouse
ETL Tools: Most
commonly used
ETL tools
are Hevo,
Sybase, Oracle
Warehouse
builder,
CloverETL, and
MarkLogic.
Data
Warehouses: M
ost commonly
used Data
Warehouses
are Snowflake,
Redshift,
BigQuery, and
Overall, ETL process is an essential process in data
warehousing that helps to ensure that the data in the data
warehouse is accurate, complete, and up-to-date.
37. ETL Process
ADVANTAGES and DISADVANTAGES
Advantages of ETL process in data warehousing:
Improved data quality: ETL process ensures that the data in the data
warehouse is accurate, complete, and up-to-date.
Better data integration: ETL process helps to integrate data from multiple
sources and systems, making it more accessible and usable.
Increased data security: ETL process can help to improve data security by
controlling access to the data warehouse and ensuring that only authorized
users can access the data.
Improved scalability: ETL process can help to improve scalability by
providing a way to manage and analyze large amounts of data.
Increased automation: ETL tools and technologies can automate and simplify
the ETL process, reducing the time and effort required to load and update data
in the warehouse.
38. ETL Process
ADVANTAGES OR DISADVANTAGES
Disadvantages of ETL process in data warehousing:
High cost: ETL process can be expensive to implement and maintain,
especially for organizations with limited resources.
Complexity: ETL process can be complex and difficult to implement,
especially for organizations that lack the necessary expertise or resources.
Limited flexibility: ETL process can be limited in terms of flexibility, as it
may not be able to handle unstructured data or real-time data streams.
Limited scalability: ETL process can be limited in terms of scalability, as it
may not be able to handle very large amounts of data.
Data privacy concerns: ETL process can raise concerns about data privacy, as
large amounts of data are collected, stored, and analyzed.
39. 10 Best Data Warehouse Tools to Explore
in 2023
1. Hevo Data
2. Amazon Web Services Data Warehouse Tools
3. Google Data Warehouse Tools
4. Microsoft Azure Data Warehouse Tools
5. Oracle Autonomous Data Warehouse
6. Snowflake
7. IBM Data Warehouse Tools
8. Teradata Vantage
9. SAS Cloud
10. SAP Data Warehouse Cloud
40. IMPORTANT WEBSITE LINKS
1. AWS Redshift: Best for real-time and predictive analytics
2. Oracle Autonomous Data Warehouse: Best for autonomous management
capabilities
3. Azure Synapse Analytics: Best for intelligent workload management
4. IBM Db2 Warehouse: Best for fully managed cloud versions
5. Teradata Vantage: Best for enhanced analytics capabilities
6. SAP BW/4HANA: Best for advanced analytics and tailored applications
7. Google BigQuery: Best for built-in query acceleration and serverless
architecture
8. Snowflake for Data Warehouse: Best for separate computation and storage
41. IMPORTANT WEBSITE LINKS
9. Cloudera Data Platform: Best for faster scaling
10. Micro Focus Vertica: Best for improved query performance
11. MarkLogic: Best for complex data challenges
12. MongoDB: Best for sophisticated access management
13. Talend: Best for simplified data governance
14. Informatica: Best for intelligent data management
15. Arm Treasure Data: Best for connected customer experience