This document provides an overview of multi-dimensional databases and star schemas. It defines key concepts like facts, dimensions, and fact and dimension tables. It explains how star schemas organize this data and allow for common OLAP operations like drilling down, rolling up, slicing and dicing. Snowflake schemas are also introduced as a variation that uses normalization. Examples of star and snowflake schemas are presented to illustrate these concepts.
An introductory session to DAX and common analytic patterns that we've built and used in enterprise environments. This session was originally presented at SQL Saturday Silicon Valley 2016.
Dianne Finch, visiting assistant professor of communications at Elon University, provided this data visualization handout from an issue of the Communications of the ACM during the SABEW 2014 session, "Data Visualization: A Hands-On Primer for Business Journalists," March 28, 2014.
For more information about training for journalists, please visit http://businessjournalism.org.
An introductory session to DAX and common analytic patterns that we've built and used in enterprise environments. This session was originally presented at SQL Saturday Silicon Valley 2016.
Dianne Finch, visiting assistant professor of communications at Elon University, provided this data visualization handout from an issue of the Communications of the ACM during the SABEW 2014 session, "Data Visualization: A Hands-On Primer for Business Journalists," March 28, 2014.
For more information about training for journalists, please visit http://businessjournalism.org.
PL/SQL applications do not live on an island - any longer. Increasingly, applications need relate to the rest of the world. Either to make themselves and the services they provide accessible to external parties - that may not speak PL/SQL at all - or to access information or enlist help from external services.
Fortunately, PL/SQL can do much more than invoke other PL/SQL applications or execute SQL. PL/SQL - sometimes in conjunctions with other components in the Oracle RDBMS - provides many inbound and outbound channels for such interactions. This session discusses and demonstrates a number of channels - when and why to use them and how to use them.
The presentation discusses how to publish data to consumers via HTTP, using both XMLDB and the Embedded PL/SQL Gateway - for example to deliver HTML, XML or RSS or to provide REST-style (web)services that are much in demand. The session also discusses the importance of email as a vehicle for human-application interaction, demonstrating how to send and how to act on received emails. An important topic is how to engage in queue based interactions (for example with a SOA infrastructure) and it concludes with how through utl_http or XMLDB and (simple) middleware, the world of SOA, REST and even the internet is ours as well. It includes a demo on 'chatting from the database' (database triggers that send out IM alerts to human agents).
PL/SQL applications do not live on an island - any longer. Increasingly, applications need relate to the rest of the world. Either to make themselves and the services they provide accessible to external parties - that may not speak PL/SQL at all - or to access information or enlist help from external services.
Fortunately, PL/SQL can do much more than invoke other PL/SQL applications or execute SQL. PL/SQL - sometimes in conjunctions with other components in the Oracle RDBMS - provides many inbound and outbound channels for such interactions. This session discusses and demonstrates a number of channels - when and why to use them and how to use them.
The presentation discusses how to publish data to consumers via HTTP, using both XMLDB and the Embedded PL/SQL Gateway - for example to deliver HTML, XML or RSS or to provide REST-style (web)services that are much in demand. The session also discusses the importance of email as a vehicle for human-application interaction, demonstrating how to send and how to act on received emails. An important topic is how to engage in queue based interactions (for example with a SOA infrastructure) and it concludes with how through utl_http or XMLDB and (simple) middleware, the world of SOA, REST and even the internet is ours as well. It includes a demo on 'chatting from the database' (database triggers that send out IM alerts to human agents).
Data marts,Types of Data Marts,Multidimensional Data Model,Fact table ,Dimension table ,Data Warehouse Schema,Star Schema,Snowflake Schema,Fact-Constellation Schema
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
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
3. 9/23/2010 3
Star SchemaStar Schema
ØØFact tableFact table
ØØDimensionsDimensions
ØØDrilling Down & Roll upDrilling Down & Roll up
ØØSlicing & DicingSlicing & Dicing
4. 9/23/2010 4
Fact
• Definition : Facts are numeric measurements (values) that
represent a specific business activity.
Facts are stored in a FACT table I.e. the center of the
star schema.
Facts are used in business data analysis, are units,
cost, prices and revenues
• Example: sales figures are numeric measurements that
represent product and/or service sales.
5. 9/23/2010 5
Fact Table
Central table
– Mostly raw numeric items
– Narrow rows, a few columns at most
– Large number of rows (millions to a billion)
– Access via dimensions
6. 9/23/2010 6
Fact Table
Definition :The centralized table in a star schema is called
as FACT table, that contains facts and connected to
dimensions. A fact table typically has two types of
columns:
Ø Contain facts and
Ø Foreign keys to dimension tables.
Ø The primary key of a fact table is usually a
composite key that is made up of all of its foreign
keys.
A fact table might contain either detail level
facts or facts that have been aggregated (fact tables
that contain aggregated facts are often instead
called summary tables). A fact table usually contains
facts with the same level of aggregation.
7. 9/23/2010 7
Dimension
• Definition : Qualifying characteristics that provide
additional perspective to a given fact.
• Example: sales might be compared by product from
region to region and from one time period to the
next.
Here sales have product, location and time dimensions.
Such dimensions are stored in DIMENSIONAL TABLE.
8. 9/23/2010 8
Dimension Tables
• Definition: The dimensions of the fact table are
further described with dimension tables
• Fact table:
Sales (Market_id, Product_Id, Time_Id, Sales_Amt)
• Dimension Tables:
Market (Market_Id, City, State, Region)
Product (Product_Id, Name, Category, Price)
Time (Time_Id, Week, Month, Quarter)
9. 9/23/2010 9
Definition: Star Schema is a relational database schema for
representing multidimensional data. It is the simplest form
of data warehouse schema that contains one or more
dimensions and fact tables.
• It is called a star schema because the entity-
relationship diagram between dimensions and fact tables
resembles a star where one fact table is connected to
multiple dimensions.
• The center of the star schema consists of a large
fact table and it points towards the dimension tables.
• The advantage of star schema are slicing down, performance
increase and easy understanding of data.
What is Star Schema?
10. 9/23/2010 10
Steps in designing Star Schema
Ø Identify a business process for analysis(like sales).
Ø Identify measures or facts (sales dollar).
Ø Identify dimensions for facts(product dimension, location
dimension, time dimension, organization dimension).
Ø List the columns that describe each dimension.(region name,
branch name, region name).
Ø Determine the lowest level of summary in a fact table(sales
dollar).
Ø In a star schema every dimension will have a primary key.
Ø In a star schema, a dimension table will not have any parent
table.
• Whereas in a snow flake schema, a dimension table will have
one or more parent tables.
Ø Hierarchies for the dimensions are stored in the dimensional
table itself in star schema.
Ø Whereas hierarchies are broken into separate tables in snow
flake schema. These hierarchies helps to drill down the data
from topmost hierarchies to the lowermost hierarchies.
11. 9/23/2010 11
Attributes
• Each dimension table contain attributes.
• Used to search, filter and classify facts.
• Example, Sales, we can identify some attributes for
each dimension:
– Product Dimension: product ID, description, product
type
– Location Dimension: region, state, city.
– Time Dimension: year quarter, month, week and date.
12. 9/23/2010 12
Attributes Hierarchy
•Definition : AH provides a top-down data organization
•Used for aggregation and drill-down/roll-up data
analysis.
•Example, location dimension attributes can be organized in a
hierarchy by region, state and city.
•AH provides the capability to perform drill-down and roll-up
searches.
•Allows the DW and OLAP systems to to have defined path.
13. 9/23/2010 13
A Concept Hierarchy: Dimension (location)
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
M. WindL. Chan
...
......
... ...
...
all
region
office
country
TorontoFrankfurtcity
14. 9/23/2010 14
A Concept Hierarchy: Dimension (location)
The Adventuresof
HuckleberryFinn
FictionAudiobooksBooks
Winnie The PoohChildrensAudiobooksBooks
The HobbitChildrensAudiobooksBooks
Wild Swans:Three
Daughtersof China
BiographiesAudiobooksBooks
High Top AlmondsArchitectureArtsand MusicBooks
Product Name
Product
Category
Product FamilyProduct Line
Product_Line->Product_Family->Product_Category->Product_Name
15. 9/23/2010 15
Multidimensional Data
• Sales volumeas afunction of product,
month, and region
ProductRegion
Month
Dimensions: Product, Location, Time
Hierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
16. 9/23/2010 16
A Sample Data Cube
Total annual sales
of TV in U.S.A.
Date
Product
Country
sum
sum
TV
VCR
PC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada
Mexico
sum
17. 9/23/2010 17
A Sample Data Cube
Total annual sales
of TV in U.S.A.
Date
Product
Country
sum
sumTV
VCR
PC
1Qtr 2Qtr3Qtr 4Qtr
U.S.A
Canada
Mexico
sum
Illnois
300Ohio
Texas
California
New York
Mac
Qtr4Qtr3Qtr2Qtr1
3466346634663466Illnois
6633663366336633Ohio
63446634466344663446Texas
200200200200California
1000100010001000New York
John
SalesSalesSalesSales
Qtr4Qtr3Qtr2Qtr1Sales Manager
Essbase
18. 9/23/2010 18
Star Schema
• A single fact tableand
for each dimension
onedimension table
• Doesnot capture
hierarchiesdirectly
20. 9/23/2010 20
In the example, sales fact table is connected to
dimensions location, product, time and organization.
It shows that data can be sliced across all
dimensions and again it is possible for the data to
be aggregated across multiple dimensions. "Sales
dollar" in sales fact table can be calculated across
all dimensions independently or in a combined manner
which is explained below.
Ø Sales dollar value for a particular product
Ø Sales dollar value for a product in a location
Ø Sales dollar value for a product in a year within a
location
Ø Sales dollar value for a product in a year within a
location sold or serviced by an employee
21. 9/23/2010 21
Example of Star Schema
•time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
province_or_street
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch
22. 9/23/2010 22
Aggregation
• Many OLAP queries involve aggregation of the data in
the fact table
• For example, to find the total sales (over time) of
each product in each market, we might use
SELECT S.Market_Id, S.Product_Id, SUM
(S.Sales_Amt)
FROM Sales S
GROUP BY S.Market_Id, S.Product_Id
• The aggregation is over the entire time dimension and
thus produces a two-dimensional view of the data
23. 9/23/2010 23
Aggregation Over Time
The output of the previous query
………P5
…70007503P4
…34503P3
…24026003P2
…15033003P1
M4M3M2M1
SUM(Sales_Amt)
Market_Id
Product_Id
24. 9/23/2010 24
Typical OLAP Operations
• Roll up (drill-up): summarize data
– by climbing up hierarchy or by dimension reduction
• Drill down (roll down): reverse of roll-up
– from higher level summary to lower level summary or
detailed data, or introducing new dimensions
• Slice and dice:
– project and select
• Pivot (rotate):
– reorient the cube, visualization, 3D to series of 2D
planes.
• Other operations
– drill across: involving (across) more than one fact table
– drill through: through the bottom level of the cube to its
back-end relational tables (using SQL)
25. 9/23/2010 25
Drilling Down and Rolling Up
• Some dimension tables form an aggregation hierarchy
Market_Id ® City ® State ® Region
• Executing a series of queries that moves down a
hierarchy (e.g., from aggregation over regions to
that over states) is called drilling down
– Requires the use of the fact table or information
more specific than the requested aggregation (e.g.,
cities)
• Executing a series of queries that moves up the
hierarchy (e.g., from states to regions) is called
rolling up
26. 9/23/2010 26
• Drilling down on market: from Region to State
Sales (Market_Id, Product_Id, Time_Id, Sales_Amt)
Market (Market_Id, City, State, Region)
– SELECT S.Product_Id, M.Region, SUM (S.Sales_Amt)
FROM Sales S, Market M
WHERE M.Market_Id = S.Market_Id
GROUP BY S.Product_Id, M.Region
– SELECT S.Product_Id, M.State, SUM (S.Sales_Amt)
FROM Sales S, Market M
WHERE M.Market_Id = S.Market_Id
GROUP BY S.Product_Id, M.State,
Drilling Down
27. 9/23/2010 27
Rolling Up
• Rolling up on market, from State to Region
– If we have already created a table, State_Sales, using
1. SELECT S.Product_Id, M.State, SUM
(S.Sales_Amt)
FROM Sales S, Market M
WHERE M.Market_Id = S.Market_Id
GROUP BY S.Product_Id, M.State
then we can roll up from there to:
2. SELECT T.Product_Id, M.Region, SUM
(T.Sales_Amt)
FROM State_Sales T, Market M
WHERE M.State = T.State
GROUP BY T.Product_Id, M.Region
28. 9/23/2010 28
Roll-up and Drill Down
Ø Sales Channel
Ø Region
Ø Country
Ø State
Ø Location Address
Ø Sales
Representative
RollUp
Higher Level of
Aggregation
Low-level
Details
Drill-Down
29. 9/23/2010 29
“Slicing and Dicing”
Product
Sales Channel
Regions
Retail Direct Special
Household
Telecomm
Video
Audio India
Far East
Europe
The Telecomm Slice
30. 9/23/2010 30
Snowflake Schema
A snowflake schema is a term that
describes a star schema structure normalized
through the use of outrigger tables. i.e
dimension table hierarchies are broken into
simpler tables. In star schema example we had
4 dimensions like location, product, time,
organization and a fact table (sales)
31. 9/23/2010 31
Snowflake schema
• Represent dimensional hierarchy directly by
normalizing tables.
• Easy to maintain and saves storage
33. 9/23/2010 33
Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
item
branch_key
branch_name
branch_type
branch
supplier_key
supplier_type
supplier
city_key
city
province_or_street
country
city