Idea body scan

ingrediants


https://www.wired.com/story/naked-labs-3d-body-scanner/

From Blogger iPhone client



Smart mirrors and body-scanning devices are increasingly used to monitor weight and health-related metrics. These devices use various technologies like computer vision, AI, and sensors to provide detailed insights. Here are some types and examples:


1. Smart Mirrors with Integrated Body Scanning


These mirrors combine augmented reality and 3D scanning to analyze a person’s body shape, weight distribution, and other metrics.

• Features:

• Provide body composition analysis (e.g., BMI, muscle, fat percentages).

• Use cameras and depth sensors for 3D modeling.

• Suggest fitness programs or dietary changes.

• Examples:

• Naked Labs 3D Fitness Tracker: A smart mirror that scans your body and shows a 3D model of your progress.

• Styku: Used in gyms and clinics to measure body fat percentage and body shape changes.


2. Smart Scales with Companion Apps


These are enhanced weighing scales that sync data with mobile apps for detailed body metrics.

• Features:

• Measure weight, body fat, muscle mass, and water percentage.

• Use bioelectrical impedance analysis (BIA).

• Integrate with fitness and health apps.

• Examples:

• Withings Body+: Tracks body composition and weight trends.

• Eufy Smart Scale P2: Offers 16 body health measurements.


3. Wearable Tech with Body Scanning Features


Some wearables integrate with apps to provide body fat percentage, posture analysis, and more.

• Features:

• Combine accelerometers, gyroscopes, and AI algorithms.

• Analyze posture, weight trends, and caloric burn.

• Examples:

• Amazon Halo Band: Uses a 3D scanner for body fat percentage analysis.

• Garmin Index S2 Scale: Syncs weight and metrics with Garmin wearables.


4. Standalone 3D Body Scanners


High-end devices used in clinics, gyms, or wellness centers.

• Features:

• Provide accurate 3D models of body shape and dimensions.

• Detect weight imbalances or posture issues.

• Analyze progress over time with detailed charts.

• Examples:

• FIT3D ProScanner: Commonly used in fitness centers.

• Shapescale: A personal 3D body scanner for home use.


5. Augmented Reality (AR) Smart Mirrors


These mirrors focus more on visual feedback for weight loss, posture improvement, and fitness.

• Features:

• Overlay AR visuals on your body (e.g., virtual trainers).

• Monitor real-time posture during workouts.

• Suggest adjustments to routines or diet.

• Examples:

• Mirror by Lululemon: Offers fitness classes and virtual trainers.

• CareOS Poseidon: Combines beauty, fitness, and health monitoring.


6. Medical-Grade Smart Devices


Used in healthcare for detailed body composition and weight-related diagnoses.

• Features:

• Use advanced imaging like DEXA scans for body fat and bone density analysis.

• Provide detailed health insights beyond weight (e.g., visceral fat levels).

• Examples:

• InBody Scanner: Popular in clinics for comprehensive body composition analysis.

• GE Healthcare DEXA: Measures fat distribution and bone health.


Emerging Trends

• AI-Powered Virtual Assistants: Some devices use AI to provide personalized recommendations based on scans.

• Contactless Monitoring: Devices like Kinect-style cameras or LiDAR for full-body scans without wearable components.

• Integration with Smart Home Ecosystems: Sync health data with systems like Google Home or Amazon Alexa for holistic health management.


Let me know if you need help choosing one!



Integration between Planview and Azure DevOps

Integrating Adaptive Work with Azure DevOps (ADO) requires connecting the two systems so they can share information such as tasks, workflows, or project updates. Here’s a high-level guide to achieve this integration:


1. Understand the Integration Goals


Identify what you want to achieve with the integration:

• Sync tasks, work items, or statuses.

• Automate workflows between Adaptive Work and ADO.

• Provide visibility across both platforms.

• Consolidate reporting or analytics.


2. Integration Methods


Choose an integration approach based on your needs and technical capabilities:


a. Native Integration Tools (if available)


Check if Adaptive Work provides built-in support for Azure DevOps. Some platforms have connectors for ADO, which allow easy integration without custom development.


b. Use Third-Party Tools


Integration platforms like Zapier, Workato, or MuleSoft often support both Adaptive Work and ADO. These tools allow you to set up no-code or low-code workflows.


c. Develop a Custom Integration


If there’s no direct integration or third-party support, develop a custom solution using APIs.


3. Custom Integration Steps


a. Review API Documentation

• Adaptive Work API: Check its REST API or other programmatic interfaces.

• Azure DevOps API: Familiarize yourself with ADO’s REST API for accessing work items, projects, and pipelines.


b. Set Up Authentication

• Use OAuth or personal access tokens for API authentication.

• Ensure secure storage and access to these tokens.


c. Define Data Mapping


Map the fields between Adaptive Work and ADO, such as:

• Adaptive Work task → Azure DevOps work item.

• Status updates → State changes.

• Comments → Discussion threads.


d. Create a Synchronization Service


Build a middleware service (e.g., using Python, Node.js, or .NET) that:

1. Polls or listens to changes in Adaptive Work.

2. Calls the ADO API to update work items (or vice versa).

3. Handles bidirectional updates, if necessary.


e. Automate Triggered Workflows


For example:

• When a task is created in Adaptive Work, create a work item in ADO.

• When an ADO work item is updated, sync the status back to Adaptive Work.


f. Set Up Logging and Error Handling


Ensure you log synchronization activity and handle API errors gracefully.


4. Monitor and Maintain

• Periodically review the integration for performance and reliability.

• Update the integration when Adaptive Work or ADO APIs change.


Example with Azure Logic Apps


You can use Azure Logic Apps to integrate Adaptive Work and ADO:

1. Create a Logic App in Azure.

2. Use the Adaptive Work and ADO connectors or call their APIs directly.

3. Define workflows to automate sync operations.


Let me know if you’d like help with specific APIs, workflows, or examples!



From Blogger iPhone client

Useful books

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf




http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf



From Blogger iPhone client

Azure data lake architecture

Near real-time lakehouse data processing


Data-driven enterprises need to keep their back end and analytics systems in near real-time sync with customer-facing applications. The impact of transactions, updates, and changes must reflect accurately through end-to-end processes, related applications, and online transaction processing (OLTP) systems. The tolerable latency for changes in OLTP applications to reflect in the downstream systems that use the data might be just a few minutes.

This article describes an end-to-end solution for near real-time data processing to keep lakehouse data in sync. The solution uses Azure Event Hubs, Azure Synapse Analytics, and Azure Data Lake Storage for data processing and analytics.

Apache® and Apache Spark are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks.


Architecture

Download a Visio file of this architecture.


Dataflow



  1. Change data capture is a prerequisite for source systems to listen to changes. Debezium connectors can connect to different source systems and tap into changes as they happen. The connectors can capture changes and produce events from various relational database management systems (RDBMS). Installing a Debezium connector requires a Kafka connect system.

  2. The connectors extract change data and send the captured events to Azure Event Hubs. Event Hubs can receive large amounts of data from multiple sources.

  3. Event Hubs directly streams the data to Azure Synapse Analytics Spark pools, or can send the data to an Azure Data Lake Storage landing zone in raw format.

  4. Other batch data sources can use Azure Synapse pipelines to copy data to Data Lake Storage and make it available for processing. An end-to-end extract, transform, load (ETL) workflow might need to chain different steps or add dependencies between steps. Azure Synapse pipelines can orchestrate workflow dependencies within the overall processing framework.

  5. Azure Synapse Spark pools use fully supported Apache Spark structured streaming APIs to process data in the Spark Streaming framework. The data processing step incorporates data quality checks and high-level business rule validations.

  6. Data Lake Storage stores the validated data in the open Delta Lake format. Delta Lake provides atomicity, consistency, isolation, and durability (ACID) semantics and transactions, scalable metadata handling, and unified streaming and batch data processing for existing data lakes.
  7. Using indexes for query acceleration augments Delta with further performance enhancements. Data from the Data Lake Storage validated zone can also be a source for further advanced analytics and machine learning.
  8. Data from the Data Lake Storage validated zone, transformed and enriched with more rules into its final processed state, loads to a dedicated SQL pool for running large scale analytical queries.

  9. Power BI uses the data exposed through the dedicated SQL pool to build enterprise-grade dashboards and reports.

  10. You can also use captured raw data in the data lake store landing zone and validated data in the Delta format for:
  11. Further ad hoc and exploratory analysis through Azure Synapse SQL serverless pools.
  12. Machine learning through Azure Machine Learning.
  13. For some low-latency interfaces, data must be denormalized for single-digit server latencies. This usage scenario is mainly for API responses. This scenario queries documents in a NoSQL datastore such as Azure Cosmos DB for single-digit millisecond responses.

  14. The Azure Cosmos DB partitioning strategy might not lend itself to all query patterns. If that's the case, you can augment the solution by indexing the data that the APIs need to access with Azure Cognitive Search. Azure Cosmos DB and Cognitive Search can fulfill most scenarios that require low latency query responses.

Components


This solution uses the following Azure components:


  • Event Hubs is a managed, distributed ingestion service that can scale to ingest large amounts of data. With the Event Hubs subscriber-publisher mechanism, different applications can send messages to topics in Event Hubs, and downstream consumers can connect to and process messages. The capture feature can write messages to Data Lake Storage in Avro format as they arrive. This ability enables easy micro-batch processing and long-term retention scenarios. Event Hubs also offers a Kafka-compatible API and supports schema registry.

  • Data Lake Storage forms the storage subsystem that stores all data in raw and validated formats. Data Lake Storage can handle transactions at scale, and supports different file formats and sizes. Hierarchical namespaces help organize data into a familiar folder structure and support Portable Operating System Interface for Unix (POSIX) permissions. The Azure Blob Filesystem (ABFS) driver offers a Hadoop-compatible API.

  • Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. This solution uses the following features of the Azure Synapse Analytics ecosystem:
  • Azure Synapse Spark pools offer an on-demand Spark runtime that adds built-in performance enhancements to open-source Spark. Customers can configure flexible autoscale settings, submit jobs remotely through the Apache Livy endpoint, and use the Synapse Studio notebook interface for interactive experiences.

  • Azure Synapse SQL serverless poolsprovide an interface to query lakehouse data by using a familiar T-SQL syntax. There's no infrastructure to set up, and Azure Synapse workspace deployment automatically creates the endpoint. Azure Synapse SQL serverless pools enable basic discovery and exploration of data in place, and are a good option for user ad hoc query analysis.

  • Azure Synapse dedicated SQL poolsstore data in relational tables with columnar storage. Dedicated SQL pools use a scale-out architecture to distribute data processing across multiple nodes. PolyBase queries bring the data into SQL pool tables. The tables can connect to Power BI for analysis and reporting.
  • Power BI provides a visual interface to create and access reports and dashboards. Power BI Desktop can connect to various data sources, combine the sources into a data model, and build reports or dashboards. With Power BI, you can transform data based on business requirements, and share visuals and reports with others through the Power BI service.

  • Azure Cosmos DB is a managed, multi-modal NoSQL database that supports open APIs such as MongoDB and Cassandra. This solution uses Azure Cosmos DB for applications that require single-digit millisecond response times and high availability. Azure Cosmos DB offers multi-region writes across all Azure regions. You can use Azure Synapse Link for Azure Cosmos DB to derive insights and run analytics over data in real time.

  • Azure Cognitive Search is a cloud search service that can index the data your applications and APIs need. Cognitive Search has optional AI enrichment features that help with text extraction and infer text from non-text files. Cognitive Search integrates with services like Azure Data Lake Storage and Azure Cosmos DB to easily access and index data. You can query the indexed data by using a REST API or the .NET SDK. To get data from two separate indexes, you can combine them into a single index or use complex data types.

Scenario details

The end-to-end workflow to process changes in near real-time requires:


  • A change data capture (CDC) technology. The OLTP applications might have different back-end data stores, such as SQL Server, MySQL, and Oracle. The first step is to listen to changes as they happen, and propagate them forward.
  • An ingestion buffer to publish the change events at scale. This service should have the ability to handle large amounts of data as messages arrive. Individual subscribers can connect to this system and process the data.
  • Distributed and scalable storage for data as-is in a raw format.
  • A distributed, efficient stream processing system that lets users restart and manage state.
  • An analytics system that runs at scale to power business decisions.
  • A self-serve analytics interface.
  • For low-latency API responses, a NoSQL database to store denormalized representation of the data.
  • For some cases, a system to index data, refresh the index at regular intervals, and make the latest data available for downstream consumption.

All the preceding technologies should use relevant security constructs for perimeter security, authentication, authorization, and data encryption.


Potential use cases


This solution is well-suited for:


  • Industries that need to propagate changes from OLTP to online analytics processing (OLAP).
  • Applications that require data transformation or enrichment.

The real-time data processing scenario is especially important for financial services industries. For example, if an insurance, credit card, or bank customer makes a payment and then immediately contacts customer service, the customer support agent needs to have the latest information.

Similar scenarios apply to retail, commerce, and healthcare sectors. Enabling these scenarios streamlines operations, leading to greater organizational productivity and increased customer satisfaction.


Considerations

These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.


Reliability


Reliability ensures your application can meet the commitments you make to your customers. For more information, see Overview of the reliability pillar.


  • Event Hubs offers 90-day data retention on the Premium and Dedicated tiers. For failover scenarios, you can set up a secondary namespace in the paired region and activate it during failover.

  • Azure Synapse Spark pool jobs are recycled every seven days as nodes are taken down for maintenance. Consider this activity as you work through the service level agreements (SLAs) tied to the system. This limitation isn't an issue for many scenarios where recovery time objective (RTO) is around 15 minutes.

Cost optimization


Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational efficiencies. For more information, see Overview of the cost optimization pillar.


  • You can select from different Event Hubs tiers based on workload characteristics. Event Hubs bills Capture storage separately, based on the amount of data being stored on Data Lake Storage.

  • Consider object lifecycle management through tiers on Azure Data Lake Storage. As data ages, you can move data from a hot tier, where you need to access recent data for analytics, to a cold storage tier that is priced much lower. The cold storage tier is a cost-effective option for long-term retention.

  • You can pause the dedicated SQL pool when you're not using it in your development or test environments. You can schedule a script to pause the pool as needed, or you can pause the pool manually through the portal.

  • Azure Cosmos DB offers different provisioning models, such as serverless, manual provisioned throughput, and autoscale. Consider using serverless provisioning for your development and test workloads. You can also use autoscale, where you can set maximum request units (RUs) per second (RU/s) on the container. The throughput on the container scales automatically between 10% of maximum RU/s as a lower threshold and the maximum configured RU/s.

Performance efficiency


Performance efficiency is the ability of your workload to scale to meet the demands placed on it by users in an efficient manner. For more information, see Performance efficiency pillar overview.


  • You can scale Event Hubs through partitioning. Consider partitioning your data to preserve the order of events through a commit log. Partitioning lets you create multiple parallel logs by maximizing the available throughput capacity.

  • You can set up Azure Synapse Spark pools with small, medium, or large virtual machine (VM) SKUs, based on the workload. You can also configure autoscale on Azure Synapse Spark pools to account for spiky workloads. If you need more compute resources, the clusters automatically scale up to meet the demand, and scale down after processing is complete.

  • Use best practices for designing tables in the dedicated SQL pool. Associated performance and scalability limits apply, based on the tier that the SQL pool is running on.

  • Azure Cosmos DB uses partitions to scale containers, based on a partition key. All data based on a partition key forms a logical partition. Make sure to choose the correct partitioning strategy based on workload requirements. You can also use indexes for faster data retrieval.

Contributors

This article is maintained by Microsoft. It was originally written by the following contributors.

Principal author:


Other contributor:


To see non-public LinkedIn profiles, sign in to LinkedIn.


Next steps

Related resources


From Blogger iPhone client


https://learn.microsoft.com/en-us/azure/architecture/example-scenario/data/real-time-lakehouse-data-processing

Creating legal basement in Ontario

Creating a legal basement apartment in Ontario involves several key steps to ensure compliance with provincial regulations and local bylaws. Here’s an overview of the necessary paperwork and requirements:

1. Building Permit: Before beginning any construction, obtain a building permit from your local municipal building department. This permit ensures your project complies with the Ontario Building Code and local zoning bylaws. You’ll need to submit construction drawings and other required documents as part of your application.

2. Zoning Approval: Verify that your property is zoned to allow a secondary unit. Zoning bylaws vary by municipality and dictate whether a basement apartment is permissible on your property. Consult your local planning department to confirm compliance.

3. Fire Safety Compliance: Ensure the basement apartment meets fire safety standards, including proper fire separation between units, installation of interconnected smoke alarms and carbon monoxide detectors, and adequate means of egress. A fire inspection may be required to verify compliance.

4. Electrical Safety Authority (ESA) Inspection: If your project involves electrical work, an inspection by the ESA is necessary to ensure all electrical installations meet safety standards. It’s recommended to hire a licensed electrical contractor for this purpose.

5. Plumbing Permit: If your basement renovation includes plumbing work, such as adding a bathroom or kitchen, a plumbing permit is required. This ensures all plumbing installations comply with the Ontario Building Code.

6. Registration of Second Unit: Some municipalities require the registration of secondary units. For example, in Mississauga, all second units must be registered with the city and meet the Ontario Building Code, Ontario Fire Code, and Zoning By-law requirements. Check with your local municipality for specific registration requirements.


It’s advisable to consult with your local municipal building and planning departments to understand the specific requirements and processes in your area. Hiring qualified professionals, such as architects or licensed contractors, can also help ensure your basement apartment meets all legal and safety standards.



From Blogger iPhone client


costing


Designing a basement in Ontario involves several cost considerations, including the creation of design plans, obtaining necessary permits, and the actual construction. Here’s a breakdown of the potential expenses:

1. Design Plans: Hiring a professional designer or architect to create detailed plans is essential for ensuring compliance with building codes and meeting your specific needs. The cost for design services can vary based on the complexity of the project and the professional’s experience. On average, you might expect to pay between $1,500 and $5,000 for comprehensive design plans.

2. Building Permits: Securing the appropriate permits is a crucial step in the process. Permit fees can vary depending on the municipality and the scope of the project. In Ontario, building permit fees are often calculated based on the size and value of the construction. For a basement renovation, permit costs can range from $500 to $2,000.

3. Construction Costs: The actual construction costs for finishing a basement depend on factors such as size, design complexity, materials chosen, and additional features like bathrooms or kitchens. In Ontario, the average cost to finish a basement ranges from $30 to $80 per square foot. For example, finishing a 1,000 square foot basement could cost between $30,000 and $80,000.

4. Additional Features: Incorporating elements like bathrooms, kitchens, or custom finishes will increase the overall cost. Adding a bathroom can cost between $12,500 and $20,000, depending on the size and features.


It’s important to note that these figures are estimates, and actual costs can vary based on specific project requirements, local market conditions, and the professionals you hire. Consulting with local contractors and designers will provide a more accurate assessment tailored to your project’s needs.



Guelph Laws


To obtain approval for a basement design in Guelph, Ontario, especially when creating an additional residential dwelling unit (ARDU), you must adhere to specific requirements set by the City of Guelph. Here’s a comprehensive guide to help you navigate the process:


1. Zoning Regulations:

• Permissible Zones: ARDUs are permitted in R.1 (R.1A, R.1B, R.1C, R.1D), R.2, and R.3B residential zones. They are not allowed in cluster townhouses in R.3A zones or houses with more than two dwelling units (e.g., triplexes).

• Size Constraints:

• Within Main Building: The ARDU cannot exceed 45% of the gross floor area of the building. It can have a maximum of two bedrooms and may occupy the entire basement.

• In Accessory Buildings: The unit’s size is limited to 80 square meters (861 sq ft) and must not exceed 45% of the main building’s gross floor area. It can have up to two bedrooms and must adhere to specific height and setback requirements.


2. Building Permit Application:

• Submission: Applications are submitted electronically through the Guelph Permit and Application System. Ensure all drawings are in PDF format and drawn to a recognizable scale.

• Required Documents:

• Completed application form.

• Energy Efficiency Design Summary (SB-12).

• Comprehensive site plan or property survey.

• Complete set of construction drawings.

• Owner Authorization form (if applicable).

• Septic System Evaluation Form or separate building permit application for septic system (if applicable).


3. Registration and Fees:

• Registration: All ARDUs must be registered with the City as per the Additional Residential Dwelling Units Bylaw. This applies to both new and existing units.

• Fees:

• New Unit (Owner-Occupied): $160

• New Unit (Non-Owner-Occupied): $320

• Existing Unit (Owner-Occupied): $320

• Existing Unit (Non-Owner-Occupied): $640


4. Parking Requirements:

• Provide a minimum of one parking space for each ARDU.

• Ensure at least one legal, off-street parking space (minimum 3m x 6m) for the host unit.

• Exterior parking spaces must be at least 2.5m x 5.5m each.

• Parking spaces can be stacked behind the legal, off-street parking space.


5. Building Code Requirements:

• Room Sizes and Ceiling Heights: Adhere to the Ontario Building Code’s minimum requirements for room dimensions and ceiling heights.

• Fire Safety: Ensure proper fire separations between units, compliant with fire resistance ratings. Install interconnected smoke alarms and carbon monoxide detectors as required.

• Egress Windows: Bedrooms must have egress windows with an unobstructed opening of at least 0.38 sq m (4.1 sq ft), with no dimension less than 450 mm (18”). The window must be openable from the inside without tools or special knowledge.


6. Inspections:

• Electrical Safety: Obtain a report from a licensed electrical contractor confirming the absence of visible fire or shock hazards.

• Building Inspections: Schedule inspections at various construction stages to ensure compliance with approved plans and codes.


7. Additional Considerations:

• Property Taxes: The creation of an ARDU may affect property taxes. For specific inquiries, contact the Municipal Property Assessment Corporation (MPAC).

• Professional Assistance: Engaging with qualified professionals, such as architects or designers, is advisable to ensure all design and construction aspects meet the City’s requirements.


For detailed information and access to necessary forms, visit the City of Guelph’s official website:


Adhering to these guidelines will facilitate the approval of your basement design in Guelph, ensuring it meets all regulatory standards and provides a safe, legal living space.



companies in Guelph


Guelph, Ontario, hosts a diverse array of design service providers across various disciplines, including architectural design, interior design, graphic design, and web design. While an exact count of these companies is challenging due to the dynamic nature of the industry, several resources can help you identify and connect with local design professionals:

1. Architectural and Building Design Services:

• Houzz Directory: Features over 3,250 architects, architecture firms, and building designers serving the Guelph area.

• City of Guelph’s Qualified Designers List: Provides information on finding qualified designers and registered firms for building projects.

2. Interior Design Services:

• Yelp Listings: Showcases top-rated interior design firms in Guelph, offering services ranging from home staging to comprehensive interior design solutions.

3. Graphic Design Services:

• Designers of Guelph: A collective of local graphic designers, illustrators, and related professionals.

• Yelp Listings: Lists graphic design firms and freelancers in Guelph, providing services such as branding, print design, and digital graphics.

4. Web Design Services:

• helloDarwin Directory: Offers a selection of web design firms in Guelph, specializing in website creation, redesign, and enhancement.

• Massive Web Design: A Guelph-based company specializing in WordPress site development.


These resources can assist you in identifying and contacting design service providers in Guelph to suit your specific project needs.



registered companies in Guelph


If you’re seeking professional basement design and renovation services in Guelph, Ontario, here are five reputable companies to consider:


When selecting a company for your basement project, consider factors such as experience, customer reviews, design capabilities, and alignment with your specific needs and budget. It’s advisable to consult with multiple contractors to discuss your project requirements and obtain detailed quotes before making a decision.



Contracting in Canada

Data engineering contracting opportunities are abundant in Canada, with positions available across various industries and regions. Here are some current opportunities:


These positions offer competitive compensation, with hourly rates ranging from $40 to $80, depending on experience and specific role requirements.


To explore these opportunities further, consider visiting job platforms such as Indeed, Glassdoor, and Workopolis, where you can filter searches by contract type, location, and salary to find roles that align with your preferences and expertise.



From Blogger iPhone client

Acquiring data analytics from Windows store

Yes, you can pull data analytics for apps listed on the Microsoft Store (Windows Store), but it typically requires access to the Microsoft Partner Center, which provides analytics and reporting tools for app developers.


Steps to Access Data Analytics from the Windows Store:

1. Sign in to Microsoft Partner Center

• You need a developer account to log in. Visit Microsoft Partner Center.

2. Navigate to Analytics Section

• Go to the “Analytics” section in the Partner Center dashboard.

• Available reports include Acquisitions, Usage, Ratings and Reviews, Payouts, and more.

3. Download Reports

• Microsoft Partner Center allows you to download analytics data for further analysis.

• Reports can typically be exported as CSV files for use in tools like Excel or Power BI.

4. Use APIs for Automation

Microsoft provides APIs to programmatically retrieve analytics data.

• Microsoft Store Analytics API: Enables app developers to access data such as acquisitions, usage, add-on sales, and customer demographics.

• API Documentation: Store Analytics API


Key Insights You Can Access:

• App Installations and Uninstallations

• User Demographics

• Engagement Metrics (e.g., session durations, active devices)

• Ratings and Reviews

• In-App Purchases and Revenue


Additional Considerations:

• Permissions: Ensure you have the appropriate permissions to access the Partner Center and its analytics.

• Data Privacy: Comply with GDPR, CCPA, and other data privacy regulations when handling user data.


Would you like help setting up API access or analyzing the data?



From Blogger iPhone client

SAP ERP List of Finance tables

SAP ERP provides a variety of tables in its Finance (FI) module to manage and analyze financial transactions. These tables store data for general ledger (G/L), accounts payable (AP), accounts receivable (AR), asset accounting (AA), and controlling (CO). Below is a categorized list of important tables in the SAP FI module:


1. General Ledger (G/L) Accounting Tables


Table Name Description

BKPF Document Header Table (stores metadata for financial documents like date, type, user, etc.)

BSEG Document Line Items Table (stores transactional line items for G/L, AR, AP, etc.)

SKA1 G/L Account Master (Chart of Accounts)

SKB1 G/L Account Master (Company Code Level)

BSIS G/L Open Items

BSAS G/L Cleared Items


2. Accounts Payable (AP) Tables


Table Name Description

LFA1 Vendor Master (General Data)

LFB1 Vendor Master (Company Code Level)

LFC1 Vendor Transaction Figures

BSAK Cleared Vendor Items

BSIK Open Vendor Items

PAYR Payment Document Information


3. Accounts Receivable (AR) Tables


Table Name Description

KNA1 Customer Master (General Data)

KNB1 Customer Master (Company Code Level)

KNKK Customer Credit Management Data

DPSHP Customer Payment History

BSID Open Customer Items

BSAD Cleared Customer Items


4. Asset Accounting (AA) Tables


Table Name Description

ANLA Asset Master Record

ANLB Asset Master Record (Depreciation Areas)

ANLC Asset Values

ANEA Asset Line Items

ANEP Asset Line Items (Depreciation)

ANKA Asset Classes


5. Controlling (CO) Tables


Table Name Description

COEP Controlling Document Line Items

COSS Cost Totals for Cost Centers

CSKS Cost Center Master Data

CSKT Cost Center Descriptions

COSP Cost Totals for Internal Orders

AUFK Internal Orders Master Data


6. Financial Reporting Tables


Table Name Description

GLT0 G/L Account Balances

GLPCA Profit Center Accounting Line Items

FAGLFLEXA General Ledger (New) - Line Items

FAGLFLEXT General Ledger (New) - Totals

FAGLFLEXP General Ledger (New) - Planning


7. Tax Tables


Table Name Description

T001 Company Codes

T007A Tax Codes

T007S Tax Rates

BSET Tax Data Document Segment


8. Bank Accounting Tables


Table Name Description

BNKA Bank Master Data

FEBKO Bank Statement Header Data

FEBEP Bank Statement Line Items

PAYR Payment Data


9. Integration with Other Modules


Table Name Description

EKBE Goods Receipt/Invoice Receipt (MM Integration)

COBK Controlling Documents

RBKP Invoice Document Header (MM Integration)

BKPF Cross-Module Financial Document Header


Example Use Cases with Tables

1. View Open Customer Invoices:

• Tables: BSID, KNA1, KNB1

2. Analyze G/L Account Balances:

• Tables: GLT0, SKA1, SKB1

3. Track Vendor Payments:

• Tables: LFA1, BSIK, BSAK, PAYR

4. Asset Depreciation Analysis:

• Tables: ANLC, ANEP, ANEA

5. Cost Center Reporting:

• Tables: CSKS, CSKT, COSS


Let me know if you need specific queries, table relationships, or schema diagrams!



From Blogger iPhone client

SAP ERP Supply chain and logistics tables

SAP ERP contains a wide range of tables for managing Supply Chain Management (SCM) and Inventory Management (IM) processes. Here’s a categorized list of the most commonly used tables in these modules:


1. Supply Chain Management Tables


Master Data Tables

1. Material Master (MM):

• MARA: General data about materials.

• MARC: Plant-specific material data.

• MARD: Storage location-specific stock data.

• MAKT: Material descriptions.

• MBEW: Material valuation data.

2. Vendor Master:

• LFA1: General vendor master data.

• LFB1: Vendor data specific to company codes.

• LFM1: Vendor data for purchasing organizations.

3. Customer Master:

• KNA1: General customer master data.

• KNVV: Sales area-specific customer data.

• KNVP: Partner functions for customers.

4. Bill of Materials (BOM):

• STKO: BOM header.

• STPO: BOM items.

5. Work Centers:

• CRHD: Work center header.

• CRCO: Assignment of work centers to cost centers.


Transaction Tables

1. Sales and Operations Planning (SOP):

• PGMI: Planning data for SOP.

• PGAN: Assignments in SOP.

2. Production Planning (PP):

• AFKO: Order header data for production orders.

• AFPO: Order item data for production orders.

3. Demand Planning:

• PBIM: Independent requirements by material.

• PBED: Planned requirements for materials.

4. Purchasing:

• EKKO: Purchasing document header (e.g., purchase orders).

• EKPO: Purchasing document items.

5. Supply Chain Execution:

• LIPS: Delivery document items.

• LIKP: Delivery document headers.


2. Inventory Management Tables


Stock Management

1. Stocks and Quantities:

• MKPF: Material document header.

• MSEG: Material document items (e.g., goods movement).

• MBEWH: Historical stock valuation.

2. Warehouse Management:

• LAGP: Storage bins.

• LQUA: Quants (specific stock quantities in bins).

• LTAP: Transfer order items.

• LTAK: Transfer order headers.

3. Batch Management:

• MCHB: Batch stocks.

• MCHA: Batch data.

• MCH1: Batch data for materials.


Physical Inventory

1. Physical Inventory Documents:

• ISEG: Physical inventory document items.

• IKPF: Physical inventory document header.


Goods Movements

1. Movement Types:

• T156: Movement type definitions.

• T157D: Stock type changes during movement.

2. Goods Issue/Receipt:

• RESB: Reservation items for goods movements.

• RKPF: Reservation headers.


3. Integration and Reporting Tables


SCM Reporting and Analytics

1. Logistics Information System (LIS):

• S001 - S999: Various structures for logistics reporting.

• MCBE: Inventory data for material movements.

2. Inventory Analytics:

• MCWBE: Historical stock data for warehouse reporting.

• MVER: Material consumption data for MRP.


Integration with Other Modules

1. Integration with Finance (FI):

• BKPF: Accounting document header (for goods movements).

• BSEG: Accounting document items.

2. Integration with Sales (SD):

• VBAP: Sales order items.

• VBEP: Schedule lines for sales documents.


Common Queries and Use Cases

1. Track Inventory Levels by Plant:

• Tables: MARD (storage location stock), MBEW (valuation).

• Query: “What is the current stock quantity for material X in plant Y?”

2. Analyze Purchase Orders by Vendor:

• Tables: EKKO, EKPO.

• Query: “What are the total purchase orders placed with vendor Z in 2024?”

3. Monitor Goods Movements:

• Tables: MKPF, MSEG.

• Query: “List all goods receipts for material A in warehouse W.”

4. Warehouse Performance Reporting:

• Tables: LQUA, LTAP, MCBE.

• Query: “What is the total number of items moved between bins in Q4 2024?”


Need Further Assistance?


Would you like detailed table relationships, an ER diagram, or SQL queries for specific use cases in SCM or Inventory?



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Oracle EBS HR module dimensions and facts

In Oracle E-Business Suite (EBS), the Human Resource (HR) module uses various tables to store transactional and dimensional data for HR activities like employee records, payroll, benefits, and job positions. For reporting and analysis, these tables can be structured into fact and dimension tables in a data warehouse or BI environment.


Key Fact Tables for HR Module


Fact tables in the HR module primarily store measurable and transactional data.


Fact Table Description

HR_PAYROLL_FACT Stores payroll-related data, such as salaries, bonuses, and deductions.

HR_EMPLOYEE_ATTENDANCE Tracks attendance records like working hours, leaves, and overtime.

HR_TRAINING_FACT Captures training sessions attended by employees, completion rates, and scores.

HR_RECRUITMENT_FACT Contains recruitment metrics such as applications, interviews, and hires.

HR_BENEFITS_FACT Records benefits enrollment and usage details like health plans and retirement.

HR_PERFORMANCE_FACT Stores employee performance evaluation scores and appraisal metrics.


Key Dimension Tables for HR Module


Dimension tables provide descriptive data for analysis and join with fact tables via foreign keys.


Dimension Table Description

EMPLOYEE_DIM Stores employee details like name, department, job title, and hire date.

JOB_DIM Stores job roles, job categories, and pay grades.

DEPARTMENT_DIM Stores department names, IDs, and hierarchy.

TIME_DIM Stores time attributes (day, month, quarter, year) for analyzing trends.

LOCATION_DIM Stores office locations, regions, and countries.

PAYROLL_DIM Stores payroll period details like start date, end date, and frequency.

BENEFITS_PLAN_DIM Stores details about benefits plans like health, dental, and retirement plans.

TRAINING_DIM Stores training program details like course name, trainer, and duration.

RECRUITMENT_STAGE_DIM Tracks stages of recruitment like screening, interview, and onboarding.


Example Schema Design


Fact Table: HR_PAYROLL_FACT


Column Description

EMPLOYEE_ID Foreign key to EMPLOYEE_DIM.

PAYROLL_ID Foreign key to PAYROLL_DIM.

PAY_PERIOD Foreign key to TIME_DIM.

GROSS_PAY Total pay before deductions.

NET_PAY Pay after deductions.

DEDUCTIONS Amount deducted (taxes, benefits).


Dimension Table: EMPLOYEE_DIM


Column Description

EMPLOYEE_ID Unique identifier for employees.

EMPLOYEE_NAME Full name of the employee.

DEPARTMENT_ID Foreign key to DEPARTMENT_DIM.

JOB_ID Foreign key to JOB_DIM.

HIRE_DATE Employee’s hire date.


Dimension Table: TIME_DIM


Column Description

TIME_ID Unique identifier for time periods.

DATE Specific date.

MONTH Month name.

QUARTER Fiscal quarter.

YEAR Year.


Example Query


Question: What is the total payroll cost by department in Q4 2023?

SQL:


SELECT 

  d.DEPARTMENT_NAME,

  t.QUARTER,

  SUM(f.GROSS_PAY) AS TOTAL_PAYROLL

FROM 

  HR_PAYROLL_FACT f

JOIN 

  EMPLOYEE_DIM e ON f.EMPLOYEE_ID = e.EMPLOYEE_ID

JOIN 

  DEPARTMENT_DIM d ON e.DEPARTMENT_ID = d.DEPARTMENT_ID

JOIN 

  TIME_DIM t ON f.PAY_PERIOD = t.TIME_ID

WHERE 

  t.YEAR = 2023 AND t.QUARTER = 'Q4'

GROUP BY 

  d.DEPARTMENT_NAME, t.QUARTER;


ETL Process for HR Data

1. Extract: Pull data from Oracle EBS HR tables (PER_ALL_PEOPLE_F, PAY_PAYROLL_ACTIONS, PER_ALL_ASSIGNMENTS_F).

2. Transform:

• Map source columns to fact and dimension tables.

• Cleanse data (e.g., deduplicate employees, standardize job titles).

• Generate surrogate keys for dimensions.

3. Load: Populate fact and dimension tables in the data warehouse.


Benefits of Dimensional Modeling in HR

1. Performance: Simplifies complex queries for HR analytics.

2. Flexibility: Supports slicing and dicing data by time, department, or job role.

3. Scalability: Easy to add new metrics or dimensions, such as new benefits plans.


This approach transforms Oracle EBS HR data into actionable insights for strategic decision-making.



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Different Types of Dimensional Data Model

 There are several types of dimensional models, each suited to specific data warehouse and reporting needs. While the star schema is the most common implementation, other types of dimensional models exist to address varying requirements. Here are the main types:

1. Star Schema

  • Description: Central fact table connected to denormalized dimension tables.
  • Advantages:Simplified querying and high performance for analytical use cases.
  • Easy for business users to understand.
  • Limitations:Data redundancy in dimension tables can increase storage requirements.
  • Use Case: Best for straightforward analytics with a focus on performance.

2. Snowflake Schema

  • Description: A variation of the star schema where dimension tables are normalized into multiple related tables.
  • Advantages:Reduces redundancy in dimension tables, saving storage space.
  • Easier to maintain consistency across related data in dimensions.
  • Limitations:More complex queries due to the need for additional joins.
  • Slightly reduced query performance compared to a star schema.
  • Use Case: Suitable for scenarios where storage efficiency and consistency are prioritized over query simplicity.

3. Galaxy Schema (Fact Constellation)

  • Description: Combines multiple fact tables sharing common dimension tables.
  • Advantages:Supports complex business processes involving multiple fact tables.
  • Facilitates analysis across different data marts with shared dimensions.
  • Limitations:Complexity increases with the number of fact tables.
  • Requires careful design to avoid performance bottlenecks.
  • Use Case: Ideal for enterprise-scale data warehouses with multiple related subject areas (e.g., sales, inventory, and finance).

4. Data Vault Model

  • Description: A highly flexible and scalable approach that separates data into three components:Hubs: Represent core business entities (e.g., Customer, Product).
  • Links: Capture relationships between hubs (e.g., Customer-Product transactions).
  • Satellites: Store attributes and time-variant data for hubs and links.
  • Advantages:Excellent for handling historical and evolving data structures.
  • Scalable and suited for agile development.
  • Limitations:Not as intuitive for end-users as star or snowflake schemas.
  • Requires additional transformation steps for querying.
  • Use Case: Best for large-scale systems requiring flexibility and historical tracking.

5. Starflake Schema

  • Description: A hybrid approach combining elements of both star and snowflake schemas.
  • Advantages:Balances query performance with storage efficiency.
  • Combines denormalized and normalized tables for flexibility.
  • Limitations:Adds complexity to the schema design.
  • Use Case: Appropriate when some dimensions require normalization while others benefit from denormalization.

6. Core and Custom Dimensional Models

  • Description: Divides the dimensional model into a core model (common dimensions and facts) and custom extensions (specific to business units or regions).
  • Advantages:Supports global and localized reporting requirements.
  • Reusability of core components reduces duplication.
  • Limitations:Complexity in maintaining consistency between core and custom models.
  • Use Case: Useful for global organizations with diverse reporting needs.

7. Virtual Star Schema

  • Description: Logical schema created by views or virtualization, without physically materializing tables.
  • Advantages:Reduces storage needs by avoiding data duplication.
  • Flexible and easier to modify.
  • Limitations:Performance may depend on the underlying database engine and optimization.
  • Use Case: Suitable for scenarios where physical data warehouses are complemented by virtualized layers.

8. Bus Architecture

  • Description: Focuses on shared dimensions across multiple star or snowflake schemas, with a "bus matrix" to map dimensions and facts.
  • Advantages:Facilitates consistency across different subject areas.
  • Promotes reuse of dimensions across data marts.
  • Limitations:Requires strict governance to maintain shared dimensions.
  • Use Case: Best for large organizations consolidating data across multiple business domains.

9. Hybrid Dimensional Models

  • Description: Combines aspects of dimensional and relational models to meet specific business and technical requirements.
  • Advantages:Flexibility to address unique data warehouse challenges.
  • Tailored to balance performance and scalability.
  • Limitations:Custom implementations can be more complex to design and maintain.
  • Use Case: For organizations with highly customized data needs.

10. Aggregate Fact Tables

  • Description: Uses pre-aggregated data to optimize performance for specific queries (e.g., monthly sales summaries).
  • Advantages:Significantly faster query performance for aggregated metrics.
  • Reduces computational load on raw fact tables.
  • Limitations:Requires careful design to align with anticipated queries.
  • Increases storage requirements for pre-aggregated data.
  • Use Case: For dashboards and reports requiring quick insights on aggregated data.

Final Notes

The choice of dimensional model depends on:


  • Data volume: Larger datasets may favor normalized models for efficiency.
  • Query complexity: Simple star schemas are better for quick insights, while snowflake schemas handle more nuanced relationships.
  • Business needs: Aligning the model with reporting and analytics goals ensures maximum utility.


Popular dimensional modeling



Dimensional data modeling is a design technique for structuring data in a way that supports easy querying and analysis, typically in a data warehouse or data mart. The three main types of dimensional data models are Star Schema, Snowflake Schema, and Galaxy Schema. Here’s an end-to-end explanation with use cases for each:


1. Star Schema


Definition:

The Star Schema organizes data into a central fact table and connected dimension tables in a denormalized structure. Each dimension table contains descriptive attributes related to the fact table.


Use Case Example: Retail Sales Analysis

• Objective: Analyze sales performance across stores, products, and time periods.


Steps:

1. Fact Table:

• Name: Sales_Fact

• Columns: Date_Key, Store_Key, Product_Key, Sales_Amount, Quantity_Sold.

2. Dimension Tables:

• Date_Dim: Contains attributes like Date, Month, Year.

• Store_Dim: Contains Store_ID, Location, Manager.

• Product_Dim: Contains Product_ID, Category, Brand.

3. Schema Design:

The Sales_Fact table connects to each dimension table using foreign keys (e.g., Store_Key connects to Store_Dim).

4. Query Example:

“What were the total sales by store in Q4 of 2023?”

• Joins the Sales_Fact with Date_Dim and Store_Dim.


Advantages:

• Simple and intuitive for business users.

• Optimized for read-heavy queries.


2. Snowflake Schema


Definition:

A Snowflake Schema is a normalized version of the Star Schema where dimension tables are further split into sub-dimensions.


Use Case Example: Customer Relationship Analysis

• Objective: Analyze customer purchases by geographic region and product category.


Steps:

1. Fact Table:

• Name: Purchases_Fact

• Columns: Date_Key, Customer_Key, Product_Key, Purchase_Amount.

2. Dimension Tables:

• Customer_Dim: Contains Customer_ID, Region_Key.

• Region_Dim: Contains Region_ID, Country, Continent.

• Product_Dim: Contains Product_ID, Category_Key.

• Category_Dim: Contains Category_ID, Category_Name.

3. Schema Design:

Region_Dim normalizes location details, and Category_Dim organizes products into categories.

4. Query Example:

“What are the total purchases by region for Electronics in 2024?”

• Joins Purchases_Fact with Customer_Dim, Region_Dim, and Product_Dim.


Advantages:

• Reduced data redundancy.

• More scalable for complex dimensions.


3. Galaxy Schema


Definition:

The Galaxy Schema (or Fact Constellation) contains multiple fact tables that share dimension tables.


Use Case Example: Enterprise Resource Planning (ERP) Analysis

• Objective: Analyze sales and inventory data across the organization.


Steps:

1. Fact Tables:

• Sales_Fact: Tracks sales transactions.

• Inventory_Fact: Tracks stock levels.

2. Dimension Tables:

Shared dimensions: Product_Dim, Store_Dim, Date_Dim.

3. Schema Design:

Both Sales_Fact and Inventory_Fact use shared dimensions like Date and Product.

4. Query Example:

“What is the correlation between inventory levels and sales trends in Q4 2023?”

• Joins both fact tables with Date_Dim and Product_Dim.


Advantages:

• Supports complex analyses across multiple business processes.

• Centralizes dimensions for consistent data reporting.


End-to-End Process

1. Requirements Gathering:

• Understand business questions and metrics (e.g., “What are the monthly sales trends?”).

2. Source Data Identification:

• Identify transactional data sources like ERP systems or CRM databases.

3. Data Modeling:

• Choose a schema type (Star, Snowflake, or Galaxy).

• Design fact and dimension tables using tools like ERwin or Lucidchart.

4. ETL Process:

• Extract: Pull data from source systems.

• Transform: Clean and structure data for the chosen schema.

• Load: Populate the fact and dimension tables in the data warehouse.

5. Reporting:

• Use tools like Power BI, Tableau, or Looker for visualization and querying.

6. Performance Tuning:

• Index fact tables.

• Optimize joins and aggregations.


Key Differences Between Schemas:


Feature Star Schema Snowflake Schema Galaxy Schema

Normalization Denormalized Normalized Combination

Query Complexity Simplified Complex Complex

Data Redundancy Higher Lower Moderate

Use Case Simple Reporting Complex Dimensions Multi-Process Analysis




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Data models

What is Data Modeling?

Data modeling is the process of creating a visual representation of a system’s data and its relationships. It serves as a blueprint for how data is stored, accessed, and structured in a database or application, ensuring consistency, efficiency, and clarity in data management.


Example of Data Modeling


Consider an e-commerce system:

• Entities: Customers, Orders, Products.

• Attributes:

• Customer: ID, Name, Email.

• Order: Order ID, Order Date, Total Amount.

• Product: Product ID, Name, Price.

• Relationships:

• A Customer places multiple Orders.

• An Order contains multiple Products.


This information would be represented visually as a data model, showing how these entities connect.


Types of Data Modeling


There are three primary types of data modeling, each with specific use cases:


1. Conceptual Data Modeling

• Purpose: High-level overview of the system. Focuses on what data is stored, not how.

• Audience: Business stakeholders.

• Example:

• Entities: Customer, Product, Order.

• Relationships:

• A Customer can place multiple Orders.

• An Order includes multiple Products.


Diagram Example:


Customer ─── places ───> Order

Order ─── includes ───> Product


2. Logical Data Modeling

• Purpose: Defines the structure of the data in detail, including attributes and relationships, but without database-specific implementation details.

• Audience: Data architects and analysts.

• Example (for the e-commerce system):

• Entity: Customer

• Attributes: CustomerID (PK), Name, Email.

• Entity: Order

• Attributes: OrderID (PK), OrderDate, CustomerID (FK).

• Entity: Product

• Attributes: ProductID (PK), Name, Price.


Diagram Example (ERD - Entity Relationship Diagram):


Customer (CustomerID, Name, Email)

   |

   └───<places>───┐

          |

        Order (OrderID, OrderDate, CustomerID)

          |

          └───<includes>───>

              Product (ProductID, Name, Price)


3. Physical Data Modeling

• Purpose: Implementation-specific; focuses on how the data will be physically stored in a database.

• Audience: Database administrators and developers.

• Example (for a relational database):

• Table: Customer

• Columns: CustomerID (Primary Key), Name (VARCHAR), Email (VARCHAR).

• Table: Order

• Columns: OrderID (Primary Key), OrderDate (DATE), CustomerID (Foreign Key).

• Table: Product

• Columns: ProductID (Primary Key), Name (VARCHAR), Price (DECIMAL).


Diagram Example (SQL Schema):


CREATE TABLE Customer (

  CustomerID INT PRIMARY KEY,

  Name VARCHAR(50),

  Email VARCHAR(100)

);


CREATE TABLE Order (

  OrderID INT PRIMARY KEY,

  OrderDate DATE,

  CustomerID INT,

  FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID)

);


CREATE TABLE Product (

  ProductID INT PRIMARY KEY,

  Name VARCHAR(50),

  Price DECIMAL(10, 2)

);


Summary of Types


Type Focus Audience Example

Conceptual High-level entities and relationships Business stakeholders Customer places Orders with Products

Logical Attributes, relationships, no tech details Data architects/analysts ERD with attributes (CustomerID, etc.)

Physical Database implementation details DBAs/developers SQL tables and constraints





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