Saving Fuel using Jet Stream

A jet stream is a fast-moving, narrow band of strong winds in the upper atmosphere, typically found at altitudes of 9–16 km (30,000–52,000 feet). These winds flow from west to east and can reach speeds of over 200 mph (320 km/h).

Key Features of Jet Streams:

1. Location: Found in the tropopause, the boundary between the troposphere (weather layer) and stratosphere.

2. Formation: Created by differences in temperature between warm and cold air masses.

3. Types:

• Polar Jet Streams (stronger, near 60° latitude)

• Subtropical Jet Streams (weaker, near 30° latitude)

4. Effect on Weather: They influence storms, weather patterns, and aviation. Planes use them for faster travel eastward and avoid them when flying westward.

Would you like to know more about their impact on climate and weather forecasting?

Impact of Jet Streams on Climate and Weather Forecasting

Jet streams play a crucial role in shaping global weather patterns and long-term climate trends. Their position and strength influence storms, temperature shifts, and precipitation patterns worldwide.

1. Impact on Weather

a) Storms and Extreme Weather

• Steering Weather Systems: Jet streams guide storms, moving low and high-pressure systems across continents.

• Intensifying Storms: When a jet stream strengthens, it can fuel powerful storms, such as hurricanes, blizzards, and severe thunderstorms.

• Blocking Patterns: If a jet stream slows or becomes stationary, it can cause prolonged droughts, heatwaves, or flooding.

b) Temperature Variations

• When the polar jet stream dips southward, it brings cold Arctic air, leading to winter storms and extreme cold spells.

• When it shifts northward, warm tropical air moves in, causing heatwaves and unseasonably warm weather.

c) Aviation and Air Travel

• Planes flying eastward can use jet streams to save fuel and reduce flight time.

• Turbulence increases when crossing a strong jet stream, affecting flight safety and comfort.

2. Impact on Climate

a) Climate Change and Jet Streams

• As the Arctic warms faster than the rest of the planet, the temperature contrast between polar and tropical regions weakens.

• This causes the polar jet stream to slow down and become wavier, leading to more extreme weather events like:

• Longer heatwaves

• Prolonged droughts

• Intense storms

b) Monsoon and Rainfall Patterns

• The subtropical jet stream affects monsoon rains, particularly in South Asia.

• A weaker jet stream can delay or reduce monsoon rains, leading to droughts, while a stronger one can cause heavy floods.

3. Jet Streams in Weather Forecasting

• Meteorologists track jet streams to predict storms, cold waves, and heatwaves.

• Changes in the jet stream help forecasters warn about hurricanes, tornadoes, and winter storms days in advance.

• Climate models study long-term jet stream shifts to understand global warming’s effects.

Conclusion

Jet streams are critical drivers of weather and climate. Their shifts influence everything from daily weather to long-term climate change. Understanding them helps scientists predict extreme weather and develop better climate models.

Would you like a deeper dive into jet stream patterns in a specific region?

How Qatar Airways Can Use Jet Streams to Reduce Costs

Qatar Airways, like other long-haul airlines, can strategically use jet streams to reduce fuel consumption, lower operating costs, and improve efficiency. Since fuel is one of the largest expenses for an airline, optimizing routes with jet streams can lead to significant savings.

1. Utilizing Jet Streams for Fuel Efficiency

• Eastbound Flights: Qatar Airways can plan eastward routes (e.g., from Doha to Asia, Europe, or North America) to take advantage of strong westerly jet streams.

• Benefit: Aircraft flying within the jet stream experience a tailwind, increasing speed and reducing fuel burn.

• Example: A flight from Doha (DOH) to New York (JFK) can use the jet stream over the Atlantic to shorten travel time and save fuel.

• Westbound Flights: Flights returning westward (e.g., from the U.S. or Europe to Doha) should avoid jet streams to prevent headwinds.

• Solution: Pilots can fly at altitudes or latitudes where the jet stream is weaker, reducing fuel wastage.

2. Cost Savings from Jet Stream Optimization

• Fuel Cost Reduction: By flying with the jet stream, an airline can save up to 5-15% on fuel, translating to millions in savings annually.

• Shorter Flight Times: Reduced flight time means less engine wear, lower maintenance costs, and improved aircraft utilization.

• Improved Scheduling Efficiency: Faster flights mean better on-time performance, reducing airport congestion and labor costs.

3. Advanced Route Planning Using AI & Meteorology

Qatar Airways can integrate AI-powered flight planning tools that analyze real-time jet stream patterns to:

• Adjust cruising altitude dynamically to maximize wind assistance.

• Select the most fuel-efficient flight path for each route.

• Monitor weather conditions to avoid turbulence and improve passenger comfort.

4. Sustainability & Environmental Impact

By leveraging jet streams, Qatar Airways can:

• Reduce CO₂ emissions, supporting its sustainability goals.

• Align with global aviation regulations (e.g., CORSIA) by lowering carbon footprint.

Conclusion

By strategically using jet streams, Qatar Airways can achieve lower fuel costs, reduced emissions, and improved operational efficiency. AI-driven flight planning can further enhance these benefits. Would you like insights into specific routes or AI-based optimizations?

https://youtube.com/shorts/tnpjwJ3hcfY?si=9oShT0yivKfeLpkq

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Tableau and google analytics integration

Yes, you can use Google Analytics (GA) to track user navigation and interactions on Tableau Server dashboards, but there are some important considerations:

Approaches to Track Tableau Server Usage with Google Analytics

1. Using Google Analytics JavaScript in Tableau Web

• If your Tableau dashboards are embedded in a web application, you can add Google Analytics tracking scripts to the web pages.

• This will allow GA to capture user navigation, page views, and interactions.

• Example: If your dashboards are embedded using Tableau’s JavaScript API, you can include GA’s tracking script on the hosting web page.

✅ Best for: Tableau dashboards embedded in web apps.

❌ Not possible for: Native Tableau Server (no direct GA script injection).

2. Tracking User Activity via Tableau Server Logs

• Tableau Server itself does not support Google Analytics natively, but you can track user navigation via Tableau’s usage logs.

• You can extract data from:

• Tableau Repository (PostgreSQL DB) → Tracks logins, dashboard views, and user interactions.

• VizQL Server Logs → Records detailed interactions.

✅ Best for: Internal Tableau Server usage tracking.

❌ Doesn’t provide: Real-time analytics like GA.

3. Using Google Tag Manager (GTM) for Embedded Tableau Dashboards

• If Tableau dashboards are embedded in a web portal, you can use Google Tag Manager (GTM) to track events like:

• Page loads

• Button clicks

• Dashboard filters applied

✅ Best for: Embedded dashboards where GTM is implemented.

❌ Not applicable: Directly within Tableau Server.

Alternative: Tableau Server Built-in Monitoring

• If GA is not an option, consider Tableau Server’s built-in monitoring:

• Admin Views → Provides insights into user activity.

• Custom SQL Queries on Tableau Repository → Query historical_events, http_requests, etc.

• Third-Party Monitoring Tools → Tools like New Relic or Splunk can provide similar insights.

Conclusion: Can You Use GA in Tableau Web?

✔ Yes, if Tableau dashboards are embedded in a web app (via JavaScript API + GA tracking).

❌ No direct GA tracking for standalone Tableau Server dashboards (use Tableau logs instead).



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Tableau Audit user downloads

Tableau Server and Tableau Cloud provide auditing capabilities that can help track user activities, including exporting data. To detect or list users who have downloaded/exported data, you can use the following approaches:

1. Using Tableau’s Administrative Views

Tableau Server and Tableau Cloud offer built-in administrative views to monitor user activities. The “Actions by All Users” or similar admin dashboards include data about downloads:

• Navigate to the Admin Insights section in Tableau Server/Cloud.

• Look for actions such as “Export Data” or “Download Crosstab.”

• Filter the data to identify the users and their activity timestamps.

2. Using Tableau Server Repository (PostgreSQL Database)

Tableau Server stores detailed event logs in its repository (PostgreSQL database). You can query the repository to identify users who downloaded/exported data. Use a query similar to:

SELECT 

  u.name AS username,

  w.name AS workbook_name,

  v.name AS view_name,

  eh.timestamp AS event_time,

  eh.action AS action

FROM 

  historical_events eh

JOIN 

  users u ON eh.user_id = u.id

JOIN 

  views v ON eh.target_id = v.id

JOIN 

  workbooks w ON v.workbook_id = w.id

WHERE 

  eh.action = 'export.crosstab' -- or 'export.data' depending on the action

ORDER BY 

  event_time DESC;

Note: Access to the Tableau repository requires enabling repository access via Tableau Server settings.

3. Using Tableau’s Event Logs

Tableau generates event logs for all user activities. You can parse these logs to find export/download events. The logs are located in the Tableau Server’s logs directory. Search for keywords like "export.crosstab" or "export.data" in the logs.

4. Custom Tableau Dashboard for Monitoring Exports

Create a custom dashboard for monitoring exports by connecting to the Tableau Server repository. Use visualizations to track user activity, including export/download actions.

5. Third-Party Tools or APIs

If you prefer more granular monitoring, use:

• Tableau REST API: Fetch audit data using the Query Workbook or View Activity endpoints.

• Tableau Metadata API: Extract detailed information about user interactions and exported data.

Prerequisites:

• Admin or Site Admin access is required for the repository or admin views.

• Enable Auditing in Tableau Server to ensure activity logs are captured.

Would you like help setting up a specific method?

From Blogger iPhone client

AI models similar to Open AI

Chinese AI startup DeepSeek has recently introduced an open-source model named DeepSeek-R1, which has garnered significant attention for its performance and efficiency. Developed with limited resources, DeepSeek-R1 has outperformed models from major American AI companies in various benchmarks. This achievement underscores the potential of open-source models to rival proprietary systems. 

Meta’s Chief AI Scientist, Yann LeCun, highlighted that DeepSeek’s success exemplifies how open-source models can surpass proprietary ones. He emphasized that this development reflects the advantages of open-source approaches rather than a competition between Chinese and American AI capabilities. 

DeepSeek’s accomplishment is particularly notable given the constraints posed by U.S. export restrictions on advanced chips. The company has demonstrated that innovative software optimization and efficient model architectures can compensate for hardware limitations, allowing them to remain competitive in the AI landscape. 

In addition to DeepSeek, other Chinese tech giants are making strides in the AI sector. For instance, ByteDance, the owner of TikTok, has released an updated AI model named Doubao-1.5-pro, aiming to outperform OpenAI’s latest reasoning models. This move signifies a broader effort by Chinese companies to advance in AI reasoning and challenge global competitors. 

These developments highlight the dynamic and rapidly evolving nature of the AI industry, with open-source models playing a pivotal role in driving innovation and competition.

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Comparison Partition vs Cluster vs Shard

Here’s a detailed comparison matrix and use-case list for Partitioned Tables, Clustered Tables, and Sharded Tables in BigQuery. It covers factors like cost, performance, and trade-offs:

Comparison Matrix

Factor Partitioned Tables Clustered Tables Sharded Tables

Definition Divides a table into logical segments (partitions) based on a column (e.g., DATE or INTEGER). Organizes data within the table into sorted blocks based on one or more columns. Splits data into multiple physical tables (e.g., table_2025, table_2026).

Data Organization Data is stored by partition column (e.g., daily or monthly). Data within the table is clustered and sorted by the specified column(s). Data is stored in completely separate tables.

Supported Columns DATE, TIMESTAMP, DATETIME, INTEGER (for range partitions). Any column type (STRING, DATE, INTEGER, etc.). No restrictions; data is stored in separate tables.

Performance Query performance improves significantly when partition filters are used. Query performance improves for clustered column filters but requires a full table scan if filters are missing. Query performance is good when targeting specific shards but degrades with multiple shards.

Query Cost Costs are lower when partition filters are used (scans only relevant partitions). Costs are lower for clustered column filters, but full table scans cost more. Costs are higher for queries spanning multiple shards.

Storage Cost Single table, optimized for storage efficiency. Single table, efficient storage with clustering metadata overhead. Higher storage costs due to multiple tables.

Scalability Automatically adds partitions as new data arrives. Automatically handles clustering as new data arrives. Requires manual table creation/management for new shards.

Ease of Maintenance Easy to maintain; no manual intervention needed. Easy to maintain; no manual intervention needed. High maintenance; requires creating and managing multiple tables.

Trade-offs Optimized for large datasets with specific partitioning needs (e.g., time-series data). Best for tables with secondary filtering needs (e.g., on a STRING column after partitioning). Simple for small-scale datasets but becomes difficult to manage at scale.

Best Use Case Time-series or range-based data (e.g., logs, analytics data by date). Tables frequently queried with specific column filters (e.g., customer_id). Small datasets that naturally divide into discrete tables (e.g., annual reports).

Use Case List

1. Partitioned Tables

• Best For:

• Large, time-series datasets (e.g., logs, IoT data, analytics data).

• Queries that filter on date or range (e.g., WHERE date >= '2025-01-01' AND date <= '2025-01-31').

• Advantages:

• Optimized query performance with partition filters.

• Lower query costs since only relevant partitions are scanned.

• Scales automatically without manual intervention.

• Trade-offs:

• Limited to DATE, TIMESTAMP, DATETIME, or INTEGER columns for partitioning.

• Requires careful design to avoid too many small partitions (e.g., daily granularity for low-volume datasets).

• Example:

• A web analytics table partitioned by DATE to store daily user activity.

2. Clustered Tables

• Best For:

• Non-time-series data where queries filter on specific columns (e.g., user_id, region, product_id).

• Complementing partitioned tables for multi-dimensional filtering.

• Advantages:

• Improved query performance for columns used in clustering.

• No need to create or manage additional tables.

• Works with all column types, including STRING.

• Trade-offs:

• Full table scans occur if clustering filters are not applied.

• Clustering works best with frequently queried columns.

• Example:

• A sales data table clustered by region and product_id for optimized filtering.

3. Sharded Tables

• Best For:

• Small datasets with distinct natural separations (e.g., annual or region-specific data).

• Use cases with low query frequency where table-level granularity is acceptable.

• Advantages:

• Simple design for small datasets.

• No constraints on partitioning or clustering column types.

• Trade-offs:

• High maintenance: must manually create and manage new tables as data grows.

• Querying across multiple shards is expensive and slow.

• Does not leverage BigQuery’s native partitioning/clustering optimizations.

• Example:

• Annual billing reports stored as billing_2023, billing_2024, etc.

Trade-Offs Summary

1. Partitioned Tables:

• Optimal for date or range-based queries.

• Lower cost and maintenance for large datasets.

2. Clustered Tables:

• Great for secondary filters and multi-dimensional queries.

• Requires careful selection of clustering columns.

3. Sharded Tables:

• Simple but inefficient for large-scale datasets.

• High maintenance and expensive for queries spanning multiple tables.

Would you like help designing a schema or selecting the right strategy for your specific dataset?

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Partitioning existing tables

No, you cannot directly add partitions to an existing non-partitioned table in BigQuery. BigQuery does not support converting an existing non-partitioned table into a partitioned table or adding partitions to a table that wasn’t originally created with partitioning.

However, there are workarounds to achieve a similar result:

Workaround 1: Create a New Partitioned Table and Migrate Data

1. Create a New Partitioned Table: Define the partitioning scheme you need.

2. Insert Data from the Old Table: Use a SELECT query to copy data from the existing non-partitioned table to the new partitioned table.

Example:

-- Step 1: Create a new partitioned table

CREATE TABLE `project_id.dataset_id.new_table`

PARTITION BY DATE(timestamp_column) -- Replace with your partition column

AS

SELECT * FROM `project_id.dataset_id.old_table`;

3. Update your application or queries to use the new table.

Workaround 2: Use Clustering Instead of Partitioning

If you can’t migrate data, consider clustering the existing table. Clustering optimizes query performance and does not require recreating the table. Clustering can be done on multiple columns, including STRING, DATE, or INTEGER.

Example:

-- Create a clustered table

CREATE TABLE `project_id.dataset_id.new_table`

CLUSTER BY column_name -- Replace with the column(s) to cluster by

AS

SELECT * FROM `project_id.dataset_id.old_table`;

Workaround 3: Add Partitions to an Existing Partitioned Table

If the table is already partitioned (e.g., by date or range), BigQuery will automatically add partitions as new data arrives that falls outside the existing partitions. You don’t need to explicitly define additional partitions.

Example:

# Append new data to an existing partitioned table

from google.cloud import bigquery

import pandas as pd

client = bigquery.Client()

# Sample data

data = {"partition_column": ["2025-01-01"], "data_column": [100]}

df = pd.DataFrame(data)

# Load data into the table

table_id = "project_id.dataset_id.partitioned_table"

job_config = bigquery.LoadJobConfig(

  write_disposition="WRITE_APPEND",

  time_partitioning=bigquery.TimePartitioning(field="partition_column")

)

job = client.load_table_from_dataframe(df, table_id, job_config=job_config)

job.result()

Workaround 4: Use Sharded Tables Instead of Partitioning

If you can’t migrate or modify your existing table, you can implement sharded tables (e.g., table_2025, table_2026) to emulate partitioning.

Key Considerations

• Migrating data to a partitioned table is better for performance and cost efficiency, especially for large datasets.

• Clustering works well when partitioning isn’t feasible.

• Sharded tables can become difficult to manage over time.

Let me know if you want assistance with any specific approach!

From Blogger iPhone client

Clustering in bigquery to reduce filter cost

No, BigQuery does not support partitioning on string columns directly. Partitioning in BigQuery is limited to:

1. Time Partitioning:

• Based on DATE, TIMESTAMP, or DATETIME columns.

2. Integer Range Partitioning:

• Based on integer columns with a defined range.

Workaround for Partitioning on String Values

If you need to partition data based on string values, you can use one of these approaches:

1. Use Clustered Tables

BigQuery allows clustering on string columns. Clustering organizes data based on specified columns, improving query performance for those columns. While it’s not partitioning, it serves a similar purpose for filtering.

Example:

from google.cloud import bigquery

# Define table configuration with clustering

table = bigquery.Table("your-project-id.your-dataset-id.your-table-id")

table.clustering_fields = ["string_column"] # Specify string column for clustering

# Create the table

client = bigquery.Client()

client.create_table(table)

print(f"Table {table.table_id} created with clustering.")

2. Map Strings to Integers

You can map your string values to integers and use integer range partitioning.

Example:

If you have strings like ["A", "B", "C"], you can map them to integers [1, 2, 3]. Then use integer range partitioning on the mapped column.

# Mapping string to integer before loading into BigQuery

data = {

  "partition_column": [1, 2, 3], # Mapped integers

  "original_column": ["A", "B", "C"]

}

df = pd.DataFrame(data)

3. Use a Pseudo-Partition

Instead of native partitioning, add a STRING column to represent categories and filter the data in queries. This approach does not provide the storage and query optimization benefits of native partitioning.

Example:

SELECT * 

FROM `your-project-id.your-dataset-id.your-table-id`

WHERE string_column = "desired_value"

4. Use a DATE-Based Proxy

If string values correspond to dates (e.g., year-month), you can convert them into DATE format and use time partitioning.

Example:

df['partition_column'] = pd.to_datetime(df['string_column'], format="%Y-%m")

Key Considerations:

• Performance: Native partitioning is more efficient than pseudo-partitions.

• Cost: Filtering by string without clustering may increase query costs.

• Schema Design: Choose an approach that aligns with your query patterns.

Let me know if you’d like help implementing one of these approaches!

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Partitioning in BigQuery

When appending data to a partitioned table in BigQuery using Python and a DataFrame, you can specify the partition to which the data should be written. Here’s how you can do it step by step:

Prerequisites

1. Install the required libraries:

pip install google-cloud-bigquery pandas

2. Ensure your BigQuery table is partitioned (e.g., by date or integer range).

Code Example

Here’s an example of appending a DataFrame to a BigQuery partitioned table:

from google.cloud import bigquery

import pandas as pd

# Set up BigQuery client

client = bigquery.Client()

# Your project and dataset details

project_id = "your-project-id"

dataset_id = "your-dataset-id"

table_id = "your-table-id" # Replace with your table name

# Full table ID (project.dataset.table)

full_table_id = f"{project_id}.{dataset_id}.{table_id}"

# Sample DataFrame to append

data = {

  "partition_column": ["2025-01-01", "2025-01-02"], # Partition column (e.g., DATE)

  "data_column": [100, 200] # Other columns

}

df = pd.DataFrame(data)

# Define job configuration

job_config = bigquery.LoadJobConfig(

  write_disposition=bigquery.WriteDisposition.WRITE_APPEND, # Append data

  schema_update_options=[

    bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION # If needed

  ],

  time_partitioning=bigquery.TimePartitioning( # Specify time partitioning

    field="partition_column" # The column used for partitioning

  ),

)

# Load DataFrame into BigQuery

job = client.load_table_from_dataframe(df, full_table_id, job_config=job_config)

job.result() # Wait for the job to complete

# Print success message

print(f"Data appended to table: {full_table_id}")

Key Points:

1. Partition Column: Ensure that the partition_column in your DataFrame matches the partitioning column of the BigQuery table.

2. Job Configuration:

• Use bigquery.TimePartitioning to specify the partition column if the table is time-partitioned.

• Use bigquery.RangePartitioning for range-partitioned tables.

3. Write Disposition: Set WRITE_APPEND to append data to the table.

4. Schema Update: If the schema changes (e.g., new columns), include SchemaUpdateOption.ALLOW_FIELD_ADDITION.

Partition Types:

• Time Partitioning: Based on a DATE or TIMESTAMP column.

• Range Partitioning: Based on an integer column.

If your table uses integer range partitions, adjust the configuration:

range_partitioning = bigquery.RangePartitioning(

  field="partition_column", # Integer column

  range_=bigquery.PartitionRange(start=0, end=1000, interval=100),

)

Common Errors and Solutions:

• Partition Not Found: Ensure the partition_column values in the DataFrame are compatible with the table’s partitioning.

• Schema Mismatch: Ensure the DataFrame columns match the table schema. Use explicit casting if needed.

Let me know if you need help with a specific part!

From Blogger iPhone client

Hedging

The increasing crack spread has significant implications for your hedging position. Here’s an overview of how it might affect your strategy and actions you can take:

What Is the Crack Spread?

The crack spread is the difference between the price of crude oil and the prices of its refined products (like gasoline and diesel). An increasing crack spread means that refining margins are improving—refined products are becoming more valuable compared to crude oil.

How It Affects Hedging Positions

1. If You Are a Refiner:

• Positive Impact: An increasing crack spread benefits refiners because it widens profit margins.

• Hedging Strategy:

• You might have hedged your crack spread to lock in profits. If the crack spread increases, unhedged volumes will generate higher profits, but hedged volumes may limit your upside.

• Review your existing hedges to ensure they align with current market trends. You could consider unwinding some hedges or rolling them forward.

2. If You Are a Consumer of Refined Products:

• Negative Impact: Higher refined product prices increase costs.

• Hedging Strategy:

• Ensure that you have enough hedges in place to mitigate the risk of rising refined product prices.

• Evaluate increasing your hedging coverage to lock in current prices for products like diesel or gasoline.

3. If You Are a Producer of Crude Oil:

• Neutral to Negative Impact: Rising crack spreads may not benefit crude oil producers directly unless tied to refined product sales.

• Hedging Strategy:

• Monitor downstream operations if you are vertically integrated, as higher crack spreads could improve downstream profitability.

• Assess the impact of crude price volatility and adjust crude oil hedging positions accordingly.

Actions to Consider

1. Reassess Your Hedging Ratio:

• Determine how much of your exposure is hedged and whether the current ratio is still optimal under the increasing crack spread scenario.

2. Evaluate the Cost of Adjusting Hedges:

• Unwinding or restructuring hedges may come at a cost, so analyze the financial impact.

3. Monitor Market Trends:

• Keep track of both crude oil and refined product markets to anticipate future movements in the crack spread.

4. Scenario Analysis:

• Run sensitivity analyses on your portfolio to understand how various crack spread levels could affect profitability.

5. Consider Hedging Alternative Spreads:

• For more advanced strategies, consider hedging the crack spread itself through futures or options if your exposure is directly tied to it.

Would you like assistance with modeling or optimizing your hedging strategy for this scenario?

From Blogger iPhone client

Data Vault

Data Vault 2.0 is an advanced, agile approach to data warehousing that builds on the principles of the original Data Vault methodology. It is designed to handle large-scale, complex, and rapidly changing data environments. Introduced by Dan Linstedt, Data Vault 2.0 focuses on providing a more robust, scalable, and flexible data architecture to meet modern business needs.

Key Features of Data Vault 2.0

1. Agile and Scalable:

• Designed to support incremental development, making it suitable for agile projects.

• Scales well to handle large volumes of data, both structured and unstructured.

2. Model Components:

• Hubs: Represent unique business keys.

• Links: Capture relationships between business keys.

• Satellites: Store descriptive data (context and history) for hubs and links.

3. Separation of Concerns:

• Decouples business keys, relationships, and descriptive attributes for easier manageability and scalability.

• Allows for parallel development and better handling of changes in source systems.

4. Automation:

• Emphasizes automation of ETL/ELT processes to speed up development and ensure consistency.

5. Business Agility:

• Facilitates rapid adaptation to business changes, making it easier to integrate new data sources or change existing structures.

6. Auditable and Secure:

• Ensures full auditability and traceability by keeping track of all data changes.

• Built-in security controls to handle sensitive data.

7. Big Data and Cloud Integration:

• Extends to handle big data platforms and cloud-native architectures, allowing hybrid implementations.

8. Governance and Compliance:

• Aligns with data governance practices and regulatory requirements.

Key Differences from Data Vault 1.0

• Big Data Readiness: Incorporates methods for handling NoSQL and big data sources.

• Agile Development: Fully supports agile methodologies for iterative delivery.

• Performance: Focus on improved query performance and scalability.

• Standardization: Includes standardized rules for loading, error handling, and metadata-driven automation.

Advantages

• Flexibility: Easily adapts to business changes and new data sources.

• Historical Tracking: Retains the full history of data changes.

• High ROI: Reduces development time and cost through automation and modular design.

• Compliance Ready: Facilitates meeting data governance and regulatory requirements.

Use Cases

• Building enterprise data warehouses for analytics and reporting.

• Integrating diverse data sources in a centralized architecture.

• Creating a data foundation for machine learning and AI initiatives.

Data Vault 2.0 is particularly beneficial for organizations that require agility, scalability, and strong data governance, making it a go-to choice for modern enterprise data management.

Data Vault is different from the Star Schema and Galaxy Schema methodologies commonly used in data warehouses. While both approaches aim to support analytical workloads, they differ significantly in their design principles, use cases, and flexibility.

Comparison: Data Vault vs. Star/Galaxy Schema

Aspect Data Vault Star/Galaxy Schema

Purpose Designed for flexibility, scalability, and change. Optimized for fast querying and reporting.

Model Components Hubs, Links, Satellites (separate business keys, relationships, and descriptive data). Fact Tables (metrics) and Dimension Tables (context).

Scalability Scales well for large and complex datasets. Better suited for smaller, well-defined datasets.

Adaptability Handles frequent schema changes easily. Requires significant rework when schema changes.

Historical Data Preserves all history by default. Can preserve history, but typically by adding slowly changing dimensions (SCDs).

Performance Requires transformation for reporting (not optimized for queries). Optimized for direct query performance.

Automation Automation-driven, metadata-based implementation. Typically manual development of schemas.

Use Case: Data Vault 2.0

Scenario: Retail Chain Expansion

A large retail chain operates multiple stores in various regions and uses a centralized data warehouse to analyze sales, inventory, and customer behavior. The company is expanding rapidly, acquiring new stores and integrating new systems from mergers and acquisitions.

Challenges:

1. Diverse Data Sources: The new stores have different point-of-sale (POS) systems and customer management systems.

2. Frequent Schema Changes: The business frequently modifies its reporting requirements, adding new metrics and dimensions.

3. Compliance Requirements: Regulatory bodies require auditable data lineage and full historical records for financial reporting.

Solution with Data Vault 2.0:

1. Integration of Diverse Systems:

• Create Hubs to store unique business keys like Product_ID, Customer_ID, Store_ID.

• Use Links to capture relationships such as Customer_Purchase (Customer_ID → Product_ID → Store_ID).

• Add Satellites to track descriptive attributes, such as customer demographics, product details, or store locations.

2. Scalability for Expansion:

• As new stores are acquired, their data can be integrated into the Data Vault without altering existing structures. New Hubs, Links, and Satellites are added incrementally.

3. Historical Tracking:

• The Satellite tables store changes to descriptive data (e.g., price changes, customer preferences) over time, preserving full history for analysis and audit.

4. Agile Reporting:

• Analytical models (e.g., Star Schema) can be generated dynamically from the Data Vault for reporting purposes. This allows BI teams to focus on creating views for specific reporting needs without altering the raw data structure.

5. Regulatory Compliance:

• Data lineage and traceability are inherently built into the Data Vault. This ensures the company meets audit and compliance standards, such as GDPR or financial regulations.

Benefits of Data Vault in This Use Case:

• Flexibility: Easily integrates new systems from acquired stores.

• Auditability: Full data lineage and historical tracking for compliance.

• Scalability: Supports growing data volumes and complex relationships.

• Adaptability: Handles frequent schema changes without impacting existing data.

When to Use Star/Galaxy Schema Instead:

• If the retail chain already has well-defined, stable reporting needs (e.g., weekly sales trends by region).

• When fast query performance is critical, and the schema is unlikely to change frequently.

By contrast, Data Vault 2.0 is better suited for dynamic, evolving environments where scalability, flexibility, and governance are paramount.



From Blogger iPhone client

Create a pipeline in azure data factory

Below is an Azure CLI script to create an Azure Data Factory (ADF) instance and set up a basic copy flow (pipeline) to copy data from a source (e.g., Azure Blob Storage) to a destination (e.g., Azure SQL Database).

Pre-requisites

1. Azure CLI installed and authenticated.

2. Required Azure resources created:

• Azure Blob Storage with a container and a sample file.

• Azure SQL Database with a table to hold the copied data.

3. Replace placeholders (e.g., <RESOURCE_GROUP_NAME>) with actual values.

Script: Create Azure Data Factory and Copy Flow

# Variables

RESOURCE_GROUP="<RESOURCE_GROUP_NAME>"

LOCATION="<LOCATION>"

DATA_FACTORY_NAME="<DATA_FACTORY_NAME>"

STORAGE_ACCOUNT="<STORAGE_ACCOUNT_NAME>"

BLOB_CONTAINER="<BLOB_CONTAINER_NAME>"

SQL_SERVER_NAME="<SQL_SERVER_NAME>"

SQL_DATABASE_NAME="<SQL_DATABASE_NAME>"

SQL_USERNAME="<SQL_USERNAME>"

SQL_PASSWORD="<SQL_PASSWORD>"

PIPELINE_NAME="CopyPipeline"

DATASET_SOURCE_NAME="BlobDataset"

DATASET_DEST_NAME="SQLDataset"

LINKED_SERVICE_BLOB="BlobLinkedService"

LINKED_SERVICE_SQL="SQLLinkedService"

# Create Azure Data Factory

az datafactory create \

 --resource-group $RESOURCE_GROUP \

 --location $LOCATION \

 --factory-name $DATA_FACTORY_NAME

# Create Linked Service for Azure Blob Storage

az datafactory linked-service create \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --linked-service-name $LINKED_SERVICE_BLOB \

 --properties "{\"type\": \"AzureBlobStorage\", \"typeProperties\": {\"connectionString\": \"DefaultEndpointsProtocol=https;AccountName=$STORAGE_ACCOUNT;EndpointSuffix=core.windows.net\"}}"

# Create Linked Service for Azure SQL Database

az datafactory linked-service create \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --linked-service-name $LINKED_SERVICE_SQL \

 --properties "{\"type\": \"AzureSqlDatabase\", \"typeProperties\": {\"connectionString\": \"Server=tcp:$SQL_SERVER_NAME.database.windows.net,1433;Initial Catalog=$SQL_DATABASE_NAME;User ID=$SQL_USERNAME;Password=$SQL_PASSWORD;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;\"}}"

# Create Dataset for Azure Blob Storage

az datafactory dataset create \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --dataset-name $DATASET_SOURCE_NAME \

 --properties "{\"type\": \"AzureBlob\", \"linkedServiceName\": {\"referenceName\": \"$LINKED_SERVICE_BLOB\", \"type\": \"LinkedServiceReference\"}, \"typeProperties\": {\"folderPath\": \"$BLOB_CONTAINER\", \"format\": {\"type\": \"TextFormat\"}}}"

# Create Dataset for Azure SQL Database

az datafactory dataset create \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --dataset-name $DATASET_DEST_NAME \

 --properties "{\"type\": \"AzureSqlTable\", \"linkedServiceName\": {\"referenceName\": \"$LINKED_SERVICE_SQL\", \"type\": \"LinkedServiceReference\"}, \"typeProperties\": {\"tableName\": \"<TABLE_NAME>\"}}"

# Create a Copy Pipeline

az datafactory pipeline create \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --pipeline-name $PIPELINE_NAME \

 --properties "{\"activities\": [{\"name\": \"CopyFromBlobToSQL\", \"type\": \"Copy\", \"inputs\": [{\"referenceName\": \"$DATASET_SOURCE_NAME\", \"type\": \"DatasetReference\"}], \"outputs\": [{\"referenceName\": \"$DATASET_DEST_NAME\", \"type\": \"DatasetReference\"}], \"typeProperties\": {\"source\": {\"type\": \"BlobSource\"}, \"sink\": {\"type\": \"AzureSqlSink\"}}}]}"

# Trigger the Pipeline Run

az datafactory pipeline create-run \

 --resource-group $RESOURCE_GROUP \

 --factory-name $DATA_FACTORY_NAME \

 --pipeline-name $PIPELINE_NAME

echo "Azure Data Factory and Copy Pipeline setup complete!"

Steps Breakdown

1. Create Data Factory: Sets up the ADF instance in the specified resource group and location.

2. Linked Services:

• Blob Storage: Connects ADF to Azure Blob Storage.

• SQL Database: Connects ADF to Azure SQL Database.

3. Datasets:

• Source Dataset: Represents the data in Azure Blob Storage.

• Destination Dataset: Represents the table in Azure SQL Database.

4. Pipeline: Defines a copy activity to transfer data from Blob to SQL.

5. Trigger: Starts the pipeline to execute the copy process.

Customize

• Replace <TABLE_NAME> in the SQL dataset properties with the target SQL table.

• Update typeProperties for datasets and activities to match specific formats (e.g., CSV, JSON).

Let me know if you need adjustments or additional features!

From Blogger iPhone client

Airline industry forecasting projects

Here are the top 10 predictive and forecasting project ideas tailored for the technical department of an airline. These projects leverage advanced analytics, machine learning, and AI to optimize operations, reduce costs, and enhance safety:

1. Aircraft Maintenance Prediction (Predictive Maintenance)

• Objective: Predict component failures or maintenance needs before they occur.

• Data: Sensor data from aircraft systems (IoT), maintenance logs, and flight hours.

• Tools: Time series forecasting, anomaly detection, and machine learning.

• Impact: Reduces unplanned downtime and maintenance costs while improving safety.

2. Fuel Consumption Forecasting

• Objective: Predict fuel consumption for flights based on historical data, weather conditions, and aircraft types.

• Data: Historical fuel usage, flight routes, aircraft models, and meteorological data.

• Tools: Regression models, neural networks, and optimization algorithms.

• Impact: Helps optimize fuel planning and reduce operational costs.

3. Flight Delay Prediction

• Objective: Predict potential flight delays due to technical issues, weather, or other factors.

• Data: Historical flight data, weather conditions, airport congestion, and maintenance schedules.

• Tools: Machine learning classification models like random forests or gradient boosting.

• Impact: Improves operational efficiency and customer satisfaction by proactive decision-making.

4. Spare Parts Inventory Forecasting

• Objective: Predict the demand for spare parts to ensure optimal inventory levels.

• Data: Maintenance records, component lifespan data, and inventory usage.

• Tools: Time series analysis, demand forecasting models (ARIMA, Prophet).

• Impact: Reduces inventory holding costs while ensuring parts availability.

5. Aircraft Health Monitoring System

• Objective: Continuously monitor and forecast the health of critical aircraft systems.

• Data: Sensor and telemetry data from aircraft systems.

• Tools: Real-time anomaly detection, machine learning, and IoT integration.

• Impact: Enhances safety by identifying potential risks during operations.

6. Crew Scheduling and Optimization

• Objective: Predict and optimize crew schedules based on flight demand and operational constraints.

• Data: Crew availability, flight schedules, and historical data.

• Tools: Optimization algorithms, predictive models, and scheduling software.

• Impact: Reduces overstaffing, underutilization, and scheduling conflicts.

7. Aircraft Route Optimization

• Objective: Forecast optimal routes for fuel efficiency and reduced travel time.

• Data: Historical flight paths, weather conditions, air traffic data.

• Tools: Machine learning, optimization algorithms, and geospatial analytics.

• Impact: Minimizes operational costs and improves on-time performance.

8. Weather Impact Prediction

• Objective: Predict the impact of weather conditions on flight operations.

• Data: Meteorological data, historical flight delays, and cancellations.

• Tools: Predictive analytics and machine learning models.

• Impact: Enhances decision-making for scheduling and operations during adverse weather conditions.

9. Passenger Demand Forecasting

• Objective: Predict passenger demand for flights to adjust aircraft allocation and technical resources.

• Data: Historical passenger data, booking trends, seasonal factors, and economic indicators.

• Tools: Time series models and deep learning.

• Impact: Aligns aircraft and technical resources with demand, reducing costs.

10. Safety Incident Prediction

• Objective: Predict the likelihood of safety incidents based on operational and maintenance data.

• Data: Incident reports, flight logs, and maintenance history.

• Tools: Machine learning classification models and natural language processing (NLP) for analyzing incident reports.

• Impact: Enhances safety compliance and proactive risk mitigation.

Tools and Technologies:

• Programming Languages: Python, R, SQL.

• Machine Learning Libraries: TensorFlow, PyTorch, Scikit-learn, XGBoost.

• Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn.

• Forecasting Models: ARIMA, Prophet, LSTM (Long Short-Term Memory).

Would you like detailed guidance or implementation support for any of these projects?

From Blogger iPhone client

Excel leveraging medians

To calculate median values in a PivotTable in Excel, you need to use a workaround because PivotTables do not have a built-in function for the median (unlike average, sum, etc.). Here’s how you can calculate median values step by step:

Method 1: Using Helper Columns

1. Add a Helper Column:

• In your dataset, add a helper column for ranking or grouping data. For example, add a column that uniquely identifies records for each group (e.g., dates, categories, or regions).

2. Sort the Data:

• Sort your data by the field for which you want to calculate the median.

3. Use the MEDIAN Function:

• Outside the PivotTable, use the MEDIAN function for each group.

• Example:

• If your group is “Category A” and your values are in Column D, use:

=MEDIAN(IF(A:A="Category A", D:D))

• Use Ctrl + Shift + Enter for array formulas (or just Enter in newer versions of Excel).

Method 2: Using Power Query (Preferred for Large Data)

1. Load Data into Power Query:

• Select your dataset → Go to the Data tab → Click Get & Transform Data → Choose From Table/Range.

2. Group the Data:

• In Power Query, use the Group By feature.

• Select the column to group by (e.g., “Category”).

• Under Operations, choose All Rows.

3. Add a Median Column:

• For each group, add a custom column to calculate the median.

• Use the formula:

=List.Median([Values])

4. Load Back to Excel:

• Once done, load the grouped table back into Excel.

Method 3: Using DAX in a Data Model

1. Load Data to Power Pivot:

• Select your data → Go to the Insert tab → Click PivotTable → Check Add this data to the Data Model.

2. Create a DAX Measure:

• In the Power Pivot window, create a new measure:

MedianValue = MEDIAN(Table[ValueColumn])

3. Add Measure to PivotTable:

• Add the DAX measure to your PivotTable to calculate the median dynamically.

These methods will allow you to calculate medians and display them effectively in your PivotTables. For large datasets, Power Query or DAX is more efficient.

From Blogger iPhone client

Oracle CLOB to BigQuery

To transfer a CLOB (Character Large Object) from Oracle to BigQuery, follow these steps:

1. Extract CLOB Data from Oracle

You need to extract the CLOB data in manageable chunks, as shown in the error message, because the CLOB data size exceeds the buffer limit. Use one of the following methods:

Option A: Using DBMS_LOB.SUBSTR

SELECT DBMS_LOB.SUBSTR(wo_task_card_xml, 4000, 1) AS clob_chunk

FROM odb.wo_task_card

WHERE wo_task_card_xml IS NOT NULL;

This extracts the first 4000 characters of the CLOB. Repeat the process for the remaining chunks by adjusting the starting position.

Option B: Export the Entire CLOB

Use Oracle SQL Developer or a similar tool to export the full CLOB content into a text or CSV file.

2. Save the Extracted Data

• Save the CLOB data into a file in a readable format (e.g., .csv, .json).

• Ensure the file adheres to BigQuery’s schema requirements.

3. Prepare BigQuery Schema

BigQuery does not support CLOBs directly. Instead, store the CLOB as a STRING or JSON in BigQuery. Define a column with a STRING type in your BigQuery table.

4. Load Data into BigQuery

Use one of these methods to load data:

Option A: BigQuery Console

1. Upload the exported file via the BigQuery web interface.

2. Map the CLOB field to the STRING column.

Option B: BigQuery CLI

Use the bq command-line tool:

bq load --source_format=CSV your_dataset.your_table path_to_file.csv schema.json

Option C: Dataflow/ETL Tools

Use tools like Google Dataflow, Apache Beam, or Fivetran for automated CLOB extraction and transfer.

Additional Notes

• Ensure that your CLOB data is UTF-8 encoded to avoid character encoding issues.

• If the CLOB contains XML/JSON, validate it before loading into BigQuery.



From Blogger iPhone client

ETL tools to review

Azure Data Factory (ADF) is a powerful cloud-based data integration service, but there isn’t an open-source equivalent that provides the exact same functionality and seamless integration with Azure services. However, there are several open-source tools and frameworks that can be used as alternatives to build similar workflows and pipelines for data integration and ETL (Extract, Transform, Load). Here are some notable ones:

Open-Source Alternatives to Azure Data Factory

1. Apache NiFi

• Description: Apache NiFi is an open-source data integration tool that supports real-time data flows and processing. It provides a web-based interface for designing and monitoring workflows.

• Features:

• Visual pipeline design

• Built-in processors for various data sources

• Real-time data streaming

• Website: Apache NiFi

2. Apache Airflow

• Description: Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows as directed acyclic graphs (DAGs).

• Features:

• Python-based workflow creation

• Scalability and flexibility

• Broad support for external integrations

• Website: Apache Airflow

3. Luigi

• Description: Luigi is a Python package for building complex pipelines of batch jobs. It is designed to handle dependencies and scheduling.

• Features:

• Dependency management

• Built-in support for Hadoop, Spark, and more

• Website: Luigi

4. Dagster

• Description: Dagster is an orchestrator for the development, production, and observation of data assets.

• Features:

• Type-safe and versioned pipelines

• Integration with Pandas, Spark, and more

• Modern developer experience

• Website: Dagster

5. Kettle (Pentaho Data Integration)

• Description: Kettle, now part of the Pentaho suite, is an open-source data integration tool that provides a GUI for designing data pipelines.

• Features:

• Easy-to-use visual interface

• Support for complex transformations

• Website: Pentaho Kettle

6. Talend Open Studio

• Description: Talend Open Studio is a widely used open-source ETL tool that offers a graphical interface for designing pipelines.

• Features:

• Drag-and-drop interface

• Pre-built connectors for various databases and services

• Website: Talend Open Studio

7. Hevo (Free Tier Option)

• Description: While not entirely open source, Hevo offers a free tier and provides a managed, no-code ETL platform.

• Website: Hevo

Key Considerations

While these tools offer similar functionalities, choosing the right one depends on:

• Your specific requirements (batch vs. streaming, cloud vs. on-premise)

• The level of coding or automation needed

• The ease of integration with existing data systems

Let me know if you’d like a deeper dive into any of these tools or how to integrate them into your workflows!

From Blogger iPhone client

Organization transition from SCCM to Microsoft store

Organizations can switch from System Center Configuration Manager (SCCM) to leverage the Microsoft Store for Business or Microsoft Store in Intune as part of a modern software distribution and management strategy. However, the feasibility and effectiveness of this switch depend on the organization’s needs and the features available in these platforms.

Key Considerations for Switching

1. Benefits of Using the Microsoft Store for Business

• Centralized Management:

• Organizations can use the Microsoft Store for Business to acquire, manage, and distribute apps to users.

• Integration with tools like Microsoft Intune enables seamless app deployment and updates.

• Seamless Updates:

• Apps from the Microsoft Store update automatically, reducing the need for manual patching or deployments.

• Simplified Deployment:

• Pre-packaged apps in the Microsoft Store are ready for deployment without additional packaging efforts required in SCCM.

• Cost Efficiency:

• Reduces reliance on SCCM infrastructure, which can save on server costs and administrative overhead.

• User Self-Service:

• Users can access approved apps directly through a company-managed store, improving productivity.

2. Limitations of the Microsoft Store

• Limited Software Availability:

• Not all enterprise applications are available in the Microsoft Store. Many enterprise apps require custom installers, which SCCM handles effectively.

• Custom Installations:

• SCCM supports customized installations (e.g., silent installs, pre-configuration settings), which are harder to achieve through the Microsoft Store.

• Compliance and Security:

• SCCM offers granular control over deployments, configurations, and compliance policies that may be more robust than what the Microsoft Store provides.

• Legacy and Non-UWP Apps:

• The Microsoft Store primarily supports Universal Windows Platform (UWP) apps. Legacy apps and apps requiring deep system integration may not be available.

3. Use of Microsoft Intune as a Bridge

Organizations moving away from SCCM can combine Microsoft Intune with the Microsoft Store for Business:

• App Deployment:

• Intune can deploy apps from both the Microsoft Store and external sources, providing flexibility for software not available in the Store.

• Device Management:

• Intune provides mobile device and PC management capabilities, replacing SCCM’s device management features.

• Policy Enforcement:

• Intune allows enforcement of security and compliance policies.

4. Hybrid Approach

For organizations with diverse requirements, a hybrid approach can be effective:

• Use SCCM for managing legacy, custom, or complex applications.

• Use the Microsoft Store (via Intune) for modern UWP apps and straightforward installations.

• Gradually transition workloads to Intune as more apps adopt modern packaging.

5. Key Scenarios for Switching

• Small to Medium Businesses (SMBs):

• The Microsoft Store for Business with Intune is a cost-effective solution for SMBs with simpler IT needs.

• Cloud-First Organizations:

• Organizations moving to a fully cloud-based infrastructure may benefit from Intune and the Store for app management.

• Modern Device Deployment (Windows Autopilot):

• The Microsoft Store integrates well with Windows Autopilot for zero-touch device provisioning.

6. Considerations for Transition

• Training:

• IT teams need training on Intune and Microsoft Store management.

• Software Compatibility:

• Evaluate if all required software is available or can be transitioned to modern app packages.

• Cost Analysis:

• Assess costs associated with migrating to Intune and decommissioning SCCM infrastructure.

Conclusion

Switching from SCCM to the Microsoft Store and Intune is feasible for many organizations, especially those prioritizing cloud-based infrastructure and modern app delivery. However, for enterprises with complex app requirements or reliance on legacy systems, SCCM may still be necessary in a hybrid setup.

Would you like guidance on implementing a specific transition strategy or exploring Intune in more detail?

From Blogger iPhone client

Tableau journey to join windows store

Tableau Desktop is not available in the Windows Store due to several key reasons related to the way Tableau is designed, distributed, and managed:

1. Software Complexity

• Tableau Desktop is a robust data visualization tool that relies on specific configurations, custom installers, and libraries that may not be compatible with the Microsoft Store’s app packaging standards.

• Microsoft Store apps often use the Universal Windows Platform (UWP) framework, which imposes certain restrictions that Tableau Desktop doesn’t conform to.

2. Licensing and Pricing Model

• Tableau uses a subscription-based licensing model (e.g., Tableau Creator), requiring license activation via Tableau’s account system. This model may not align with the transactional framework of the Microsoft Store.

• Licensing often involves enterprise agreements or volume licensing, which is managed directly through Tableau’s website or authorized resellers.

3. Enterprise-Level Features

• Tableau Desktop is an enterprise-grade software that integrates with complex systems like databases, APIs, Tableau Server, and Tableau Cloud.

• It is typically deployed in corporate environments where IT teams manage installations and configurations. These enterprise deployment scenarios are better handled outside the Microsoft Store’s consumer-oriented model.

4. Platform Independence

• Tableau Desktop supports both Windows and macOS. By hosting its downloads on its website, Tableau ensures platform-agnostic distribution.

• The Windows Store is primarily designed for UWP apps, whereas Tableau Desktop requires platform-specific installers with dependencies.

5. Security and IT Management

• Enterprises often require greater control over how software is installed and managed (e.g., silent installations, IT-managed updates). Direct downloads from Tableau’s site allow IT administrators to use tools like:

• Microsoft Intune

• SCCM (System Center Configuration Manager)

6. Legacy Systems Compatibility

• Tableau Desktop supports older versions of Windows (e.g., Windows 10 and earlier). The Microsoft Store often targets more modern systems, potentially leaving legacy users without access.

7. Application Size and Customization

• Tableau Desktop is a heavyweight application that includes drivers, connectors, and libraries for integration with databases and other tools.

• Packaging such a feature-rich application to meet Microsoft Store requirements may compromise functionality or require significant development effort.

8. Focus on Direct Relationships

• Tableau prioritizes a direct relationship with its users and enterprises through its own website for support, updates, and licensing.

• This allows them to maintain greater control over customer experience and avoid reliance on third-party platforms like the Windows Store.

Alternatives in the Microsoft Store

While Tableau Desktop is not available, alternatives like Tableau Public (a free version with limited functionality) can be used by casual users. However, this is also distributed directly from Tableau’s website, not via the Windows Store.

If you have specific needs for Tableau, such as enterprise deployment or app store-like access, Tableau may be deployed using alternative methods, such as managed app deployment through Microsoft Intune or VDI solutions. Let me know if you’d like more details!

From Blogger iPhone client

Windows store ETL pipelines and integration

Sharing data from the Windows Store to a data warehouse typically involves data integration and ETL (Extract, Transform, Load) technologies. The exact technology stack can vary depending on the tools and architecture being used, but here are the key components and options:

1. Windows Store Data Access

• Windows Store Analytics API:

• Microsoft provides the Windows Store Analytics API to retrieve app performance data, including metrics like downloads, revenue, ratings, and usage.

• This API is a REST-based API that enables secure programmatic access to data.

• Technology: REST API

• Authentication: OAuth 2.0

• Format: Data is returned in JSON or XML format.

2. Data Extraction

• Custom Scripts:

• Use programming languages like Python, Java, or PowerShell to call the Windows Store Analytics API and extract the data.

• Python libraries like requests can handle API calls, while pandas can format the data.

• Example with Python:

import requests

# Define API endpoint and parameters

api_url = "https://manage.devcenter.microsoft.com/v1.0/my/analytics/appPerformance"

headers = {"Authorization": "Bearer YOUR_ACCESS_TOKEN"}

params = {"applicationId": "your_app_id", "startDate": "2025-01-01", "endDate": "2025-01-07"}

# Fetch data

response = requests.get(api_url, headers=headers, params=params)

data = response.json()

# Process and store the data

print(data)

3. Transformation and Loading

After data extraction, it needs to be cleaned, transformed, and loaded into the warehouse.

Options for ETL Tools:

1. Cloud-Based ETL Tools:

• Azure Data Factory (ADF):

• Best for integrating data from Microsoft sources like Windows Store to Azure Synapse Analytics or other warehouses.

• Fivetran:

• Automates data pipeline creation for APIs like Windows Store.

• Stitch:

• Connects APIs to data warehouses like BigQuery, Snowflake, or Redshift.

2. Custom ETL Pipelines:

• Use tools like Apache Airflow or Prefect for creating custom workflows.

• Example: Extract with Python, transform with Pandas, and load using a warehouse SDK (e.g., BigQuery or Snowflake SDKs).

4. Data Warehouse Integration

• Popular Data Warehouses:

• Azure Synapse Analytics: Microsoft’s solution for large-scale data warehousing.

• Google BigQuery: Best for integration with Google Cloud and analytics workloads.

• Amazon Redshift: Suitable for AWS-based setups.

• Snowflake: A cloud-native, scalable warehouse.

• Data Loading Methods:

• Batch Uploads:

• Save extracted data into files (CSV/JSON) and upload them to the warehouse.

• Streaming:

• Use APIs or SDKs for real-time data ingestion.

5. Automation and Scheduling

• Scheduler Tools:

• Use Cron Jobs, Apache Airflow, or Azure Logic Apps to schedule the pipeline for regular data extraction.

• Serverless Solutions:

• Use Azure Functions or AWS Lambda to trigger data extraction and loading based on events.

6. Data Security

• Ensure data encryption in transit (HTTPS) and at rest in the warehouse.

• Use OAuth 2.0 tokens to securely access the Windows Store Analytics API.

Example Architecture

1. Extract: Use a Python script or Azure Data Factory to fetch data from the Windows Store Analytics API.

2. Transform: Clean and format the JSON data into a tabular format.

3. Load: Push data into the warehouse (e.g., Azure Synapse Analytics or Snowflake) using their native connectors.

Let me know if you’d like code examples, a walkthrough for a specific ETL tool, or guidance on setting up a warehouse integration!

From Blogger iPhone client