Data Warehouse approaches with Dynamics AXAlvin You
Dynamics AX의 BI 구축을 위해 필요한 Data Warehouse 내용입니다.
• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions
Data Warehouse approaches with Dynamics AXAlvin You
Dynamics AX의 BI 구축을 위해 필요한 Data Warehouse 내용입니다.
• What is a Data Warehouse
• Data Warehouse Approaches
• Why Invest in a Data Warehouse
• Getting Started
• BI Models
• BI Solutions
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
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.
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
This ppt includes an overview of
-OPS Data Mining method,
-mining incomplete servey data,
-automated decision systems,
-real-time data warehousing,
-KPIs,
-Six Sigma Strategy and its possible intergation with Lean approach,
-summary of my OLAP practice with Northwind data set (Access)
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
Watch video presentation and get a FREE performance management kit at
http://www.lifecycle-performance-pros.com
This presentation takes you through the steps of understanding your business intelligence needs and identifying the right tools for you. We discuss the different types of BI tools. We to discuss the criteria for selecting each type of tools. We to discuss popular Business Intelligence vendors and how to rate them. And we are going to discuss the job functions and responsibilities for a typical BI implementation
Data Lakehouse, Data Mesh, and Data Fabric (r2)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 modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
Erik Baardse and Ajit Gadge from EDB Postgres presented on how to transform your DBMS in order to drive digital business. How Postgres enables you to support a wider range of workloads with your relational database which opens the Big Data doors. They also cover EnterpriseDB’s Strategy around Big Data which focuses on 3 areas and finally last but not the last how to find money in IT with Big Data and digital transformation
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
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.
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
This ppt includes an overview of
-OPS Data Mining method,
-mining incomplete servey data,
-automated decision systems,
-real-time data warehousing,
-KPIs,
-Six Sigma Strategy and its possible intergation with Lean approach,
-summary of my OLAP practice with Northwind data set (Access)
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
Watch video presentation and get a FREE performance management kit at
http://www.lifecycle-performance-pros.com
This presentation takes you through the steps of understanding your business intelligence needs and identifying the right tools for you. We discuss the different types of BI tools. We to discuss the criteria for selecting each type of tools. We to discuss popular Business Intelligence vendors and how to rate them. And we are going to discuss the job functions and responsibilities for a typical BI implementation
Data Lakehouse, Data Mesh, and Data Fabric (r2)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 modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
Erik Baardse and Ajit Gadge from EDB Postgres presented on how to transform your DBMS in order to drive digital business. How Postgres enables you to support a wider range of workloads with your relational database which opens the Big Data doors. They also cover EnterpriseDB’s Strategy around Big Data which focuses on 3 areas and finally last but not the last how to find money in IT with Big Data and digital transformation
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. OLTP vs. Warehousing
• Organized by transactions vs. Organized by
particular subject
• More number of users vs. less
• Accesses few records vs. entire table
• Smaller database vs. Large database
• Normalised data structure vs. Unnormalized
• Continuous update vs. periodic update
4. Definition
• A datawarehouse is a subject-oriented,
integrated, time-variant and non-volatile
collection of data in support of
managements decision making process.
• It is the process whereby organizations
extract value from their informational assets
through use of special stores called data
warehouses
5. Types
• Operational Data Store: Operational data
mirror. Eg: Item in stock.
• Enterprise data warehouse: Historical
analysis, Complex pattern analysis.
• Data Marts
6. Uses of a datawarehouse
• Presentation of standard reports and graphs
• For dimensional analysis
• Data mining
7. Advantages
• Lowers cost of information access
• Improves customer responsiveness
• Identifies hidden business opportunities
• Strategic decision making
8. Roadmap to DataWarehousing
• Data extracted, transformed and cleaned
• Stored in a database - RDBMS, MDD
• Query and Reporting systems
• Executive Information System and Decision
Support System
9. Data Extraction and Load
• Find sources of data : Tables, files,
documents, commercial databases, emails,
Internet
• Bad data Quality: Same name but different
things, Different Units
• Tool to clean data - Apertus
• Tool to convert codes, aggregate and
calculate derived values - SAS
• Data Reengineering tools
10. Metadata
• Database that describes various aspects of
data in the warehouse
• Administrative Metadata: Source database
and contents, Transformations required,
History of Migrated data
• End User Metadata:
Definition of warehouse data
Descriptions of it
Consolidation Hierarchy
11. Storage
• Relational databases
• MDD
Measurements are numbers that quantify
the business process
Dimensions are attributes that describe
measurements
12. Information Analysis & Delivery
• Speed up retrieval using query optimizers
and bitmap indices
• Adhoc query - Simple query and analysis
functions
• Managed Query - Business layer between
end users and database
• Multidimensional - OLAP - support
complex analysis of dimensional data
13. Information Analysis & Delivery
• EIS/DSS
Packaged queries and reports
Preplanned analytical functions
Answer specific questions
• Alerts
Specific indicators
14. Managing the Data Warehouse
• Data - Size storage needs
Security
Backups
Tracking
• Process- Monitoring update process like
changes in source, quality of data
Accurate and upto date
15. Tools
• Data Extraction - SAS
• Data Cleaning - Apertus, Trillium
• Data Storage - ORACLE, SYBASE
• Optimizers - Advanced Parallel Optimizer
Bitmap Indices
Star Index
16. Tools
• Development tools to create applications
IBM Visualizer, ORACLE CDE
• Relational OLAP
Informix Metacube
17. Architecture
• Rehosting Mainframe Applications
Moving to lower cost microprocessors
Tools - Micro Focus COBOL
Lowers Cost
No transparent Access to data
18. Architecture
• Mainframe as server
2-tier approach
Front end client & back end server
Power Builder, VB - Front end tools
Minimal investment in extra hardware
Data inconsistency hidden
Fat Client
Cannot be used if number of end users
increase
19. Architecture
• Enterprise Information Architecture
3 tier
Source data on host computer
Database servers like ORACLE,
Essbase(MDD)
Front-end tools - DSS/EIS
20. RDBMS
• RDBMS provide rapid response to queries
Bitmap index
Index structures
• Functionality added to conventional
RDBMS like data extraction and replication
21. MDD
• Decision support environment
• Supports iterative queries
• Extensions to SQL - for high performance
data warehousing
• Performance degrades as size increases
• Inability to incrementally load
• Loading is slow
• No agreed upon model
22. MDD
• No standard access method like SQL
• Minor changes require complete
reorganization
23. Data Access Tools
• Simple relational query tools - Esperent
• DSS/EIS - EXPRESS used by financial
specialists
25. Star Schema
• Consists of a group of tables that describe
the dimensions of the business arranged
logically around a huge central table that
contains all the accumulated facts and
figures of the business.
• The smaller, outer tables are points of the
star, the larger table the center from which
the points radiate.
26. Star Schema
• Fact Table
-Sales, Orders, Budget, Shipment
Real values (numeric)
• Dimension Table
-Period, Market, Product
Character data
• Summary/Aggregate data
27. Star Schema
• Data you can trust
Referrential Integrity
• Query Speed
Fact table - Primary key
Dimension table - all columns
Query optimizer which understands star
schema
28. Star Schema
• Load Processing
Must be done offline
Issue if aggregate data is stored
29. Variations of Star Schema
• Outboard tables
• Fact table families
• Multistar fact table
30. OLAP
• Front end tool for MDD
• Slice Report
• Pivot Report
• Alert-reporting
• Time-based
• Exception reporting
31. Wide OLAP
• Generating (synthesizing) information as
well as using it, and storing this additional
information by updating the data source
• Modeling capabilities, including a
calculation engine for deriving results and
creating aggregations, consolidations and
complex calculations
• Forecasting, trend analysis, optimization,
statistical analysis
32. Relational OLAP
• Has a powerful SQL-generator
• Generates SQL optimized for the target
database
• Rapidly changing dimensions
35. Uses of Metadata
• Map source system data to data warehouse
tables
• Generate data extract, transform, and load
procedures for import jobs
• Help users discover what data are in the
data warehouse
• Help users structure queries to access data
they need
36. Describing the data warehouse
• I/P - O/P object
File/Table
Archive Period
• Relationship
• Data element - Name, Defn., Type
• Relationship Member - Role, Participation
Constraint
• Field Assignment
39. Planning
• Interviews
• Data quality
• Data Access
• Timeliness and history
• Data sources
• Decide on Architecture
40. Development Process
• Project Initiation
• Develop Enterprise Info. Architecture
• Design Data Warehouse Database
• Transform data
• Manage Metadata
• Develop User-Interface
• Manage Production
41. Evolution
• Support the current DW baseline
• Enhance current baseline capabilities
• Define new business requirements
• Implement new baseline
42. Mistakes
• Starting with the wrong sponsorship chain
• Setting expectations that cannot be met
• Believing that DW design is the same as
Transactional Database Design
• Believing the Performance, Capacity
Promises
• Believing that Once the Data Warehouse Is
Up and Running Problems are finished
43. • NSWCDD - ORACLE on UNIX
• Harris Semiconductor
IYM with Alarms, INGRES