This document discusses windowing functions in SQL Server. It provides an introduction to windowing functions and the types: ranking functions, aggregate functions, and analytic functions. It then summarizes improvements in SQL Server 2012, including new analytic functions, enhancements to aggregate functions, and new keywords for defining partitions. The document ends with a question and answer section.
This is an introduction to UML (Unified Modeling Language) given to people whom has no background on business modeling using UML. This is based on UML version 2.
This is an introduction to UML (Unified Modeling Language) given to people whom has no background on business modeling using UML. This is based on UML version 2.
Enhancement of Action Description Language for UML Activity Diagram ReviewChinnapat Kaewchinporn
The UML activity diagram is graphical presentation that describes the operational process and related causes used in each stage of the system. For understanding and accurate communication, the UML standard is required for determining the congruent and consistent format application. To detect the misconception and incorrect notation, this paper presents an automation approach to reviewing UML activity diagrams based on a domain specific language, called Action Description Language (ADL). The input is the UML activity diagram in the XMI format. Due to the variations of XMI formats, the review process starts with the standardization of the XMI source file. Next, the ADL script will be created using the information extracted from the XMI file. The ADL script will then be verified against the UML constraints defined in the UML standard 2.4.1. The inspection result will be reported. In case of valid activity diagrams, the ADL scripts will be parsed to the ADL semantic model as the final output from the system. The demonstration of the proposed method was performed via three cases. Currently, the implemented prototype can review only the activity diagrams created by ArgoUML and Modelio.
Introduction to the Unified Modeling Language (UML)Marwa Ali Eissa
This Lesson covers the following topics :
Exploring the Benefits of Modeling Software
What Is a Model ?
Why Model software ?
OOSD as Model Transformation
Defining the UML
UML Elements
UML Diagrams
UML Diagrams Categories
UML CASE Tools
Fix My Functions: TSQL User Defined Functions in SQL ServerKendra Little
User Defined Functions are awesome for code reuse and expressing business rules simply — but they're famous performance killers in SQL Server. Learn which patterns cause scalar and table valued functions to drag down your query performance, and how to write your functions so they don't slack off. You'll see demos of how functions can be almost invisible in some query execution plans, and see what's really going on in your queries. You'll also learn which functions get major performance boosts from interleaved execution and the 'Froid' framework, also known as "Inline Scalar Functions" in the latest versions of SQL Server.
Are you an Oracle developer or a DBA?
Do you know the difference between aggregate and analytic functions?
Without complex sub-queries or self-joins, do you know how to:
Calculate running/cumulative totals and moving/centered averages?
List products with revenues above or below their peers or product groups?
Compute the ratio of one category’s sales to the total sales?
Select the Top-N or Top N % of the customers/products?
Classify advertisers into quartiles/n-tiles based on the revenue potential?
Compare period-over-period (year-over-year, month-over-month) growth and rank advancement?
Convert rows into columns (pivot), columns into rows (unpivot) or aggregate strings?
Perform what-if analysis and hypothetical ranking?
Analytic functions are more performant because tables need to be scanned only once. They make you more productive because there is no need to write procedural code. No wonder Tom Kyte, a well-respected Oracle guru, says analytic functions are the best thing to happen after the sliced bread.
In the first half, I will cover the basics of the various analytic functions:
Ranking: RANK, DENSE_RANK, ROW_NUMBER, NTILE, CUME_DIST, PERCENTILE_RANK
Windowing: SUM, AVG, MAX, MIN, FIRST_VALUE, LAST_VALUE
Reporting: RATIO_TO_REPORT
Others: FIRST/LAST, LEAD/LAG, hypothetical ranking,
In the second half, I will show how powerful these functions are with a few examples.
If there is time, I will cover enhanced aggregation (ROLLUP, CUBE, GROUPING SET extensions to GROUP BY clause)
This class would be useful for both developers and DBAs alike, especially for those working in Analytic, Business Intelligence, and Datawarehouse environments.
Are you already an expert in analytic functions? Then come and help me refine the content.
For more info, read
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/analysis.htm
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/aggreg.htm
rollup, cross-tabulation across different dimensions using ROLLUP, CUBE and GROUPING SETS extension to GROUP BY clause
, most active time-periods (i.e. days when the most number of tickets are open in BZ, hours with the most take-off and landings, months with the highest sales, 5-minute periods with the maximum number of calls made, etc)
data densification?
their rank last year, this year, rank growth, running/cumulative total (Year-To-Date/Month-To-Date summation), moving averages, Year-Over-Year comparison, sales projection, average/min/max time between one sale and the next sale, products with above and below average sales.
overall average, sum, departmental average, sum, ranking, job wise ranking in one SQL.
Practical SQL query monitoring and optimizationIvo Andreev
Practical SQL query monitoring and optimization
Today the project owners demand results as soon as possible and most often - for yesterday. Time to market is crucial and it is practical to deliver bit-by-bit, get feedback and grow with the number of your customers. But as the project grows, the team does too and not all have the same expertise. As well rarely in the beginning the requirements clear enough to allow performance-wise SQL interaction. In most cases there does not exist an ORM that can solve this task for you and you will need to have hard T-SQL writer in the team. If you already know this story or are going this way then in this practical session we will share how to monitor, measure and optimize your SQL code and DB layer interaction.
Enhancement of Action Description Language for UML Activity Diagram ReviewChinnapat Kaewchinporn
The UML activity diagram is graphical presentation that describes the operational process and related causes used in each stage of the system. For understanding and accurate communication, the UML standard is required for determining the congruent and consistent format application. To detect the misconception and incorrect notation, this paper presents an automation approach to reviewing UML activity diagrams based on a domain specific language, called Action Description Language (ADL). The input is the UML activity diagram in the XMI format. Due to the variations of XMI formats, the review process starts with the standardization of the XMI source file. Next, the ADL script will be created using the information extracted from the XMI file. The ADL script will then be verified against the UML constraints defined in the UML standard 2.4.1. The inspection result will be reported. In case of valid activity diagrams, the ADL scripts will be parsed to the ADL semantic model as the final output from the system. The demonstration of the proposed method was performed via three cases. Currently, the implemented prototype can review only the activity diagrams created by ArgoUML and Modelio.
Introduction to the Unified Modeling Language (UML)Marwa Ali Eissa
This Lesson covers the following topics :
Exploring the Benefits of Modeling Software
What Is a Model ?
Why Model software ?
OOSD as Model Transformation
Defining the UML
UML Elements
UML Diagrams
UML Diagrams Categories
UML CASE Tools
Fix My Functions: TSQL User Defined Functions in SQL ServerKendra Little
User Defined Functions are awesome for code reuse and expressing business rules simply — but they're famous performance killers in SQL Server. Learn which patterns cause scalar and table valued functions to drag down your query performance, and how to write your functions so they don't slack off. You'll see demos of how functions can be almost invisible in some query execution plans, and see what's really going on in your queries. You'll also learn which functions get major performance boosts from interleaved execution and the 'Froid' framework, also known as "Inline Scalar Functions" in the latest versions of SQL Server.
Are you an Oracle developer or a DBA?
Do you know the difference between aggregate and analytic functions?
Without complex sub-queries or self-joins, do you know how to:
Calculate running/cumulative totals and moving/centered averages?
List products with revenues above or below their peers or product groups?
Compute the ratio of one category’s sales to the total sales?
Select the Top-N or Top N % of the customers/products?
Classify advertisers into quartiles/n-tiles based on the revenue potential?
Compare period-over-period (year-over-year, month-over-month) growth and rank advancement?
Convert rows into columns (pivot), columns into rows (unpivot) or aggregate strings?
Perform what-if analysis and hypothetical ranking?
Analytic functions are more performant because tables need to be scanned only once. They make you more productive because there is no need to write procedural code. No wonder Tom Kyte, a well-respected Oracle guru, says analytic functions are the best thing to happen after the sliced bread.
In the first half, I will cover the basics of the various analytic functions:
Ranking: RANK, DENSE_RANK, ROW_NUMBER, NTILE, CUME_DIST, PERCENTILE_RANK
Windowing: SUM, AVG, MAX, MIN, FIRST_VALUE, LAST_VALUE
Reporting: RATIO_TO_REPORT
Others: FIRST/LAST, LEAD/LAG, hypothetical ranking,
In the second half, I will show how powerful these functions are with a few examples.
If there is time, I will cover enhanced aggregation (ROLLUP, CUBE, GROUPING SET extensions to GROUP BY clause)
This class would be useful for both developers and DBAs alike, especially for those working in Analytic, Business Intelligence, and Datawarehouse environments.
Are you already an expert in analytic functions? Then come and help me refine the content.
For more info, read
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/analysis.htm
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/aggreg.htm
rollup, cross-tabulation across different dimensions using ROLLUP, CUBE and GROUPING SETS extension to GROUP BY clause
, most active time-periods (i.e. days when the most number of tickets are open in BZ, hours with the most take-off and landings, months with the highest sales, 5-minute periods with the maximum number of calls made, etc)
data densification?
their rank last year, this year, rank growth, running/cumulative total (Year-To-Date/Month-To-Date summation), moving averages, Year-Over-Year comparison, sales projection, average/min/max time between one sale and the next sale, products with above and below average sales.
overall average, sum, departmental average, sum, ranking, job wise ranking in one SQL.
Practical SQL query monitoring and optimizationIvo Andreev
Practical SQL query monitoring and optimization
Today the project owners demand results as soon as possible and most often - for yesterday. Time to market is crucial and it is practical to deliver bit-by-bit, get feedback and grow with the number of your customers. But as the project grows, the team does too and not all have the same expertise. As well rarely in the beginning the requirements clear enough to allow performance-wise SQL interaction. In most cases there does not exist an ORM that can solve this task for you and you will need to have hard T-SQL writer in the team. If you already know this story or are going this way then in this practical session we will share how to monitor, measure and optimize your SQL code and DB layer interaction.
This presentation deals with the advanced features of SQL comprising of Arithmetic Calculations, Analytical Function, PIVOT etc. Presented by Alphalogic Inc: https://www.alphalogicinc.com/
2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologi...Jürgen Ambrosi
L’obiettivo è quello di fare una panoramica dello stato dell’arte sulle tecnologie a supporto dei database. Alcuni esempi sono la tecnologia in-memory integrata con le funzionalità di analisi operative in tempo reale e della tecnologia Always Encrypted per la protezione dei dati utilizzati in locale o durante gli spostamenti. La tecnologia in-memory consente di migliorare di 30 volte le performance delle transazioni utilizzando hardware standard di settore. Inoltre i Big Data e l'analisi sono diventati un importante fattore di differenziazione competitivo, ma la gestione delle enormi quantità di dati correlate a un tempo di attività 24 ore su 24 continua a essere una sfida per l'IT. Oggi è più importante che mai soddisfare a livello aziendale l'esigenza di prestazioni, disponibilità e sicurezza efficace per gestire carichi di lavoro mission-critical a un costo contenuto. Le soluzioni Microsoft fissano un nuovo standard nelle performance mission-critical.
Oracle 11g developer on linux training in bangaloreSuvash Chowdary
Oracle 11g Developer on linux training in Bangalore
Duration: 30 - 35 daystraining classes
Location: Courses are run in our Bangalore offices
Timings & Schedules: Both on Weekdays / Weekends
Pre-Requisite: Graduate/Software Developer/Any
Extras: Interview Questions & Answers will be covered along with course
LAB Facility: The Training is designed tentatively for each batch with Hands on Experience exposures in the Lab session. The Lab sessions are followed along with the Theory in the respective day itself.
Certification: Industry Expertise trainers will guide students to get Oracle Certification in a Latest Version & DUMPS will be provided at free of cost.
Contact : 9008500244
Oracle 11g developer on linux training in bangaloreSuvash Chowdary
Oracle 11g Developer on linux training in Bangalore
Duration: 30 - 35 daystraining classes
Location: Courses are run in our Bangalore offices
Timings & Schedules: Both on Weekdays / Weekends
Pre-Requisite: Graduate/Software Developer/Any
Extras: Interview Questions & Answers will be covered along with course
LAB Facility: The Training is designed tentatively for each batch with Hands on Experience exposures in the Lab session. The Lab sessions are followed along with the Theory in the respective day itself.
Certification: Industry Expertise trainers will guide students to get Oracle Certification in a Latest Version & DUMPS will be provided at free of cost.
Contact : 9008500244
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. Introduction
What are the windowing functions
Functions that operates/ manipulates on set of rows
Types of windowing functions
Ranking functions - Returns a ranking value for each row
Aggregate functions - Performs calculations on set of values
Analytic functions - Compute moving averages, running
totals, percentages…
Improvements of windowing functions in SQL Server
2012
3. New functionality in SQL Server
2005
OVER() Clause – Used to define a window or user
specified set of rows within a query result set
PARTITION BY Clause - Divides the query result set into
partitions
Ranking functions
Changes in aggregate functions
5. Ranking functions
ROW_NUMBER() - Sequential number of a row within
a partition of a result set
RANK() - Rank of each row within the partition
DENSE_RANK() - rank of rows within the partition of a
result set, without any gaps in the ranking
NTILE() - Distributes the rows in an ordered partition
into a specified number of groups
8. Aggregate functions cont…
Enhancements in SQL Serve 2005
SUM(Column1) OVER (PARTITION BY Column2)
Enhancements in SQL Server 2012
SUM(Column1) OVER (PARTITION BY Column2
ORDER BY Column3)
10. New features with SQL Server 2012
Analytic functions
FIRST_VALUE()
LAST_VALUE()
LEAD()
LAG()
PERCENT_RANK()
PERCENTILE_COUNT()
PERCENTILE_DISC()
CUME_DIST()
11. New features with SQL Server 2012
cont …
Enhancements of Aggregate functions
ODER BY with OVER() clause
Enhancements of defining the partition (window)
ROWS/RANGE
CURRENT ROW
PRECEDING
FOLLOWING
UNBOUNDED