This document provides an overview of dimensional modeling and loading dimensions and cubes using Oracle Warehouse Builder. It discusses key concepts like dimensional modeling, star schemas, slowly changing dimensions, and orphan management. It also describes how to define dimensional objects like dimensions and cubes in OWB and how the dimension and cube operators help to efficiently load data into those objects while handling issues like SCDs and orphans. Overall, it outlines how OWB supports dimensional modeling and ETL to help implement production data warehouse processes.
This document describes the dimensional modeling features in Oracle Warehouse Builder 11gR2. It discusses how OWB allows users to efficiently model dimensions and cubes using wizards. It also covers how the dimension and cube operators can be used to implement loading processes with functionality for slowly changing dimensions, orphan management, and materialized view creation. However, the document notes that automatic binding of dimensions may overwrite manual changes and that some default settings, like temporary tables, cannot be disabled.
This document discusses SQL Server 2005/2008 performance tuning for developers. It provides an agenda that covers general performance concepts like bottlenecks, tools, tables/indexes, and the query optimizer. The second part of the agenda covers development performance tips related to T-SQL commands, views, cursors, functions, temporary objects, stored procedures, data manipulation, transactions, dynamic SQL, triggers, locks, and table/database design. The presentation emphasizes starting performance optimization early and treating code as if it runs frequently on large data in a busy system.
This document discusses considerations for migrating to DB2 10 from earlier versions. It notes that IBM is ending support for DB2 V8 in 2012, prompting many organizations to migrate. Key topics covered include potential issues with skipping versions in migration, features deprecated in later versions, checking software prerequisites, and rebinding plans and packages to adjust to changes in access paths. The document aims to provide guidance on planning a smoother migration process.
SSIS gives you flexibility and power to manage your simple or complex ETL Projects using native SSIS features. But certain things still cannot be accomplished easily or are impossible to perform without extensive knowledge of programming. Task Factory is a collection of essential, high-performance components and tasks for SSIS that eliminate the need for programming. Using Task Factory can increase productivity and can give you a much higher level of performance.
Abstract - DB2 10 for z/OS - Where we are today, and where we are going. This session will take you through the latest with DB2 10. What functions are customers finding most valuable, what the latest enhancements are, and what is the current status of DB2 10 in the marketplace? We will also take you through the latest on DB2 11, the status of the ESP, and also touch on some industry trends that are influencing the enhancements that we are planning for DB2 in the future.
Mas 90-and-mas-200-crystal-reports-manualmtsisolutions
Crystal Reports provides customizable report templates for key Sage MAS 90 and 200 modules that allow users to design and distribute high-quality reports. It includes over 25 pre-built report templates and lets users modify template designs. Crystal Reports integrates directly with Sage MAS 90 and 200 data and allows exporting reports to other programs. It provides tools for customizing fonts, layouts, graphs, and more to enhance report presentation.
This document provides an overview of dimensional modeling and loading dimensions and cubes using Oracle Warehouse Builder. It discusses key concepts like dimensional modeling, star schemas, slowly changing dimensions, and orphan management. It also describes how to define dimensional objects like dimensions and cubes in OWB and how the dimension and cube operators help to efficiently load data into those objects while handling issues like SCDs and orphans. Overall, it outlines how OWB supports dimensional modeling and ETL to help implement production data warehouse processes.
This document describes the dimensional modeling features in Oracle Warehouse Builder 11gR2. It discusses how OWB allows users to efficiently model dimensions and cubes using wizards. It also covers how the dimension and cube operators can be used to implement loading processes with functionality for slowly changing dimensions, orphan management, and materialized view creation. However, the document notes that automatic binding of dimensions may overwrite manual changes and that some default settings, like temporary tables, cannot be disabled.
This document discusses SQL Server 2005/2008 performance tuning for developers. It provides an agenda that covers general performance concepts like bottlenecks, tools, tables/indexes, and the query optimizer. The second part of the agenda covers development performance tips related to T-SQL commands, views, cursors, functions, temporary objects, stored procedures, data manipulation, transactions, dynamic SQL, triggers, locks, and table/database design. The presentation emphasizes starting performance optimization early and treating code as if it runs frequently on large data in a busy system.
This document discusses considerations for migrating to DB2 10 from earlier versions. It notes that IBM is ending support for DB2 V8 in 2012, prompting many organizations to migrate. Key topics covered include potential issues with skipping versions in migration, features deprecated in later versions, checking software prerequisites, and rebinding plans and packages to adjust to changes in access paths. The document aims to provide guidance on planning a smoother migration process.
SSIS gives you flexibility and power to manage your simple or complex ETL Projects using native SSIS features. But certain things still cannot be accomplished easily or are impossible to perform without extensive knowledge of programming. Task Factory is a collection of essential, high-performance components and tasks for SSIS that eliminate the need for programming. Using Task Factory can increase productivity and can give you a much higher level of performance.
Abstract - DB2 10 for z/OS - Where we are today, and where we are going. This session will take you through the latest with DB2 10. What functions are customers finding most valuable, what the latest enhancements are, and what is the current status of DB2 10 in the marketplace? We will also take you through the latest on DB2 11, the status of the ESP, and also touch on some industry trends that are influencing the enhancements that we are planning for DB2 in the future.
Mas 90-and-mas-200-crystal-reports-manualmtsisolutions
Crystal Reports provides customizable report templates for key Sage MAS 90 and 200 modules that allow users to design and distribute high-quality reports. It includes over 25 pre-built report templates and lets users modify template designs. Crystal Reports integrates directly with Sage MAS 90 and 200 data and allows exporting reports to other programs. It provides tools for customizing fonts, layouts, graphs, and more to enhance report presentation.
Panorama comparatif des outils de reporting et Dashboarding Microsoft : Excel, SSRS et Power View. Les plus de chacun de ces outils de reporting pour répondre à tous vos besoins.
Speakers : Stéphane Vivien (GFI Informatique), Michael Nokhamzon (GFI Informatique), Laurent Miltgen-Delinchamp (Cumulos)
This document discusses strategies for monitoring and improving the performance of SQL Server Analysis Services (SSAS) cubes. It recommends monitoring performance counters, dynamic management views, execution logs, and profiler traces to identify bottlenecks. Some quick solutions include warming the cache by running top queries, while more complex issues may require tuning aggregations, partitions, or MDX queries. The document provides demos of monitoring tools and techniques for analyzing processing logs to further optimize cube performance.
The document discusses new features in SQL Server Analysis Services (SSAS) "Denali" release including a new unified BI Semantic Model that brings together relational and multidimensional data models. It provides more flexibility and choices in building BI applications using either tabular or multidimensional approaches. Denali also improves performance and scalability with new in-memory and compression technologies. New tools are introduced for data modeling and management.
Session des Journées SQL Server 2014 - Patrice Harel
---
L’objectif sera de comparer les comportements entre les résultats des modèles AML et ceux de SSAS.
Solutions for Sage Customers from Robert LaverySuzanne Spear
The document discusses how Norming Software solutions can help secure assets and data in Sage 300 ERP (Accpac). It describes modules that can restrict user access and transactions for banks, customers, vendors, inventory items, locations, and more based on permission settings. Demo modules were shown to control access to sensitive data for banking, accounts receivable, accounts payable, inventory, purchasing, and sales within Accpac.
This is my presentation at SQLBits 8, Brighton, 9th April 2011. This session is about advanced dimensional modelling topics such as Fact Table Primary Key, Vertical Fact Tables, Aggregate Fact Tables, SCD Type 6, Snapshotting Transaction Fact Tables, 1 or 2 Dimensions, Dealing with Currency Rates, When to Snowflake, Dimensions with Multi Valued Attributes, Transaction-Level Dimensions, Very Large Dimensions, A Dimension With Only 1 Attribute, Rapidly Changing Dimensions, Banding Dimension Rows, Stamping Dimension Rows and Real Time Fact Table. Prerequisites: You need have a basic knowledge of dimensional modelling and relational database design.
My name is Vincent Rainardi. I am a data warehouse & BI architect. I wrote a book on SQL Server data warehousing & BI, as well as many articles on my blog, www.datawarehouse.org.uk. I welcome questions and discussions on data warehousing on vrainardi@gmail.com. Enjoy the presentation.
This document provides an overview of advanced dimensional modelling techniques. It discusses:
1) Dimension structures such as slowly changing dimension type 6, using one or two dimensions, and when to snowflake dimensions.
2) Fact table considerations like primary keys, snapshotting transaction fact tables, aggregate fact tables, and vertical fact tables.
3) Dimension behaviors like rapidly changing dimensions, very large dimensions, banding and stamping dimension rows, and dimensions with multi-valued attributes.
4) Combination techniques involving real-time fact tables, dealing with currency rates and status values. The document covers several sections and many modelling patterns in 44 slides.
The document discusses various topics related to web metrics and analytics tools. It provides an overview of factors to consider when choosing tools, such as organizational needs, costs, implementation challenges, and level of sophistication required. It also covers specific metrics like visitor acquisition, segmentation, time on site, click density and heat maps. The document emphasizes focusing on a handful of key performance indicators rather than all possible metrics, and approaches like optimizing the customer journey and lifecycle. Overall it provides guidance on setting up an effective analytics program.
This document discusses big data concepts and applications. It begins by defining big data characteristics including volume, velocity, and variety. It then outlines common big data applications in business intelligence and transactions. Different big data architectures like MapReduce, massively parallel processing databases, and in-memory databases are described along with their strengths and limitations. The document concludes with a demonstration of exploring millions of US patent pages in real-time using various big data technologies.
This document summarizes an event being held by #75PRESENTS on October 3rd 2018. The event includes three presentations on DynamoDB by PolarSeven, data protection on AWS using Commvault, and incident management with PagerDuty. There will be pizza and beer during a break between the first two presentations. The document provides details on each presentation including speakers and topics to be covered.
This document proposes a new approach called a temporal snapshot fact table to analyze insurance data daily over many decades in a space-efficient manner. Traditional solutions like transactional, accumulating, and periodic snapshot fact tables are not feasible due to the large volume of data. The new approach models each fact row as a time interval rather than a point in time. This reduces duplication and allows representing the data using temporal logic and operators. Technical challenges around data integration and cube modeling are addressed through refactoring the source data into a common set of time intervals and modeling the intervals relationally in the cube.
Microsoft SQL Server - How to Collaboratively Manage Excel DataMark Ginnebaugh
How to Collaboratively Manage Excel-Based Process Data in SQL Server
Your organization probably uses Excel for a variety of business processes including budgeting, sales revenue forecasting, product demand planning, and project management.
You'll learn how to set up and manage multi-user collaborative processes using Excel as the data form and SQL Server as the data store and process engine.
You'll learn:
* How to enable cell-level collaboration between multiple users using Excel and SQL Server.
* How to effectively integrate desktop Excel-based process data with enterprise applications.
* How to mitigate the limitations normally associated with Excel-to-database connections including record locking (check-in/out), conflict management, and change management and versioning.
This document discusses an agile approach to developing a data warehouse. It advocates using an Agile Enterprise Data Model to provide vision and guidance. The "Spock Approach" is described, which uses an operational data store, dimensional data warehouse, and iterative development of data marts. Data visualization techniques like data hexes are recommended to improve planning and visibility. Leadership, version control, adaptability, refinement, and refactoring are identified as important ongoing processes for an agile data warehouse project.
Building a highly scalable and available cloud applicationNoam Sheffer
This document discusses lessons learned from building large, scalable applications on Azure. It emphasizes designing for scale from the start by making applications stateless and partitioning data. It also stresses designing for failure since failures will occur at large scale. Other key lessons include optimizing for density to reduce costs, using telemetry to monitor applications, and handling transient and enduring failures through retries and failover. The presenter concludes by offering to share more detailed guidance and reusable patterns for building scalable Azure applications.
Database Virtualization: The Next Wave of Big Dataexponential-inc
Servers, Storage and Networking have all been virtualized, the next big wave is the database. SQL databases are the one thing in the cloud that require single dedicated instances. Database virtualization changes all of this, enabling full elasticity without sacrificing functionality.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
Netflix migrated to the cloud to avoid single points of failure and to focus on their core competencies. They chose Amazon Web Services and migrated non-sensitive data and applications to the cloud. Netflix picked SimpleDB and S3 as their data stores in the cloud. Migrating from an RDBMS required translating relational concepts like normalization to key-value stores and working around issues with SimpleDB like lack of data types and transactions.
Everything You Need to Know About Oracle 12c IndexesSolarWinds
Indexes are important to consider for optimal performance in every Oracle database. However with each new release, there is an incredible amount of new features and/or changes which can impact how Oracle indexes function and are maintained. This often results in many applications running inefficiently. In this presentation, we will review current Oracle index structures/options and discuss how they work, when they should be used and how they should be maintained.
Power View: Analysis and Visualization for Your Application’s DataAndrew Brust
This document provides an overview and introduction to Power View, Microsoft's ad hoc reporting and data visualization tool. It discusses how Power View allows for analysis and exploration of data in a browser using Silverlight. The document outlines how to access and import data into Power View reports from various sources like SQL Server, Excel, and SharePoint lists. It also demonstrates Power View's abilities like filtering, advanced visualizations, and properties for customizing reports. Finally, it discusses Power View's relationship to SharePoint and potential future directions.
Panorama comparatif des outils de reporting et Dashboarding Microsoft : Excel, SSRS et Power View. Les plus de chacun de ces outils de reporting pour répondre à tous vos besoins.
Speakers : Stéphane Vivien (GFI Informatique), Michael Nokhamzon (GFI Informatique), Laurent Miltgen-Delinchamp (Cumulos)
This document discusses strategies for monitoring and improving the performance of SQL Server Analysis Services (SSAS) cubes. It recommends monitoring performance counters, dynamic management views, execution logs, and profiler traces to identify bottlenecks. Some quick solutions include warming the cache by running top queries, while more complex issues may require tuning aggregations, partitions, or MDX queries. The document provides demos of monitoring tools and techniques for analyzing processing logs to further optimize cube performance.
The document discusses new features in SQL Server Analysis Services (SSAS) "Denali" release including a new unified BI Semantic Model that brings together relational and multidimensional data models. It provides more flexibility and choices in building BI applications using either tabular or multidimensional approaches. Denali also improves performance and scalability with new in-memory and compression technologies. New tools are introduced for data modeling and management.
Session des Journées SQL Server 2014 - Patrice Harel
---
L’objectif sera de comparer les comportements entre les résultats des modèles AML et ceux de SSAS.
Solutions for Sage Customers from Robert LaverySuzanne Spear
The document discusses how Norming Software solutions can help secure assets and data in Sage 300 ERP (Accpac). It describes modules that can restrict user access and transactions for banks, customers, vendors, inventory items, locations, and more based on permission settings. Demo modules were shown to control access to sensitive data for banking, accounts receivable, accounts payable, inventory, purchasing, and sales within Accpac.
This is my presentation at SQLBits 8, Brighton, 9th April 2011. This session is about advanced dimensional modelling topics such as Fact Table Primary Key, Vertical Fact Tables, Aggregate Fact Tables, SCD Type 6, Snapshotting Transaction Fact Tables, 1 or 2 Dimensions, Dealing with Currency Rates, When to Snowflake, Dimensions with Multi Valued Attributes, Transaction-Level Dimensions, Very Large Dimensions, A Dimension With Only 1 Attribute, Rapidly Changing Dimensions, Banding Dimension Rows, Stamping Dimension Rows and Real Time Fact Table. Prerequisites: You need have a basic knowledge of dimensional modelling and relational database design.
My name is Vincent Rainardi. I am a data warehouse & BI architect. I wrote a book on SQL Server data warehousing & BI, as well as many articles on my blog, www.datawarehouse.org.uk. I welcome questions and discussions on data warehousing on vrainardi@gmail.com. Enjoy the presentation.
This document provides an overview of advanced dimensional modelling techniques. It discusses:
1) Dimension structures such as slowly changing dimension type 6, using one or two dimensions, and when to snowflake dimensions.
2) Fact table considerations like primary keys, snapshotting transaction fact tables, aggregate fact tables, and vertical fact tables.
3) Dimension behaviors like rapidly changing dimensions, very large dimensions, banding and stamping dimension rows, and dimensions with multi-valued attributes.
4) Combination techniques involving real-time fact tables, dealing with currency rates and status values. The document covers several sections and many modelling patterns in 44 slides.
The document discusses various topics related to web metrics and analytics tools. It provides an overview of factors to consider when choosing tools, such as organizational needs, costs, implementation challenges, and level of sophistication required. It also covers specific metrics like visitor acquisition, segmentation, time on site, click density and heat maps. The document emphasizes focusing on a handful of key performance indicators rather than all possible metrics, and approaches like optimizing the customer journey and lifecycle. Overall it provides guidance on setting up an effective analytics program.
This document discusses big data concepts and applications. It begins by defining big data characteristics including volume, velocity, and variety. It then outlines common big data applications in business intelligence and transactions. Different big data architectures like MapReduce, massively parallel processing databases, and in-memory databases are described along with their strengths and limitations. The document concludes with a demonstration of exploring millions of US patent pages in real-time using various big data technologies.
This document summarizes an event being held by #75PRESENTS on October 3rd 2018. The event includes three presentations on DynamoDB by PolarSeven, data protection on AWS using Commvault, and incident management with PagerDuty. There will be pizza and beer during a break between the first two presentations. The document provides details on each presentation including speakers and topics to be covered.
This document proposes a new approach called a temporal snapshot fact table to analyze insurance data daily over many decades in a space-efficient manner. Traditional solutions like transactional, accumulating, and periodic snapshot fact tables are not feasible due to the large volume of data. The new approach models each fact row as a time interval rather than a point in time. This reduces duplication and allows representing the data using temporal logic and operators. Technical challenges around data integration and cube modeling are addressed through refactoring the source data into a common set of time intervals and modeling the intervals relationally in the cube.
Microsoft SQL Server - How to Collaboratively Manage Excel DataMark Ginnebaugh
How to Collaboratively Manage Excel-Based Process Data in SQL Server
Your organization probably uses Excel for a variety of business processes including budgeting, sales revenue forecasting, product demand planning, and project management.
You'll learn how to set up and manage multi-user collaborative processes using Excel as the data form and SQL Server as the data store and process engine.
You'll learn:
* How to enable cell-level collaboration between multiple users using Excel and SQL Server.
* How to effectively integrate desktop Excel-based process data with enterprise applications.
* How to mitigate the limitations normally associated with Excel-to-database connections including record locking (check-in/out), conflict management, and change management and versioning.
This document discusses an agile approach to developing a data warehouse. It advocates using an Agile Enterprise Data Model to provide vision and guidance. The "Spock Approach" is described, which uses an operational data store, dimensional data warehouse, and iterative development of data marts. Data visualization techniques like data hexes are recommended to improve planning and visibility. Leadership, version control, adaptability, refinement, and refactoring are identified as important ongoing processes for an agile data warehouse project.
Building a highly scalable and available cloud applicationNoam Sheffer
This document discusses lessons learned from building large, scalable applications on Azure. It emphasizes designing for scale from the start by making applications stateless and partitioning data. It also stresses designing for failure since failures will occur at large scale. Other key lessons include optimizing for density to reduce costs, using telemetry to monitor applications, and handling transient and enduring failures through retries and failover. The presenter concludes by offering to share more detailed guidance and reusable patterns for building scalable Azure applications.
Database Virtualization: The Next Wave of Big Dataexponential-inc
Servers, Storage and Networking have all been virtualized, the next big wave is the database. SQL databases are the one thing in the cloud that require single dedicated instances. Database virtualization changes all of this, enabling full elasticity without sacrificing functionality.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
Netflix migrated to the cloud to avoid single points of failure and to focus on their core competencies. They chose Amazon Web Services and migrated non-sensitive data and applications to the cloud. Netflix picked SimpleDB and S3 as their data stores in the cloud. Migrating from an RDBMS required translating relational concepts like normalization to key-value stores and working around issues with SimpleDB like lack of data types and transactions.
Everything You Need to Know About Oracle 12c IndexesSolarWinds
Indexes are important to consider for optimal performance in every Oracle database. However with each new release, there is an incredible amount of new features and/or changes which can impact how Oracle indexes function and are maintained. This often results in many applications running inefficiently. In this presentation, we will review current Oracle index structures/options and discuss how they work, when they should be used and how they should be maintained.
Power View: Analysis and Visualization for Your Application’s DataAndrew Brust
This document provides an overview and introduction to Power View, Microsoft's ad hoc reporting and data visualization tool. It discusses how Power View allows for analysis and exploration of data in a browser using Silverlight. The document outlines how to access and import data into Power View reports from various sources like SQL Server, Excel, and SharePoint lists. It also demonstrates Power View's abilities like filtering, advanced visualizations, and properties for customizing reports. Finally, it discusses Power View's relationship to SharePoint and potential future directions.
Oracle 12.2 - My Favorite Top 5 New or Improved FeaturesSolarWinds
Oracle 12c (12.1) has been out for quite a while now, but it wasn’t until March of 2017 that Oracle 12.2 became available for the public to download. In this presentation we will deep dive into several of the new features that are considered some of my favorites. The participant will learn about enhancements to the In-Memory option, Multitentant (PDB) improvements, new Partitioning capabilities, the new Sharding feature and more.
BigQuery at AppsFlyer - past, present and futureNir Rubinstein
- The document discusses the past, present, and future of using BigQuery for mobile campaign analytics. In the past, they used small Python services and CouchDB which had performance issues. They started using BigQuery which improved performance but had some limitations and cost challenges. Through optimizations like unified schemas and table decorators, they addressed these issues. Going forward, they are waiting for custom partitioning functions in BigQuery to further improve performance and reduce costs.
Convergent Replicated Data Types in Riak 2.0Big Data Spain
Talk by Gordon Guthrie, Senior Software Engineer at Basho
Summary
A review of the CAP Theorem and the difficulties of resolving conflicts in highly distributed systems. Covering the issues and various theories on how to resolve including the use CRDTs in Riak
Details
CRDTs are used to replicate data across multiple computers in a network, executing updates without the need for remote synchronisation. This leads to merge conflicts in systems using conventional eventual consistency technology, but CRDTs are designed such that conflicts are mathematically impossible. Under the constraints of the CAP theorem they provide the strongest consistency guarantees for available/partition-tolerant (AP) settings.
The CRDT concept was first formally defined in 2007 by Marc Shapiro and Nuno Preguiça in terms of operation commutativity, and development was initially motivated by collaborative text editing. The concept of semilattice evolution of replicated states was first defined by Baquero and Moura in 1997, and development was initially motivated by mobile computing. The two concepts were later unified in 2011.
Basho has worked with the EU and Marc Shapiro's team to push CRDTs into distributed systems. Riak v2.x is the first commercial product to include this functionality
The majority of cloud-based DWH provides a wide range of migration tools from in-house DWH. However, I believe that cloud migration success is based not only on reducing infrastructure maintenance costs, but also on additional performance profit inherited from tailored data model.
I am going to prove that copying star or snowflake schemas as is will not lead to maximum performance boost in such DWH as Amazon Redshift and Google BigQuery. Moreover, this approach may cause additional cloud expenses.
We will discuss why data models should be different for each particular database, and how to get maximum performance from database peculiarities.
Most of performance tuning techniques for cloud-based DWH are about adding extra nodes to cluster, but it may lead to performance degradation in some cases, as well as extra costs burden. Sometimes, this approach allows to get maximum speed from current hardware configuration, may be even less expensive servers.
I will show some examples from production projects with extra performance using lower hardware, and edge cases like huge wide fact table with fully denormalized dimensions instead of classical star schema.
Similar to Biug 20112026 dimensional modeling and mdx best practices (20)
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
4. Microstrategy- BI •
DWH •
•
BI
SQL SERVER- SYBASE- •
•
DB P T • DB
OEM •
•
WEB SILVERLIGHT C •
•
.NET
5.
6. White Papers
• Analysis Services 2008 R2 Performance
Guide
• Analysis Services 2008 Operation
Guide
• Performance Improvements for MDX in
SQL Server 2008 Analysis Services
• OLAP Design Best Practices
7. 1 or 2 dimensions
a) One Dimension b) Two Dimensions
Dim Dim
Account Account
Fact Fact
Table Table
customer
Dim
attributes
Customer
• We can get the customer
• Simplicity, 1 dim
attributes without knowing the
• Hierarchy from customer
account key
attribute &account attribute
• Disadvantage: can‟t go from
• Use when we don‟t have fact
account to customer without
tables requiring customer grain.
going through the fact table -
performance
8. 1 or 2 dimensions
c) Snowflake
Dim Dim
Account Customer
Fact
Table • Dim customer is needed by another fact table
• Modular: 2 separate dim tables but we can combine
them easily to create a bigger dimension
• To get the breakdown of a measure by a customer
attribute is a bit more complicated than a)
select c. attribute, sum(f.measure1) from fact1 f
inner join dim_account a on f.account_key = a.account_key
inner join dim_customer c on a.customer_key = c.customer_key
group by c. attribute
9. When to Snowflake
1. When the sub dim is used by several dims
City-Country-Region columns exist in
DimBroker, DimPolicy, DimOffice and
DimInsured
Replaced by Location/GeoKey
pointing to DimLocation /
DimGeography
Advantage: consistent hierarchy, i.e. relationship between
City, Country & Region.
Weakness: we would lose flexibility. City to Country are
more or less fixed, but the grouping of countries might be
different between dimensions.
10. When to Snowflake
2. When the sub dim is used by both the main dim and
the fact table(s)
• DimCustomer is used in DimAccount,
and is also used in the fact table.
• DimManufacturer is used in DimProduct,
and is also used in the fact table.
• DimProductGroup is used in DimProduct,
and is also used in some fact table.
The alternative is maintaining two
full dimensions (star classic).
11. When to Snowflake
3. To make “base dim” and “detail dim”
Insurance classes, account types
(banking), product lines, diagnosis,
treatment (health care)
Policies for marine, aviation & property classes have different
attributes.
Pull common attributes into 1 dim: DimBasePolicy
Put class-specific attributes into DimMarine, DimProperty, DimAviation
Ref: Kimball DW Toolkit 2nd edition page 213
12. A dimension with only 1 attribute
Should we put the attribute in the fact table?
(like DD = Degenerate Dim)
Probably, if the grain = fact table,
and it‟s short or it‟s a number.
Reasons for putting single attribute in its own dim:
– Keep fact table slim (4 bytes int not 100 bytes varchar)
– When the value changes, we don‟t have to update the
BIG fact table – ETL performance
– Grain is much lower than fact table – small dim
– Yes it‟s only 1 attribute today, but in the future there
could be another attribute.
13. Fact Table Primary Key
Should we have a PK? Some experts totally disagree
Yes, if we need to be able to identify each fact row
1. Need to refer to a fact row from another fact row e.g. chain of events
2. Many identical fact rows and we need to update/delete only one
3. To link the fact table to another fact table
Related Trans Header - Detail Uniqueness
PK FK PK FK (no RI) PK
(not enforced)
previous/next transaction
14. Fact Table Primary Key
Single or Multi Column?
Single Column: Generated Identity
Multi Column: Dimension Keys
Single-column PK is better than multi-column PK because :
1) A multi-column PK may not be unique. A single-column PK
guarantees that the PK is unique, because it is an identity column.
2) A single-column PK is slimmer than a multi-column PK, better query
performance. To do a self join in the fact table (e.g. to link the current
fact row to the previous fact row), we join on a single integer column.
15. Fact Table Primary Key
• Advantage: Prevent duplicate rows, query performance
• Disadvantage: loading performance
• Indexing the PK: cluster or not?
– Cluster the PK if: the PK is an identity column
– Don‟t cluster the PK if: the PK is a composite, or when you need
the cluster index for query performance (with partitioning)
Example of not having a PK
If duplicate fact rows are allowed.
e.g. retail DW: Store Key, Date Key, Product Key, Customer Key
Same customer buying the same milk in the same shop on the same day
twice
16. Aggregate Fact Tables
What are they?
Base Fact Tables
• High level aggregation of base fact tables
• A “select group by” query on a 2 billion rows
fact table can take 30 mins if it joins with two
big fact tables, even with indexes in place
• So we do this query in advance as part of the
DW load and store it as an Aggregate Fact
Table 30 mins
• The report only takes 1 second to run.
Aggregate
1 sec Fact Table
Report
17. Rapidly Changing Dimension
• Why is it a problem
– Large SCD2 dim – Attributes change every day
– Slow query when join with large fact tables
• What to do
– Put into a separate dim, link direct to fact table.
– Just store the latest, type 1 attributes (or dual)
– Store in the fact table (for small attribute, e.g. indicator)
Type2 Type2 Type2
Type2 Type1
18. Very Large Dimension
Why is it a problem
– SSAS: 4 GB string store limit for dimension
– SSAS: dim is “select distinct” on each attribute
– long processing time
– Difficult to browse high cardinality attribute
– Join with fact tables – performance
19. Very Large Dimension
What to do
– Split into 2 dims, same grain. Always cut vertically.
– Remove SCD2, or at least only certain columns.
– Most common: separate the attributes with high cardinality/change
frequency
VLD
20. Real Time Fact Table
• Reporting the transaction system in real time
• View to union with the normal fact table, or use partitions
• Freezing the dims for key lookup, -3 unknown key
• Key corrections next day
Dims as of Main partition
yesterday (up to last night)
Unknown keys:
-1 null in source
-2 not in dim table Real time partition
-3 not in dim table as dim was frozen dim (intraday today)
to be resolved next batch key
21. Dealing with Currency Rates
What for/background/requirements
– Report in 3 reporting currencies, using today rates or past
– Analyse over time without the impact of currency rates (using fixed
currency rates, e.g. 2010 EOY rates)
– Had the transactions happened today
– Currency rates historical analysis
Transaction DW Reporting
Currency Transaction Currency Reporting Currency
Rates Rates
100 countries (many transaction 1 currency ( 1 reporting 3-4 currencies
40 currencies dates) e.g. GBP GBP, USD, EUR,
date)
Original
23. Dealing with Status
What/background
– Workflow (policies, contracts, documents)
– Bottleneck analysis (no of days between
stages)
– How many on each stage
Status Status Status Status
1 2 4 6
date1 date2 date3 date4
Status Status
3 5
24. Dealing with Status
Approaches
– Accumulative Snapshot Fact, 1 row per application
– SCD2 on DimApp AppKey AppID StsKey StsDate Current
1 1 1 1/3/11 N
– App Status fact table
2 1 2 3/3/11 N
3 1 3 7/3/11 Y
AppKey StsKey StsDateKey
4 2 1 6/3/11 N
1 1 61
5 2 2 7/3/11 Y
1 2 63
1 3 67
2 1 66
AppKey Sts1Date Sts1Ind Sts2Date Sts2Ind Sts3Date Sts3Ind
2 2 67
1 1/3/11 1 3/3/11 1 7/3/11 1
2 6/3/11 1 7/3/11 1 0
25. Referenced Dimensions
• Enables using one “master” member
• Not Snowflake dimension
– For ex.
• Dim customers: UK, London, Roman Avramovich.
• Dim Stores: UK, London, Friendly Bikes Store
– What is the total revenue from Internet
customers and stores in London?
26. MDX optimization Methodology
• Re-write the MDX code
• Add Aggregations
• Add pre-calculated Measure Groups (ETL)
• Solve the problem using Relational Engine
• Use .NET Store Procedures.
– Rarely the problem can be solved using better
hardware.
• Column based Databases
27. • Optimizing MDX
– Baselining Query Speeds
• Clearing the Analysis Services Caches
• Clearing the Operating System Caches using
fsutil.exe or SSAS Stored Proc (codeplex)
• Identifying and Resolving MDX Query
Performance Bottlenecks in SQL Server 2005
Analysis Services
• Configuring the Analysis Services Query Log
28. • Cell-by-Cell Mode vs. Subspace Mode
Almost always, performance obtained by
using subspace (or block computation)
mode is superior to that obtained by using
cell-by-cell (nor naïve) mode.
32. Granularity
• Single grain
– List of GROUP BY attributes in SQL SELECT
• Mixed grain
– Both Attribute.[All] and Attribute.MEMBERS
33. Granularity
All Countries, Countries,
Country, All City Cities
All City
All
Products
Products
34. Slice
• Single member
– SQL: Where City = „Redmond‟
– MDX: [City].[Redmond]
• Multiple members
– SQL: Where City IN („Redmond‟, „Seattle‟)
– MDX: { [City].[Redmond], [City].[Seattle] }
35. Slice at granularity
SQL
SELECT Sum(Sales), City FROM Sales_Table
WHERE City IN (‘Redmond’, ‘Seattle’)
GROUP BY City
MDX
SELECT Measures.Sales ON 0
, NON EMPTY {Redmond, Seattle} ON 1
FROM Sales_Cube
36. Slice below granularity
SQL
SELECT Sum(Sales) FROM Sales_Table
WHERE City IN (‘Redmond’, ‘Seattle’)
MDX
SELECT Measures.Sales ON 0
FROM Sales_Cube
WHERE {Redmond, Seattle}
37. Examples
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
38. Examples
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
(Seattle, Year.Year.MEMBERS)
39. Examples
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
(Seattle, Year.MEMBERS)
40. Examples
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
({Redmond, Seattle, London}, Year.MEMBERS)
41. Examples
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
({Redmond, Seattle}, {2005, 2006, 2007})
43. Arbitrary shaped subcubes
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
Lodnon
Union((Redmond, Year.Year.MEMBERS), (City.City.MEMBERS,
2005))
44. Arbitrary shaped subcubes
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
SF
Denver
CrossJoin(City.City.MEMBERS, Year.Year.MEMBERS) –
(Seattle, 2007)
45. Arbitrary shaped subcubes
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
{(Redmond,2005), (Seattle, 2006), (New York, 2007), (London,
2008)}
46. Arbitrary shaped subcubes
All Years 2005 2006 2007 2008
All Cities
Redmon
d
Seattle
New
York
London
Union(([All Cities], Year.MEMBERS), (City.MEMBERS, [All
Years]))
52. Leaves vs. Non Leaves
All Countries, Countries,
Country, All City Cities
All City
All
Product
s
Product Leaves
s
53. Problems with arbitrary shapes
• Caching
• Partition slices
• Indexes
• SCOPEs
• Matching calculations
• Many more
(for every topic we discuss – just ask “What will happen with arbitrary shapes”, and I am in trouble)
59. SSAS Denali
• Coming in the first half of 2012
• SSAS Tabular Mode
– Cheaper
– Not best of breed
– Uses DAX or MDX
• Have you started working with it?
60. Mobile BI
BI l
Smart Phone l
BI l
Mobile Bi
BI
Gartner
61. Social BI
• Discover New Insights - Analyze the
demographic and psychographic profiles
of your Facebook application users.
• Analyze Facebook Data - Analyze the full
spectrum of Facebook data: profiles,
interests, check-ins, and more
• Instantly Available via Cloud