Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
Data Warehousing (Need,Application,Architecture,Benefits), Data Mart, Schema,...Medicaps University
Data warehousing Components –Building a Data warehouse,
Need for data warehousing,
Basic elements of data warehousing,
Data Mart,
Data Extraction, Clean-up, and Transformation Tools –Metadata,
Star, Snow flake and Galaxy Schemas for Multidimensional databases,
Fact and dimension data,
Partitioning Strategy-Horizontal and Vertical Partitioning.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
Data Modeling & Metadata for Graph DatabasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra
Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
After completing this module, you will be able to:
List and describe the major components of the Teradata architecture.
Describe how the components interact to manage incoming and outgoing data.
List 5 types of Teradata database objects.
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
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
Data Warehousing (Need,Application,Architecture,Benefits), Data Mart, Schema,...Medicaps University
Data warehousing Components –Building a Data warehouse,
Need for data warehousing,
Basic elements of data warehousing,
Data Mart,
Data Extraction, Clean-up, and Transformation Tools –Metadata,
Star, Snow flake and Galaxy Schemas for Multidimensional databases,
Fact and dimension data,
Partitioning Strategy-Horizontal and Vertical Partitioning.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
Data Modeling & Metadata for Graph DatabasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra
Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
After completing this module, you will be able to:
List and describe the major components of the Teradata architecture.
Describe how the components interact to manage incoming and outgoing data.
List 5 types of Teradata database objects.
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
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
Say you have made a Data warehouse using dimensional modeling. Now the question will come how you are presenting to your stakeholder. SSRS is a great tools of Microsoft can be used. I am sharing the doc for who is going to start reporting using QUBE. Read this material with source code :
https://gallery.technet.microsoft.com/Step-by-Step-SSRS-Report-8de35ea8
informatica power center 9 Online Training is Offering at Glory IT Technologies. We have Certified Working Professionals on this Modules. They trained so many Global Students, We also Provides Corporate Training, Job/Project Support Services to informatica power center 9 . We are Only Institute Delivering Best Online Training Services to this Module.
Webinar - MariaDB Temporal Tables: a demonstrationFederico Razzoli
MariaDB Temporal Tables are useful to track how data change over time, and to handle data that refer to specific time periods.
In this webinar I showed:
* Which problems Temporal Tables solve
* How to create Temporal Tables
* How to turn regular tables into Temporal Tables
* Best practices
* Examples of what can be done with Temporal Tables
Denver MuleSoft Meetup: Cool Features in DataWeave 2.3 and 2.4Big Compass
The two Mule runtime engine releases in 2021 also included new releases of the DataWeave language. These releases have introduced powerful (or, if you prefer, "Way Cool") functionality to the language, ranging from the convenient update() operator to the esoteric levenshteinDistance() function (calculates the Levenshtein Distance for 2 strings).
While those two new features may be helpful, you may have other requirements that you were able to implement in prior versions of DataWeave, but perhaps not as cleanly or efficiently as you wish. Or you might not have known that a new DataWeave function exists to greatly simplify your code. Well, this session is for you!
One of Big Compass' Lead MuleSoft Consultants, Ben Stone, will deliver a session focusing on new DataWeave 2.3 and 2.4 features, along with other DataWeave tips and tricks he has learned in his MuleSoft implementations.
He will have numerous examples and demos to reinforce the concepts he will present.
After this presentation, you will understand how to best use the new features in DataWeave to more rapidly implement your MuleSoft solutions.
Directions for Multiple Trendlines on a Single Graph· After yo.docxlynettearnold46882
Directions for Multiple Trendlines on a Single Graph
· After you've created your graph, right click on the graph and click on Select Data.
· Click on the + button and add a title for this data (ex. Trendline 1). Then click on the little box to the right of the x values and select your x data for your first trendline.
· Then click on the little box to the right of the y values and select your y values for your first trendline.
· Then close out of the Select Data box.
· Right click on the new data points and click Add Trendline. Make sure that it is linear and select to include the individual equation.
· You can then click on the text box with the equation in it, and add in the appropriate units for the slope and y-intercept.
· You will then repeat this for the second trendline, by right clicking on the graph and click Select Data.
· Click on the + button and add a title for this data (ex. Trendline 2). Then click on the little box to the right of the x values and select your x data for your second trendline.
· Then click on the little box to the right of the y values and select your y values for your second trendline.
· Repeat the instructions from above to add the trendline and equation for the 2nd trendline.
· Repeat the above instructions for the 3rd trendline.
Project Management Case
You are working for a large, apparel design and manufacturing company, Trillo Apparel Company (TAC), headquartered in Albuquerque, New Mexico. TAC employs around 3000 people and has remained profitable through tough economic times. The operations are divided into 4 districts; District 1 – North, District 2 – South, District 3 – West and District 4 – East. The company sets strategic goals at the beginning of each year and operates with priorities to reach those goals.Trillo Apparel Company Current Year Priorities
· Increase Sales and Distribution in the East
· Improve Product Quality
· Improve Production in District 4
· Increase Brand Recognition
· Increase RevenuesCompany Details
Company Name: Trillo Apparel Company (TAC)
Company Type: Apparel design and production
Company Size: 3000 employees
Position
# Employees
Owner/CEO
1
Vice President
4
Chief Operating Officer
1
Chief Financial Officer
1
Chief Information Officer
1
IT Department
38
District Manager
4
Sales Team
30
Accountant
12
Administrative Assistant
7
Order Fullfilment
45
Customer Service
57
Designer
24
Project Manager
10
Maintenance
25
Operations
2500
Shipping Department
240
Total Employees
3000
Products: Various Apparel
Corporate Location: Albuquerque, New MexicoTAC Organization Chart
District 4 Production Warehouse Move Project Details
The business has expanded considerably over the past few years and District 4 in the East has outgrown its current production facility. Because of this growth the executives want to expand the current facility, moving the whole facility 10 miles away. The location selected has enough room for the production and the shipping department. However, the curre.
Implementing Change Systems in SQL Server 2016Douglas McClurg
Features or concepts like Change Tracking, Change Data Capture, Temporal Tables, and other similar delta systems are complex and may carry a stigma or misapprehension in your organization around performance or security or cost. Even if you do not directly implement these features or methods absolutely, most information systems rely on tracking changes especially from legacy line of business applications. I'm here to show you robust techniques for implementing delta systems in SQL Server to increase the trustworthiness of your data warehouse. I will also steer you away from common pitfalls.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
3. 3
??WWhhyy
Let’s take Sales fact table for example
Every day more and more sales take place, hence:
More and more rows are added to the fact table
Very rarely are the rows in the fact table updated with changes
Also Consider...
How will we adjust the fact table when changes are made?
4. 4
’’……WWhhyy?? ccoonntt
Consider the dimension tables
Comapred to the fact tables, they are more stable and less volatile
However, unlike fact tables, a dimension table does not change just
through the increase of number of rows, but also through
changes to the attributes themselves
5. 5
??WWhhoo?? WWhheenn
Who?
Fact tables and Dimension tables
We will focus on (Slowly Changing) Dimensions
When?
Good question:
Inside the ETL process
After the ETL process, as a stored procedure
Never (wait, you’ll see…)
6. 6
??HHooww
This is the big question.
From what we discussed for now, we can derive these principles:
Most dimensions are generally constant over time
Many dimensions, through not constant over time, change slowly
The product (business) key of the source record does not change
The description and other attributes change slowly over time
In the source OLTP system, the new values overwrite the old ones
Overwriting of dimension table attributes is not always the
appropriate option in a data warehouse
The ways changes are made to the dimension tables depend on
the types of changes and what information must be preserved in
the DWH
7. ::HHooww?? 33 AAnnsswweerrss
The usual changes to dimension tables are classified into three types
Type 1
Type 2
Type 3
We will consider the points discussed earlier when deciding which
Before going on, we must talk about one more thing.
7
type to use
Also Consider…
Do we have to use the same type for the entire DWH?
For the same dimension?
8. 8
SSuurrrrooggaattee KKeeyy
• A surrogate key is a unique identifier for the entity in the modeled
world
• It is not derived from application data
• It’s not meant to be shown outside the DWH
• It’s only significance is to act as the primary key
• Frequently it’s a sequential number (Sequence in Oracle or
Identity in SQL Server)
9. SSuurrrrooggaattee KKeeyy,, ccoonntt..
• Having the key independent of all other columns insulates the
database relationships from changes in the data values or
database design (making the database more agile) and guarentees
uniqueness
• For example: An employee ID is chosen as the neutral (business)
key of an employee DWH. Because of a merger with another
company, new employees from the merged company must be
inserted. There is one employee who works in both companies…
• If the key is a compound key, joining is more expensive because
there are multiple columns to compare. Surrogate keys are always
contained in a single column
9
10. OOuurr eexxaammppllee
For the demonstration, we’ll use this star schema:
Order fact
Product Key
Time Key
Customer Key
Salesperson Key
Order Dollars
Cost Dollars
Margin Dollars
Sale Units
Order fact
Product Key
Time Key
Customer Key
Salesperson Key
Order Dollars
Cost Dollars
Margin Dollars
Sale Units
10
Customer
Customer Key
Customer Name
Customer Code
Martial Status
Customer
Customer Key
Customer Name
Customer Code
Martial Status
Address
State
Zip
Address
State
Zip
Salesperson
Salesperson Key
Salesperson Name
Territory Name
Region Name
Salesperson
Salesperson Key
Salesperson Name
Territory Name
Region Name
Product
Product Key
Product Name
Product Code
Product Line
Product
Product Key
Product Name
Product Code
Product Line
Brand
Brand
Time
Time Key
Date
Month
Quarter
Year
Time
Time Key
Date
Month
Quarter
Year
11. 11
TTyyppee 11 CChhaannggeess
Usually relate to corrections of errors in the source system
For example, the customer dimension: Mickey Schreiber -> Miky Schreiber
Also Consider…
What will happen when number of children is changed?
12. ..TTyyppee 11 CChhaannggeess,, ccoonntt
General Principles for Type 1 changes:
Usually, the changes relate to correction of errors in the source system
Sometimes the change in the source system has no significance
The old value in the source system needs to be discarded
The change in the source system need not be preserved in the DWH
12
Also Consider…
What will happen when only the last value before the change is needed?
13. AAppppllyyiinngg TTyyppee 11 cchhaannggeess
Overwrite the attribute value in the dimension table row with the new value
The old value of the attribute is not preserved
No other changes are made in the dimension table row
The key of this dimension table or any other key values are not affected
Easiest to implement
13
33154112
Mickey Schreiber
K12356
Married
Negba 11 ST
Customer Key:
Customer Name:
Customer Code:
Martial Status:
Address:
33154112
Miky Schreiber
K12356
Married
Negba 11 ST
Customer Code:
K12356
Customer Name:
Miky Schreiber
Before After
Key Restructuring
K12356 ->
33154112
Also Consider…
Which indexes will help here?
How the “change box” will appear in the real world?
Change Box
14. 14
TTyyppee 22 CChhaannggeess
Let’s look at the martial status of Miky Schreiber
One the DWH’s requirements is to track orders by martial status (in addition to other
attributes)
All changes before 11/10/2004 will be under Martial Status = Single, and all changes after
that date will be under Martial Status = Married
We need to aggregate the orders before and after the marriage separately
Let’s make life harder:
Miky is living in Negba st., but on 30/8/2009 he moves to Avivim st.
15. ..TTyyppee 22 CChhaannggeess,, ccoonntt
General Principles for Type 2 changes:
They usually relate to true changes in source systems
There is a need to preserve history in the DWH
This type of change partitions the history in the DWH
Every change for the same attributes must be preserved
15
Also Consider…
Must we track changes for all the attributes?
For which attributes will we track changes? What are the considerations?
16. AAppppllyyiinngg TTyyppee 22 cchhaannggeess
16
Key Restructuring
K12356 -> 33154112
51141234
52789342
33154112
Miky Schreiber
K12356
Single
Negba 11 ST
Customer Key:
Customer Name:
Customer Code:
Martial Status:
Address:
51141234
Miky Schreiber
K12356
Married
Negba 11 ST
Customer Code:
K12356
Martial Status
(11/10/2004):
Married
Address (30/8/2009):
Avivim st.
Before After 11/10/2004
After 30/8/2009
52789342
Miky Schreiber
K12356
Married
Avivim st.
Also Consider…
What will happen if in addition to Address we also have State, zip code?
What will happen if the customer code will change?
Change Box
17. TTyyppee 22 ccoonncclluuddeedd
The steps:
Add a new dimension table row with the new value of the changed attribute
An effective date will be included in the dimension table
There are no changes to the original row in the dimension table
The key of the original row is not affected
The new row is inserted with a new surrogate key
17
Also Consider…
What is the data type of the effective date column? Must it contain both date and time?
How will the surrogate key be built?
Advantages? Disadvantages?
18. 18
TTyyppee 33 CChhaannggeess
Not common at all
Complex queries on type 2 changes may be
Hard to implement
Time-consuming
Hard to maintain
We want to track history without lifting heavy burden
There are many soft changes and we don’t care for the “far” history
19. 19
TTyyppee 33 CChhaannggeess
General Principles:
They usually relate to “soft” or tentative changes in the source systems
There is a need to keep track of history with old and new values of the changes
attribute
They are used to compare performances across the transition
They provide the ability to track forward and backward
20. AAppppllyyiinngg TTyyppee 33 cchhaannggeess
12345
Boris Kavkaz
(null)
Ra’anana
1/1/1998
20
Salesperson Key:
Salesperson Name:
Old Territory Name:
Current Territory Name:
Effective Date:
Salesperson ID:
RS199701
Territory Name:
Netanya
(12/1/2000)
Before After
Key Restructuring
RS199701 -> 12345
Also Consider…
12345
Boris Kavkaz
Ra’anana
Netanya
12/1/2000
What is the effective date before the change?
Can the old terriroty column contain null? What about the current territory?
21. TTyyppee 33 ccoonncclluuddeedd
No new dimension row is needed
The existing queries will seamlessly switch to the current value
Any queries that need to use the old value must be revised accordingly
The technique works best for one soft change at a time
If there is a succession of changes, more sophisticated techniques must be advised
21
22. TThheerree’’ss eevveenn mmoorree
22
Type 0 changes
Type 4 – using history tables
Type 6 – Hybrid (what about 5?)
Type 6 – Alternative implementation
SCD in OLAP
23. 23
CCoonncclluussiioonnss
3 Main ways of history tracking
Choose the way you’d like for every dimension table
You may combine the types
It all depends on the system’s requirements
24. 24
BBiibblliiooggrraapphhyy
Data Warehousing Fundamentals, Paulraj Ponniah, John
Wiley Publication
Wikipedia (Slowly Changing Dimension)