The document provides information on skills needed to be a database professional. It lists logical data modeling, translating logical models into real database systems, special design challenges like security and access, normalization from 1NF to 5NF, and tools for data modeling like ER-Studio and ER-Win as important skills. It also discusses star schemas and snowflake schemas for data warehousing, with star schemas being better for performance in most cases.
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra
Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
Data Warehousing Trends, Best Practices, and Future OutlookJames Serra
Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn:
- Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart
- Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon
- Step by step approach to building an effective data warehouse architecture
- Common reasons for the failure of data warehouse implementations and how to avoid them
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
White Paper - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
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.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
This is a presentation I gave in 2006 for Bill Inmon. The presentation covers Data Vault and how it integrates with Bill Inmon's DW2.0 vision. This is focused on the business intelligence side of the house.
IF you want to use these slides, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
When Facts and Dimensions Alone Aren't the Answer: Logically Reversing the St...Perficient, Inc.
What is Reverse Star Schema?
Why and when would I use a Reverse Star Schema?
How would I implement a Reverse Star Schema?
What about data integrity?
In this slideshare, we'll walk through a real life implementation within the healthcare industry.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
White Paper - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
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.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
This is a presentation I gave in 2006 for Bill Inmon. The presentation covers Data Vault and how it integrates with Bill Inmon's DW2.0 vision. This is focused on the business intelligence side of the house.
IF you want to use these slides, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Warehouse:
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.
Reconciled data: detailed, current data intended to be the single, authoritative source for all decision support.
Extraction:
The Extract step covers the data extraction from the source system and makes it accessible for further processing. The main objective of the extract step is to retrieve all the required data from the source system with as little resources as possible.
Data Transformation:
Data transformation is the component of data reconcilation that converts data from the format of the source operational systems to the format of enterprise data warehouse.
Data Loading:
During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible. The target of the Load process is often a database. In order to make the load process efficient, it is helpful to disable any constraints and indexes before the load and enable them back only after the load completes. The referential integrity needs to be maintained by ETL tool to ensure consistency.
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
When Facts and Dimensions Alone Aren't the Answer: Logically Reversing the St...Perficient, Inc.
What is Reverse Star Schema?
Why and when would I use a Reverse Star Schema?
How would I implement a Reverse Star Schema?
What about data integrity?
In this slideshare, we'll walk through a real life implementation within the healthcare industry.
Logical Data Warehouse and Data Lakes can play a role in many different type of projects and, in this presentation, we will look at some of the most common patterns and use cases. Learn about analytical and big data patterns as well as performance considerations. Example implementations will be discussed for each pattern.
- Architectural patterns for logical data warehouse and data lakes.
- Performance considerations.
- Customer use cases and demo.
This presentation is part of the Denodo Educational Seminar, and you can watch the video here goo.gl/vycYmZ.
The Data Warehouse (DW) is considered as a collection of integrated, detailed, historical data, collected from different sources . DW is used to collect data designed to support management decision making. There are so many approaches in designing a data warehouse both in conceptual and logical design phases. The conceptual design approaches are dimensional fact model, multidimensional E/R model, starER model and object-oriented multidimensional model. And the logical design approaches are flat schema, star schema, fact constellation schema, galaxy schema and snowflake schema. In this paper we have focused on comparison of Dimensional Modelling AND E-R modelling in the Data Warehouse. Dimensional Modelling (DM) is most popular technique in data warehousing. In DM a model of tables and relations is used to optimize decision support query performance in relational databases. And conventional E-R models are used to remove redundancy in the data model, facilitate retrieval of individual records having certain critical identifiers, and optimize On-line Transaction Processing (OLTP) performance.
Best Practices for Building a Warehouse QuicklyWhereScape
Key factors that influence a successful data warehouse task are:
+ Implementing the True Development Approach
+ Choosing a Rapid Development Product
+ Ensuring Data Availability
+ Involving Key Users throughout the whole project
+ Relying on a Pragmatic Governance Framework
+ Utilizing experienced Team Members
+ Selecting the right Hardware, Infrastructure Technology
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data HubsCloudera, Inc.
Dimensional modeling and the star schema are some of the most important ideas in the history of analytics and data management. They provided a common language and set of patterns that allowed a broad class of users to analyze business processes and spawned an entire ecosystem. With the rise of enterprise data hubs that allow us to combine ETL, search, SQL, and machine learning in a single platform, we need to extend the principles of dimensional modeling to support new and diverse analytical workloads and users. We'll illustrate these concepts by walking through the design of a customer-centric data hub that uses all of the components of an EDH to enable everyone to understand the way that customers experience a company.
Presenter:
Josh Wills, Senior Director Data Science
Updated: October 6, 2014
Learn more about ER/Studio Data Architect and try it free at: http://embt.co/ERStudioDA
With round-trip database support, data architects using ER/Studio Data Architect have the power to easily reverse-engineer, compare and merge, and visually document data assets residing in diverse locations from data centers to mobile platforms. Enterprise data can be more effectively leveraged as a corporate asset, while compliance is supported for business standards and mandatory regulations -- essential factors in an organizational data governance program. A range of data sources are supported ranging from those residing on the cloud to data sources residing on mobile phones. A variety of database platforms, including traditional RDBMS and big data technologies such as MongoDB and Hadoop Hive, can be imported and integrated into shared models and metadata definitions.
DB Optimizer Datasheet - Automated SQL Profiling & Tuning for Optimized Perfo...Embarcadero Technologies
Learn more about DB Optimizer and try it free at: http://embt.co/DBOptimizer
Embarcadero® DB Optimizer™ XE6 is an automated SQL optimization tool that maximizes database and application performance by quickly discovering, diagnosing, and optimizing poor-performing SQL code. DB Optimizer empowers DBAs and database developers to eliminate performance bottlenecks by graphically profiling key metrics inside the database, relating resource utilization to specific queries, and helping to visually tune problematic SQL.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Oracle Certified Professional (OCP) and Tuning expert having 13 yr’s experience on medium to large-scale global project in Media and intertainment, Finance, Telecom and Insurance domain.
Migrating from CA AllFusionTM ERwin® Data Modeler to ER/StudioMichael Findling
This is a step-by-step guide to migrating from CA AllFusionTM ERwin Data Modeler to Embarcadero ER/Studio - the next-generation data modeling solutions. Embarcadero Technologies is the leading provider of database tools and developer software.
“A broad category of applications and technologies for gathering, storing, analyzing, sharing and providing access to data to help enterprise users make better business decisions” -Gartner
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
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.
3. MUST WATCH:PREREQUISITE
In Bengali, Fundamentals of Database
Management Systems
In English, Fundamentals of Database
Management Systems
4. LOGICAL DATA MODELING
Logical Data Modeling: Logical Database Design Steps: RDBMS
http://salearningschool.com/displayArticle.php?table=Articles&articleID=773
Logical Data Modeling
Identify major entities
Det ermine relationships between entities
Determine primary and alternate keys
Determine foreign keys
Determine key business rules
Add remaining attributes
Validate user views through normalization
Determine domains
Determine triggering operations
Combine user views
Integrate with existing data models
Analyze for stability and growth
5. LOGICAL MODEL INTO THE REAL DATABASE SYSTEM IDENTIFY TABLES
Translate Logical Model into the Real Database System
Identify tables
Identify columns
Adapt data structure to product environment
Design for business rules about entities
Design for business rules about relationships
Design for additional business rules about attributes
Tune for scan efficiency
Define clustering sequences
Define hash keys
Add indexes
Add duplicate data
Redefine columns
Redefine tables
6. SPECIAL DESIGN CHALLENGES
Design for Special Design Challenges
Provide for access through views
Establish security
Cope with very large databases
Access and accommodate change
Anticipate relational technology evolution
7. 3-NF NORMALIZATIONS
http://en.wikipedia.org/wiki/Third_normal_for
m
Boyce/Codd and Fourth Normal Form
http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=640
Normalization in Relational DBMS
Systems
http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=639
8. NORMALIZATION (1NF TO 5TH NF)
Normalization (1NF to 5th NF)
http://salearningschool.com/displayArticle.php?ta
ble=Articles&articleID=600
10. EXAMPLES OF DATA MODELS
Must Watch: Understanding Models
http://www.learndatamodeling.com/cdm.php#.Ui
KHVz_OCys
11. TOOLS THAT YOU SHOULD LEARN
Tools that You Should Learn
Just learn them
If you are good with DBMS theories, they will
not be difficult, you can do it mostly on your
own
14. ER/STUDIO DATA ARCHITECT
Universal Mappings Map between and within
conceptual, logical and physical model objects to
view upstream or downstream "Where Used"
Analysis Display mapping between conceptual and
logical models and their implementations across
physical designs Visual Data Lineage Visually
document source/target mapping and sourcing rules
for data movement across systems Round-trip
Database Support Round-trip database support for
forward and reverse engineering Advanced Compare
and Merge Enable advanced, bidirectional
comparisons and merges of model and database
structures
16. ER/STUDIO PORTAL
Structured Browsing & Navigation Provide a
web-based navigation of the repository
diagrams Technical Reports Pre-installed for
implementation details such as data types,
column width, column names, how objects are
related, data lineage between models and
security classification information Automatic
Data Synchronization ER/Studio diagrams and
objects are synchronized to the Portal on an
administrator controlled schedule. Advanced
Searching Wildcard searching with the ability to
limit the search to specific object types
18. ER/STUDIO REPOSITORY
Concurrent Model and Object Access Allows real-time
collaboration between modelers working on data models
down to the model object level Reviewing Changes and
Resolving User Conflict Conflict resolution through simple
and intelligent interfaces to walk users through the
discovery of differences Version Management Manages
the individual histories of models and model objects to
ensure incremental comparison between, and rollback to,
desired diagrams Component Sharing and Reuse Pre-
defined Enterprise Data Dictionary that eliminates data
redundancy and enforces data element standards
Security Center Groups Streamline security
administration with local or LDAP groups improving
productivity and reducing errors
19. ER/STUDIO BUSINESS ARCHITECTS
Skip this
Conceptual Model Creation Supports high-
level conceptual modeling using elements
such as subject areas, business entities,
interactions, and relationships Process
Model Creation Support for straightforward
process modeling that uses standard
elements such as sequences, tasks, swim
lanes, start events, and gateways
20. ER/STUDIO SOFTWARE ARCHITECT
Skip this
Model Driven Architecture & Standards
Supports Unified Modeling
LanguageTM(UML® 2.0 ), XML Metadata
Interchange (XMI® ), Query/
Views/Transformations (QVT) and Object
Constraint Language (OCL) Model Patterns
Powerful re-use facilities to jumpstart
projects through predefined patterns.
21. ER-WIN
http://en.wikipedia.org/wiki/CA_ERwin_Data_Modeler
Logical Data Modeling: Purely logical models may be created, from which physical models may
be derived. Combinations of logical and physical models are also supported. Supports entity-
type and attribute logical names and descriptions, logical domains and data types, as well as
relationship naming.
Physical Data Modeling: Purely physical models may be created as well as combinations of
logical and physical models. Supports the naming and description of tables and columns, user
defined data types, primary keys, foreign keys, alternative keys and the naming and definition of
constraints. Support for indexes, views, stored procedures and triggers is also included.
Logical-to-Physical Transformation: Includes an abbreviation/naming dictionary called "Naming
Standards Editor" and a logical-to-RDBMS data type mapping facility called "Datatype Standards
Editor", both of which are customizable with entries and basic rule enforcement.
Forward engineering: Once the database designer is satisfied with the physical model, the tool
can automatically generate a SQL Data Definition Language (DDL) script that can either be
directly executed on the RDBMS environment or saved to a file.
Reverse engineering: If an analyst needs to examine and understand an existing data structure,
ERwin will depict the physical database objects in an ERwin model file.
Model-to-model comparison: The "Complete/Compare" facility allows an analyst or designer to
view the differences between two model files (including real-time reverse-engineered files), for
instance to understand changes between two versions of a model.
An "Undo" feature is available in version 7.
22. POWER-DESIGNER
http://en.wikipedia.org/wiki/PowerDesigner
PowerDesigner includes support for:
Business Process Modeling (ProcessAnalyst) supporting BPMN
Code generation (Java, C#, VB .NET, Hibernate, EJB3, NHibernate, JSF,
WinForm (.NET and .NET CF), PowerBuilder, ...)
Data modeling (works with most major RDBMS systems)
Data Warehouse Modeling (WarehouseArchitect)
Eclipse plugin
Object modeling (UML 2.0 diagrams)
Report generation
Supports Simul8 to add simulation functions to the BPM module to enhance
business processes design.
Repository
Requirements analysis
XML Modeling supporting XML Schema and DTD standards
Visual Studio 2005 / 2008 addin
27. DATAWAREHOUSE VS OLTP
In School, you may study a bit on Datawarehouse
However, you may not learn that though there are very few opportunities but
the successful professional are highly paid
29. STAR AND SNOWFLAKE SCHEMAS
http://www.oracle.com/webfolder/technetwork
/tutorials/obe/db/10g/r2/owb/owb10gr2_gs/o
wb/lesson3/starandsnowflake.htm
Star and Snowflake Schemas
In relational implementation, the dimensional
designs are mapped to a relational set of tables.
You can implement the design into following two
methods:
Star Schema
Snowflake Schema
30. STAR SCHEMA
What Is a Star Schema?
A star schema model can be depicted as a simple star: a
central table contains fact data and multiple tables radiate
out from it, connected by the primary and foreign keys of
the database. In a star schema implementation,
Warehouse Builder stores the dimension data in a single
table or view for all the dimension levels.
For example, if you implement the Product dimension
using a star schema, Warehouse Builder uses a single
table to implement all the levels in the dimension, as
shown in the screenshot. The attributes in all the levels
are mapped to different columns in a single table called
PRODUCT.
32. WHAT IS A SNOWFLAKE SCHEMA?
What Is a Snowflake Schema?
The snowflake schema represents a dimensional
model which is also composed of a central fact table
and a set of constituent dimension tables which are
further normalized into sub-dimension tables. In a
snowflake schema implementation, Warehouse
Builder uses more than one table or view to store the
dimension data. Separate database tables or views
store data pertaining to each level in the dimension.
The screenshot displays the snowflake
implementation of the Product dimension. Each level
in the dimension is mapped to a different table.
34. WHEN TO USE STAR/SNOW-FLAKE SCHEMAS
Ralph Kimball recommends that in most of the other cases, star
schemas are a better solution. Although redundancy is reduced in
a normalized snowflake, more joins are required. Kimball usually
advises that it is not a good idea to expose end users to a physical
snowflake design, because it almost always compromises
understandability and performance.
35. WHEN DO YOU USE SNOWFLAKE SCHEMA IMPLEMENTATION?
When do you use Snowflake Schema Implementation?
Ralph Kimball, the data warehousing guru, proposes three cases where
snowflake implementation is not only acceptable but is also the key to a
successful design:
Large customer dimensions where, for example, 80 percent of the fact table
measurements involve anonymous
visitors about whom you collect little detail, and 20 percent involve reliably
registered customers about
whom you collect much detailed data by tracking many dimensions
Financial product dimensions for banks, brokerage houses, and insurance
companies, because each of
the individual products has a host of special attributes not shared by other
products
Multienterprise calendar dimensions because each organization has
idiosyncratic fiscal periods,
seasons, and holidays