We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
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
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
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
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
INFORMATICA ONLINE TRAINING BY QUONTRA SOLUTIONS WITH PLACEMENT ASSISTANCE
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by Quontra Solutions are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Quontra Solutions is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Quontra Solutions:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
QUONTRASOLUTIONS
204-226 Imperial Drive,Rayners Lane, Harrow-HA2 7HH
Phone : +44 (0)20 3734 1498 / 99
Email: info@quontrasolutions.co.uk
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.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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.
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.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Dataware house introduction by InformaticaTrainingClasses
1. INTRODUCTION TO DATA
WAREHOUSING
BY
INFORMATICATRAININGCLASSES
PHONE : (404)-900-9988
EMAIL : INFO@INFORMATICATRAININGCLASSES.COM
WEBSITE : WWW.INFORMATICATRAININGCLASSES.COM
2. DATAWAREHOUSE
Maintain historic data
Analysis to get better understanding of business
Better Decision making
Definition: A data warehouse is a
subject-oriented
integrated
time-varying
non-volatile
collection of data that is used primarily in organizational
decision making.
-- Bill Inmon, Building the Data Warehouse
1996
3. SUBJECT ORIENTED
• Data warehouse is organized around subjects such as sales,
product, customer.
• It focuses on modeling and analysis of data for decision
makers.
• Excludes data not useful in decision support process.
4. INTEGRATED
• Data Warehouse is constructed by integrating multiple
heterogeneous sources.
• Data Preprocessing are applied to ensure consistency.
RDBMS
Legacy
System
Data
Warehouse
Flat File
Data Processing
Data Transformation
Data Processing
Data Transformation
5. NON-VOLATILE
• Mostly, data once recorded will not be updated.
• Data warehouse requires two operations in data accessing
- Incremental loading of data
- Access of data
load access
6. TIME VARIANT
• Provides information from historical perspective e.g. past 5-
10 years
• Every key structure contains either implicitly or explicitly an
element of time
7. WHY DATA WAREHOUSE?
Problem Statement:
• ABC Pvt Ltd is a company with branches at USA,
UK,CANADA,INDIA
• The Sales Manager wants quarterly sales report across the
branches.
• Each branch has a separate operational system where sales
transactions are recorded.
8. WHY DATA WAREHOUSE?
USA
UK
CANADA
INDIA
Sales
Manager
Get quarterly sales figure
for each branch
and manually calculate
sales figure across branches.
What if he need daily sales report across the branches?
9. WHY DATA WAREHOUSE?
Solution:
• Extract sales information from each database.
• Store the information in a common repository at a single site.
10. WHY DATA WAREHOUSE?
USA
UK
CANADA
INDIA
Data
Warehouse
Sales
Manager
Query &
Analysis tools
11. CHARACTERISTICS OF DATAWAREHOUSE
Relational / Multidimensional database
Query and Analysis rather than transaction
Historical data from transactions
Consolidates Multiple data sources
Separates query load from transactions
Mostly non volatile
Large amount of data in order of TBs
12. WHEN WE SAY LARGE - WE MEAN IT!
• Terabytes -- 10^12 bytes:
• Petabytes -- 10^15 bytes:
• Exabytes -- 10^18 bytes:
• Zettabytes -- 10^21 bytes:
• Zottabytes -- 10^24 bytes:
Yahoo! – 300 Terabytes and
growing
Geographic Information Systems
National Medical Records
Weather images
Intelligence Agency Videos
13. OLTP VS DATA WAREHOUSE (OLAP)
OLTP Data Warehouse (OLAP)
Indexes Few Many
Data Normalized Generally De-normalized
Joins Many Some
Derived data and aggregates Rare Common
14. DATA WAREHOUSE ARCHITECTURE
Flat
Files
ETL
(Extract
Transform
and Load)
Data
Warehouse
Sales
Data Mart
Inventory
Data Mart
Analysis
Data Mining
Reporting
Generic
Data Mart
Operational
System
Operational
System
Flat
Files
15. ETL
ETL stands for Extract, Transform and Load
Data is distributed across different sources
– Flat files, Streaming Data, DB Systems, XML, JSON
Data can be in different format
– CSV, Key Value Pairs
Different units and representation
– Country: IN or India
– Date: 20 Nov 2010 or 20101020
16. ETL FUNCTIONS
Extract
– Collect data from different sources
– Parse data
– Remove unwanted data
Transform
– Project
– Generate Surrogate keys
– Encode data
– Join data from different sources
– Aggregate
Load
17. ETL STEPS
• The first step in ETL process is mapping the data between
source systems and target database.
• The second step is cleansing of source data in staging area.
• The third step is transforming cleansed source data.
• Fourth step is loading into the target system.
Data before ETL Processing:
Data after ETL Processing:
18. ETL GLOSSARY
Mapping:
Defining relationship between source and target objects.
Cleansing:
The process of resolving inconsistencies in source data.
Transformation:
The process of manipulating data. Any manipulation beyond
copying is a transformation. Examples include aggregating, and
integrating data from multiple sources.
Staging Area:
A place where data is processed before entering the warehouse.
19. DIMENSION
Categorizes the data. For example - time, location, etc.
A dimension can have one or more attributes. For example
- day, week and month are attributes of time dimension.
Role of dimensions in data warehousing.
- Slice and dice
- Filter by dimensions
20. TYPES OF DIMENSIONS
• Conformed Dimension - A dimension that is shared across fact tables.
• Junk Dimension - A junk dimension is a convenient grouping of flags
and indicators. For example, payment method, shipping method.
• De-generated Dimension - A dimension key, that has no attributes and
hence does not have its own dimension table. For example,
transaction number, invoice number. Value of these dimension is
mostly unique within a fact table.
• Role Playing Dimensions - Role Playing dimension refers to a
dimension that play different roles in fact tables depending on the
context. For example, the Date dimension can be used for the ordered
date, shipment date, and invoice date.
• Slowly Changing Dimensions - Dimensions that have data that
changes slowly, rather than changing on a time-based, regular
schedule.
21. TYPES OF SLOWLY CHANGING DIMENSION
• Type1 - The Type 1 methodology overwrites old data with new data, and
therefore does not track historical data at all.
• Type 2 - The Type 2 method tracks historical data by creating multiple records
for a given value in dimension table with separate surrogate keys.
• Type 3 - The Type 3 method tracks changes using separate columns. Whereas
Type 2 had unlimited history preservation, Type 3 has limited history
preservation, as it's limited to the number of columns we designate for storing
historical data.
• Type 4 - The Type 4 method is usually referred to as using "history tables",
where one table keeps the current data, and an additional table is used to keep
a record of all changes.
Type 1, 2 and 3 are commonly used.
Some books talks about Type 0 and 6 also.
http://en.wikipedia.org/wiki/Slowly_changing_dimension
22. FACTS
Facts are values that can be examined and analyzed.
For Example - Page Views, Unique Users, Pieces Sold,
Profit.
Fact and measure are synonymous.
Types of facts:
– Additive - Measures that can be added across all
dimensions.
– Non Additive - Measures that cannot be added across
all dimensions.
– Semi Additive - Measures that can be added across
few dimensions and not with others.
23. HOW TO STORE DATA?
Facts and Dimensions:
1. Select the business process to model
2. Declare the grain of the business process
3. Choose the dimensions that apply to each fact table row
4. Identify the numeric facts that will populate each fact table
row
24. DIMENSION TABLE
Contains attributes of dimensions e.g. month is an attribute
of Time dimension.
Can also have foreign keys to another dimension table
Usually identified by a unique integer primary key called
surrogate key
25. FACT TABLE
Contains Facts
Foreign keys to dimension tables
Primary Key: usually composite key of all FKs
26. TYPES OF SCHEMA USED IN DATA WAREHOUSE
Star Schema
Snowflake Schema
Fact Constellation Schema
27. STAR SCHEMA
Multi-dimensional Data
Dimension and Fact Tables
A fact table with pointers to Dimension tables
29. SNOWFLAKE SCHEMA
An extension of star schema in which the dimension tables
are partly or fully normalized.
Dimension table hierarchies broken down into simpler
tables.
31. FACT CONSTELLATION SCHEMA
• A fact constellation schema allows dimension tables to be
shared between fact tables.
• This Schema is used mainly for the aggregate fact tables,
OR where we want to split a fact table for better
comprehension.
For example, a separate fact table for daily, weekly and
monthly reporting requirement.
32. FACT CONSTELLATION SCHEMA
In this example, the dimensions tables for time, item, and location are
shared between both the sales and shipping fact tables.
34. DRILL DOWN
Time
Product
Category e.g Home Appliances
Sub Category e.g Kitchen Appliances
Product e.g Toaster
35. ROLL UP
Year
Quarter
Month
Fiscal Year
Fiscal Quarter
Fiscal Month
Fiscal Week
Day
36. SLICE & DICE
Time
Product
Product = Toaster
Time
37. PIVOTING
Time
Product
• Also called rotation
• Rotate on an axis
• Interchange Rows and Columns
Region
Product
38. ADVANTAGES OF DATA WAREHOUSE
• One consistent data store for reporting, forecasting, and
analysis
• Easier and timely access to data
• Scalability
• Trend analysis and detection
• Drill down analysis
39. DISADVANTAGES OF DATA WAREHOUSE
• Preparation may be time consuming.
• High associated cost
40. CASE STUDY: WHY DATA WAREHOUSE
• G2G Courier Pvt. Ltd. is an established brand in courier
industry which has its own network in main cities and also
have sub contracted in rural areas across the country to
various partners.
• The President of the company wants to look deep into the
financial health of the company and different performance
aspects.
41. CHALLENGES
• Apart from G2G’s own transaction system, each partner has
their own system which make the data very heterogeneous.
• Granularity of data in various systems is also different. For
eg: minute accuracy and day accuracy.
• To do analysis on metrics like Revenue and Timely delivery
across various geographical locations and partner, we need
to have a unified system.