Creating a centralize data pipeline for ADO backlogs across all PODs

Sure! Here’s a polished sales pitch tailored to advocate for creating a Single Source of Truth (SSoT) for all data pipelines sourced from ADO backlogs across different pods:

Sales Pitch: Unlock Efficiency and Trust: A Unified Source for Data Pipelines

The Challenge We Face Today

Our data landscape is growing rapidly. Multiple pods are building and deploying data pipelines in parallel, with tasks tracked in Azure DevOps (ADO) backlogs. While this approach drives velocity, it also introduces fragmentation and visibility gaps:

• Siloed Backlogs: Each pod maintains its own backlog, making it difficult to track cross-pod dependencies and overall progress.

• Pipeline Overlap & Duplication: Without a consolidated view, we risk building redundant pipelines or solving the same problem differently across teams.

• Lack of Traceability: When issues arise, tracing a data pipeline back to its business context and development history is time-consuming.

• Data Quality Concerns: Inconsistent development practices lead to varied data quality, affecting downstream reporting and analytics.

These inefficiencies don’t just slow us down—they erode stakeholder confidence in our data products.

The Solution: A Single Source of Truth for Data Pipelines

We propose a centralized platform—a Single Source of Truth (SSoT)—that aggregates and standardizes information about all data pipelines across pods, directly integrated with ADO backlogs.

What Does This Look Like?

• Centralized Registry: A unified dashboard capturing metadata for every data pipeline (e.g., source system, transformation logic, target systems, SLAs).

• ADO Integration: Automated ingestion of backlog items, linking ADO work items to pipelines, data lineage, and deployment status.

• Pod-Agnostic View: Cross-pod visibility into in-flight and completed pipelines, enabling proactive identification of overlaps or gaps.

• Pipeline Lineage & Traceability: End-to-end visibility from backlog requirement → pipeline development → production deployment → data consumption.

• Standardized Metadata: Enforce minimum metadata standards for every pipeline to ensure consistency and reusability.

Key Benefits

Pain Point

How SSoT Solves It

Duplication & Wasted Effort

Pods see existing pipelines before building new ones, reducing rework.

Visibility Gaps

Leaders and teams see pipeline progress across pods in real-time.

Traceability & Auditability

Rapidly trace data issues back to the backlog item and pipeline owner.

Operational Efficiency

Reduced time spent hunting for pipeline details → Faster problem resolution.

Data Quality & Governance

Standard metadata across pipelines → Consistent development practices → Improved data trust.

Cross-Team Collaboration

Pods leverage each other’s work, fostering reuse and accelerating delivery.

Real Business Impact

• 30-40% reduction in time spent troubleshooting pipeline failures.

• 20-25% faster delivery of new pipelines by eliminating redundant development.

• Improved stakeholder confidence with traceable, well-documented data pipelines.

Call to Action

Let’s invest in building this Single Source of Truth for our data pipelines.

By doing so, we future-proof our data delivery process, empower our teams, and position our organization as a leader in data-driven excellence.

From Blogger iPhone client

Automating excel

Here are 10 AI tools that make Excel seem like a toy: 👇 

1. SheetAI App  

  - Type your request in plain English. 

  - Automates complex tasks in minutes. 

  - Perfect for large-scale analysis. 

  🔗 [https://www.sheetai.app]

If you want more tips and insights about AI, Join - my newsletter that teaches you how to leverage AI 👇100% Free - 

https://lnkd.in/gzTRpdMF

https://lnkd.in/gqqzY6bk

  

2. Arcwise 

  - Integrates AI customized to your business. 

  - Models built directly into spreadsheets. 

  - Boosts efficiency and personalization. 

  🔗 [https://arcwise.app]

  

3. ChatCSV (acquired by Flatfile)  

  - Ask questions directly to your CSV files. 

  - Acts like a personal data analyst. 

  - Simplifies complex queries effortlessly. 

  🔗 [https://www.chatcsv.co]

  

4. Numerous AI 

  - Integrates ChatGPT into Google Sheets. 

  - Simplifies data management and manipulation. 

  - Cost-effective and powerful. 

  🔗 [https://numerous.ai]

  

5. Rows 

  - AI-driven data analysis, summaries, and transformations. 

  - Accelerates spreadsheet creation. 

  - Ideal for quick decision-making. 

  🔗 [https://rows.com/ai]

  

6. Genius Sheets 

  - Connects to internal data using natural language. 

  - Runs instant analysis like never before. 

  - Perfect for real-time insights. 

  🔗 [https://lnkd.in/dVtyX7xb]

  

7. Equals 

  - Start with a blank sheet and gain instant insights. 

  - Ideal for quick, AI-powered analytics. 

  - Reduces manual effort drastically. 

  🔗 [https://equals.com/ai]

  

8. ChartPixel  

  - Creates AI-assisted charts and slides. 

  - Turns raw data into actionable insights. 

  - Saves hours of presentation preparation. 

  🔗 [https://chartpixel.com]

  

Spreadsheets don't have to be tedious anymore. 

Which of these tools are you adding to your workflow? 

Share your thoughts below! 

Bonus Alert 🎁

Free Courses you will regret not taking in 2025 👇

🚀7000+ free courses free access : https://lnkd.in/g_W26d7h

👉Microsoft Power BI

https://lnkd.in/g45MuT-W

👉Deep Learning 

https://lnkd.in/gY7WQe4K

👉Machine Learning

https://lnkd.in/ggA-6-Jh

👉IBM Data Science

https://lnkd.in/gu4RPKwD

👉IBM Data Analysts

https://lnkd.in/gyyJvR2D

👉Data Analytics

https://lnkd.in/g-3tsuKG

👉Google IT support

https://lnkd.in/gh8Gs7XN

👉Cybersecurity

https://lnkd.in/gFZPmX_c

👉IBM Project Manager

https://lnkd.in/d9g-SZsx

👉Google Project Management

https://lnkd.in/dN4Gv65a

👉AI Product Management

https://lnkd.in/dAQcVs3t

👉Meta UI/UX Design:

https://lnkd.in/gjCp7x8E

👉Meta Frontend Developer

https://lnkd.in/gTiGrbAK

👉MERN Stack Developer

https://lnkd.in/dmfer6Ys

👉Generative AI

https://lnkd.in/gXQepmtz

👉Prompt Engineering for

From Blogger iPhone client

Oracle Converting Date Date Time

The difference in results between TO_CHAR and TO_TIMESTAMP in Oracle when filtering by date and time often stems from the data type and internal storage of dates and timestamps. Here’s a breakdown of why they can produce different records:

1. Data Type Matters (DATE vs TIMESTAMP):

• DATE in Oracle stores date and time up to seconds, but it doesn’t include fractional seconds.

• TIMESTAMP includes fractional seconds.

• If you compare TO_CHAR with a formatted string and the underlying column is DATE, it truncates to seconds, so it matches based on the exact string representation.

• If you use TO_TIMESTAMP(), it is trying to match with precision, including fractional seconds if the column is TIMESTAMP.

2. TO_CHAR Behavior:

• When you use TO_CHAR(date_column, 'YYYY-MM-DD HH24:MI:SS'), it converts the date to a string representation in that format.

• This comparison is purely text-based after conversion, so it won’t consider fractional seconds.

• It can match exact HH24:MI:SS, but any fractional seconds are ignored.

3. TO_TIMESTAMP Behavior:

• When you filter with TO_TIMESTAMP(), you are comparing timestamp values.

• If your modified_date column is of type DATE, comparing it with TIMESTAMP can cause implicit type conversion, which might not work as expected.

• If modified_date is TIMESTAMP and has fractional seconds, filtering by TO_TIMESTAMP('05-FEB-25 09:46:56', 'DD-MON-YY HH24:MI:SS') will exclude rows with fractional seconds like 09:46:56.123.

4. Implicit Conversion Issues:

• Oracle might implicitly convert DATE to TIMESTAMP or vice versa when you mix types in comparison.

• This can lead to precision loss or unexpected results.

5. Best Practice:

• If modified_date is DATE type:

WHERE modified_date = TO_DATE('05-FEB-25 09:46:56', 'DD-MON-YY HH24:MI:SS')

• If modified_date is TIMESTAMP type:

WHERE modified_date = TO_TIMESTAMP('05-FEB-25 09:46:56', 'DD-MON-YY HH24:MI:SS')

• If you don’t care about fractional seconds:

WHERE TRUNC(modified_date) = TO_DATE('05-FEB-25', 'DD-MON-YY')

6. When You Use TO_CHAR:

• You are forcing a string comparison, which might work but is slower and can lead to confusion.

• It is not recommended for date filtering.

Would you like help reworking your query for your specific data type?

From Blogger iPhone client

Tableau Server Extract all workbooks attributes

Convert Tableau Server Client Workbooks List to Pandas DataFrame

When you use the Tableau Server Client (TSC) to get all workbooks from the server:

all_workbooks = list(TSC.Pager(server.workbooks))

You get a list of workbook objects. Each object has attributes like id, name, project_name, owner_id, etc.

Convert all_workbooks List to Pandas DataFrame:

import pandas as pd

import tableauserverclient as TSC

# Assuming you already have `all_workbooks` as a list

all_workbooks = list(TSC.Pager(server.workbooks))

# Extracting relevant attributes into a list of dictionaries

workbooks_data = [

  {

    'id': wb.id,

    'name': wb.name,

    'project_name': wb.project_name,

    'owner_id': wb.owner_id,

    'created_at': wb.created_at,

    'updated_at': wb.updated_at,

    'size': wb.size,

    'show_tabs': wb.show_tabs,

    'webpage_url': wb.webpage_url,

  }

  for wb in all_workbooks

]

# Convert to DataFrame

df = pd.DataFrame(workbooks_data)

print(df)

Explanation:

• List comprehension: Extracts key attributes from each WorkbookItem object.

• Attributes commonly used:

• wb.id

• wb.name

• wb.project_name

• wb.owner_id

• wb.created_at

• wb.updated_at

• wb.size

• wb.show_tabs

• wb.webpage_url

You can customize this list based on the attributes you need from the WorkbookItem object.

Sample Output:

         id      name   project_name    owner_id      created_at ... size show_tabs           webpage_url

0 abcd1234efgh5678   Sales Report Finance Project user123456789 2023-10-01 08:00:00 ... 2500   True https://tableau.server/view/...

1 wxyz9876lmno5432 Marketing Data Marketing Group user987654321 2023-11-05 10:30:00 ... 3100   False https://tableau.server/view/...

Key Notes:

• Make sure you import pandas and tableauserverclient.

• This approach is efficient and works well with TSC.Pager() results.

• You can easily export the DataFrame to CSV or Excel:

df.to_csv('tableau_workbooks.csv', index=False)

Would you like help with pagination handling, filtering specific workbooks, or exporting the DataFrame?

From Blogger iPhone client

Automating tableau bulk connection

It is technically possible to use a tool like Selenium to automate the browser‐based creation of a BigQuery connection in Tableau—complete with entering a custom query and performing bulk connection operations—but there are several important caveats to consider:

What You Can Do with Selenium

• Browser Automation:

Selenium (or a similar browser automation tool) can control Chrome (or another browser) to log into Tableau Server or Tableau Cloud, navigate the UI, and simulate the manual steps you’d normally take to create a connection. This means you could script the process of:

• Signing into Tableau.

• Navigating to the data connection or data source creation page.

• Selecting Google BigQuery as the connection type.

• Entering or uploading service account credentials.

• Inserting a custom SQL query.

• Repeating these steps in a loop to handle bulk operations.

• Bulk Operations:

With careful scripting, you can iterate over a list of parameters or queries, effectively automating the creation of multiple connections. This could be useful if you need to deploy many similar connections at once.

Challenges and Considerations

• Brittleness:

UI automation is inherently fragile. Any change to the Tableau web interface (such as layout, element identifiers, or workflow changes) can break your Selenium script. This means you’ll have to invest time in maintaining your automation scripts.

• Lack of Official Support:

Tableau does not officially support UI automation for creating or managing connections. The REST API and Tableau Server Client (TSC) library are the recommended and supported methods for automating Tableau tasks. If those APIs do not expose exactly the functionality you need (for example, the embedding of a custom query in a connection), that might force you to consider UI automation—but keep in mind the risks.

• Authentication & Security:

Automating through the browser may require handling authentication (and possibly multi-factor authentication) in a secure manner. Ensure that any credentials or service account keys are managed securely and not hard-coded in your automation scripts.

• Complexity of Custom Queries:

If your process involves creating custom SQL queries as part of the connection setup, you’ll need to script the logic to input these queries correctly. Any errors in the custom query syntax or its integration into the Tableau UI may not be easily recoverable from an automated script.

Recommended Alternatives

• Tableau REST API / TSC Library:

Before resorting to Selenium, review whether you can accomplish your goal using Tableau’s REST API or the Tableau Server Client library. Although these APIs may not let you “create a connection from scratch” in every detail (especially if you need to embed non-standard elements like a custom query), they are far more stable and supported for bulk operations.

• Hybrid Approach:

In some cases, you might use a combination of API calls (for publishing and updating data sources) and lightweight browser automation to handle any remaining steps that the API cannot cover. This minimizes the parts of the process that rely on brittle UI automation.

In Summary

Yes, you can use Selenium or a similar tool to automate the creation of a BigQuery connection (including entering a custom query and handling bulk connections) by automating browser interactions in Chrome. However, this approach is generally less robust and more error-prone than using the officially supported Tableau REST API or TSC library. If you choose the Selenium route, prepare for additional maintenance and troubleshooting as Tableau’s web interface evolves.

For more details on Tableau’s supported automation methods, see the official Tableau REST API documentation ( ).

From Blogger iPhone client

Kubernetes management

https://rancherdesktop.io/

From Blogger iPhone client

Aviation data

The primary sources for live airline flight data include:

1. ADS-B (Automatic Dependent Surveillance–Broadcast) Networks

• OpenSky Network (Free & Research-Oriented)

• ADS-B Exchange (Unfiltered Global Flight Data)

• FlightAware (Commercial & API Access)

• Flightradar24 (Commercial & API Access)

2. FAA & Government Aviation Feeds

• FAA SWIM (System Wide Information Management) – US-based real-time flight data

• Eurocontrol NM B2B – European air traffic data

3. IATA (International Air Transport Association) APIs

• Offers flight schedules, airline status, and operational data (paid access)

4. Airline & Airport APIs

• Many airlines and airports provide public or commercial APIs for live flight status

5. GDS (Global Distribution Systems)

• Amadeus, Sabre, and Travelport provide airline ticketing and scheduling data

If you’re looking for a commercial-grade solution like Aviation Week, services like FlightAware Firehose, OAG, or Cirium offer comprehensive real-time and historical aviation data. Are you planning to build something aviation-related?

From Blogger iPhone client

Tableau export workbooks

Tableau’s REST API does not natively support exporting workbooks, images, or PDFs directly. However, you can achieve this using a combination of Tableau REST API and the Tableau Server Client (TSC) or the JavaScript API. Here’s how:

1. Export a Tableau Workbook (TWB or TWBX)

You can export a workbook using the REST API by downloading it from Tableau Server:

Endpoint:

GET /api/3.15/sites/{site_id}/workbooks/{workbook_id}/content

Steps:

1. Authenticate using Tableau’s REST API (/auth/signin).

2. Get Site ID & Workbook ID from /sites and /workbooks.

3. Download the Workbook using the content endpoint.

Example using Python:

import requests

TABLEAU_SERVER = "https://your-tableau-server"

TOKEN = "your-auth-token"

SITE_ID = "your-site-id"

WORKBOOK_ID = "your-workbook-id"

url = f"{TABLEAU_SERVER}/api/3.15/sites/{SITE_ID}/workbooks/{WORKBOOK_ID}/content"

headers = {"X-Tableau-Auth": TOKEN}

response = requests.get(url, headers=headers)

if response.status_code == 200:

  with open("workbook.twbx", "wb") as file:

    file.write(response.content)

  print("Workbook downloaded successfully.")

else:

  print("Failed to download workbook:", response.text)

2. Export Image or PDF using REST API

The REST API doesn’t support direct PDF/image export, but you can use the Tableau Views API:

Export Image (PNG)

GET /api/3.15/sites/{site_id}/views/{view_id}/image

Export PDF

GET /api/3.15/sites/{site_id}/views/{view_id}/pdf

Example in Python (Export Image):

VIEW_ID = "your-view-id"

url = f"{TABLEAU_SERVER}/api/3.15/sites/{SITE_ID}/views/{VIEW_ID}/image"

response = requests.get(url, headers=headers)

if response.status_code == 200:

  with open("view.png", "wb") as file:

    file.write(response.content)

  print("Image exported successfully.")

else:

  print("Failed to export image:", response.text)

Example in Python (Export PDF):

url = f"{TABLEAU_SERVER}/api/3.15/sites/{SITE_ID}/views/{VIEW_ID}/pdf"

response = requests.get(url, headers=headers)

if response.status_code == 200:

  with open("view.pdf", "wb") as file:

    file.write(response.content)

  print("PDF exported successfully.")

else:

  print("Failed to export PDF:", response.text)

Alternative: Tableau Server Client (TSC)

Tableau Server Client (TSC) is a Python library that simplifies these operations.

Install it:

pip install tableauserverclient

Example (Download Workbook):

import tableauserverclient as TSC

TABLEAU_SERVER = "https://your-tableau-server"

USERNAME = "your-username"

PASSWORD = "your-password"

SITE_ID = "your-site-id"

WORKBOOK_ID = "your-workbook-id"

server = TSC.Server(TABLEAU_SERVER, use_server_version=True)

auth = TSC.TableauAuth(USERNAME, PASSWORD, SITE_ID)

with server.auth.sign_in(auth):

  workbook = server.workbooks.get_by_id(WORKBOOK_ID)

  server.workbooks.download(workbook.id, filepath="workbook.twbx")

  print("Workbook downloaded.")

Summary

Format

REST API

TSC Python SDK

Workbook (.twb/.twbx)

✅

✅

Image (.png)

✅

❌

PDF

✅

❌

If you’re working with Tableau Public, you can use Tableau’s JavaScript API for embedded views.

Let me know if you need help setting this up!

From Blogger iPhone client

Copilot rest api

GitHub Copilot REST API

GitHub Copilot primarily operates through integrations in IDEs (VS Code, JetBrains, Neovim, etc.), but GitHub does not provide a public REST API for Copilot at this time.

Alternative Options:

1. GitHub Copilot CLI (Experimental)

• GitHub is testing a CLI-based Copilot, which might expose API-like capabilities in the future.

2. Using OpenAI API Instead

• Since GitHub Copilot is built on OpenAI’s Codex model, you can use OpenAI’s GPT API (e.g., gpt-4-turbo) to achieve similar code-generation capabilities.

• Example OpenAI API call using Python:

import openai

response = openai.ChatCompletion.create(

  model="gpt-4-turbo",

  messages=[{"role": "user", "content": "Write a Python function to reverse a string"}]

)

print(response["choices"][0]["message"]["content"])

3. GitHub GraphQL & REST APIs for Repository Actions

• If you want automation related to GitHub repositories, issues, or PRs, you can use:

• GitHub REST API

• GitHub GraphQL API

Would you like help integrating OpenAI’s API as a Copilot alternative?

From Blogger iPhone client

Snowflake and Alteryx

Yes, Snowflake integrates with Alteryx, allowing users to connect, transform, and analyze data seamlessly. Alteryx provides built-in connectors to read from and write to Snowflake, enabling data preparation, blending, and advanced analytics.

How to Connect Alteryx with Snowflake

There are two primary ways to connect Alteryx to Snowflake:

1. Using the Alteryx Snowflake Connector (Recommended)

• Alteryx has a native Snowflake connector that simplifies the integration.

• This method supports bulk loading, query pushdown, and optimized performance.

Steps:

1. Open Alteryx Designer.

2. Drag a “Input Data” tool to the workflow.

3. Select Snowflake as the data source.

4. Enter the connection details:

• Server: <your_snowflake_account>.snowflakecomputing.com

• Database: <your_database>

• Warehouse: <your_compute_warehouse>

• Username & Password: <your_credentials>

5. Choose the table/query you want to use.

6. Click OK to establish the connection.

2. Using ODBC Driver for Snowflake

• If the native connector is not available, Alteryx can connect via Snowflake’s ODBC driver.

• This method provides greater flexibility but may require more setup.

Steps:

1. Install the Snowflake ODBC driver from the Snowflake website.

2. Configure an ODBC Data Source in Windows:

• Open ODBC Data Source Administrator.

• Add a new System DSN.

• Select Snowflake ODBC Driver.

• Enter your Snowflake account details.

3. In Alteryx:

• Drag a “Input Data” tool.

• Choose ODBC as the connection type.

• Select your configured Snowflake DSN.

• Enter a SQL query or select a table.

4. Click OK to connect.

Key Benefits of Using Snowflake with Alteryx

✅ Fast Query Processing – Snowflake’s optimized compute engine speeds up Alteryx workflows.

✅ Pushdown Processing – Alteryx can offload queries to Snowflake for better performance.

✅ Seamless Data Blending – Combine Snowflake data with other sources in Alteryx.

✅ Bulk Loading Support – Large datasets can be written back to Snowflake efficiently.

✅ Secure & Scalable – Snowflake handles enterprise-grade security and scaling automatically.

Common Use Cases

• Data Preparation & Transformation – Load raw data from Snowflake, clean it in Alteryx, and write back transformed data.

• Predictive Analytics & ML – Use Alteryx for advanced modeling while leveraging Snowflake’s storage.

• Business Intelligence (BI) Enablement – Process Snowflake data in Alteryx before sending it to BI tools like Tableau or Power BI.

Would you like a specific example or workflow template for Snowflake-Alteryx integration?

From Blogger iPhone client

Snowflake and spark integration

Yes, you can use Apache Spark and Databricks with Snowflake to enhance data processing and analytics. There are multiple integration methods depending on your use case.

1. Using Apache Spark with Snowflake

• Snowflake provides a Spark Connector that enables bi-directional data transfer between Snowflake and Spark.

• The Snowflake Connector for Spark supports:

• Reading data from Snowflake into Spark DataFrames

• Writing processed data from Spark back to Snowflake

• Query pushdown optimization for performance improvements

Example: Connecting Spark to Snowflake

from pyspark.sql import SparkSession

# Initialize Spark session

spark = SparkSession.builder.appName("SnowflakeIntegration").getOrCreate()

# Define Snowflake connection options

sf_options = {

  "sfURL": "https://your-account.snowflakecomputing.com",

  "sfDatabase": "YOUR_DATABASE",

  "sfSchema": "PUBLIC",

  "sfWarehouse": "YOUR_WAREHOUSE",

  "sfUser": "YOUR_USERNAME",

  "sfPassword": "YOUR_PASSWORD"

}

# Read data from Snowflake into Spark DataFrame

df = spark.read \

  .format("snowflake") \

  .options(**sf_options) \

  .option("dbtable", "your_table") \

  .load()

df.show()

2. Using Databricks with Snowflake

Databricks, which runs on Apache Spark, can also integrate with Snowflake via:

• Databricks Snowflake Connector (similar to Spark’s connector)

• Snowflake’s Native Query Engine (for running Snowpark functions)

• Delta Lake Integration (for advanced lakehouse architecture)

Integration Benefits

• Leverage Databricks’ ML/AI Capabilities → Use Spark MLlib for machine learning.

• Optimize Costs → Use Snowflake for storage & Databricks for compute-intensive tasks.

• Parallel Processing → Use Databricks’ Spark clusters to process large Snowflake datasets.

Example: Querying Snowflake from Databricks

# Configure Snowflake connection in Databricks

sfOptions = {

  "sfURL": "https://your-account.snowflakecomputing.com",

  "sfDatabase": "YOUR_DATABASE",

  "sfSchema": "PUBLIC",

  "sfWarehouse": "YOUR_WAREHOUSE",

  "sfUser": "YOUR_USERNAME",

  "sfPassword": "YOUR_PASSWORD"

}

# Read Snowflake table into a Databricks DataFrame

df = spark.read \

  .format("snowflake") \

  .options(**sfOptions) \

  .option("dbtable", "your_table") \

  .load()

df.display()

When to Use Snowflake vs. Databricks vs. Spark?

Feature

Snowflake

Databricks

Apache Spark

Primary Use Case

Data warehousing & SQL analytics

ML, big data processing, ETL

Distributed computing, real-time streaming

Storage

Managed cloud storage

Delta Lake integration

External (HDFS, S3, etc.)

Compute Model

Auto-scale compute (separate from storage)

Spark-based clusters

Spark-based clusters

ML/AI Support

Snowpark (limited ML support)

Strong ML/AI capabilities

Native MLlib library

Performance

Fast query execution with optimizations

Optimized for parallel processing

Needs tuning for performance

Final Recommendation

• Use Snowflake for structured data storage, fast SQL analytics, and ELT workflows.

• Use Databricks for advanced data engineering, machine learning, and big data processing.

• Use Spark if you need real-time processing, batch jobs, or a custom big data pipeline.

Would you like an example for a specific integration use case?

From Blogger iPhone client

Snowflake data warehouse

What is Snowflake Data?

Snowflake is a cloud-based data platform that provides a fully managed data warehouse-as-a-service (DWaaS). It enables businesses to store, process, and analyze large volumes of structured and semi-structured data efficiently. Unlike traditional on-premises databases, Snowflake is designed for the cloud, offering scalability, performance, and ease of use without requiring infrastructure management.

Key Features of Snowflake:

1. Multi-Cloud Support – Runs on AWS, Azure, and Google Cloud.

2. Separation of Compute and Storage – Allows independent scaling of processing power and storage.

3. Pay-as-You-Go Pricing – Charges based on actual usage.

4. Zero-Copy Cloning – Enables instant duplication of databases without extra storage costs.

5. Automatic Optimization – Handles performance tuning automatically.

6. Multi-Tenancy – Allows multiple users and workloads to run concurrently.

Competitors of Snowflake

Snowflake competes with several cloud-based and on-premises data warehousing solutions, including:

1. Cloud-Based Competitors:

• Google BigQuery – Serverless data warehouse with real-time analytics, integrated with Google Cloud.

• Amazon Redshift – Fully managed data warehouse from AWS, optimized for complex queries.

• Microsoft Azure Synapse Analytics – Combines big data and data warehousing with integrated analytics.

• Databricks – Unified analytics platform built on Apache Spark, optimized for AI/ML and big data.

2. On-Premises & Hybrid Competitors:

• Teradata – High-performance on-premises and hybrid cloud data warehousing solution.

• Oracle Autonomous Data Warehouse – Cloud-based and on-premises data warehouse with automation features.

• IBM Db2 Warehouse – Enterprise data warehouse with AI-powered insights.

3. Open-Source & Alternative Solutions:

• ClickHouse – Open-source columnar database designed for fast analytics.

• Apache Druid – Real-time analytics database for high-speed queries.

• Presto (Trino) – SQL query engine for big data analytics.

How Snowflake Stands Out

Snowflake’s major advantage lies in its simplicity, scalability, and cloud-native architecture, making it easier to use compared to traditional solutions like Teradata or Oracle. However, competitors like Google BigQuery and Amazon Redshift challenge it with their deep cloud integration and cost-effective pricing models.



From Blogger iPhone client

Airline

As of February 2025, JetBlue Airways’ leadership team includes:

• Chief Executive Officer (CEO): Joanna Geraghty

• President: Marty St. George

• Chairman of the Board: Peter Boneparth

Joanna Geraghty was appointed CEO in February 2024, becoming the first woman to lead a major U.S. airline. Marty St. George returned to JetBlue as President in February 2024, overseeing the airline’s commercial functions. Peter Boneparth has served as Chairman since May 2020. 

Jet Blue as a business

JetBlue Airways has established itself as a distinctive player in the airline industry by blending cost-effective operations with enhanced passenger experiences. Here’s an overview of its business model, competitive standing, fleet, and technological advancements:

Business Model and Market Position

JetBlue operates a hybrid model that combines elements of low-cost carriers with services typical of full-service airlines. This approach allows the airline to offer competitive fares while providing value-added amenities. Key aspects of JetBlue’s business model include:

• Customer-Centric Services: Passengers enjoy complimentary in-flight entertainment, free Wi-Fi, and snacks, enhancing the overall travel experience.

• Strategic Route Network: Serving over 100 destinations across the U.S., Caribbean, and Latin America, JetBlue focuses on high-demand markets to maximize efficiency.

• Loyalty Program: The TrueBlue program incentivizes repeat business, contributing significantly to customer retention.

Despite these strengths, JetBlue faces challenges in profitability. The airline has reported losses in recent years, prompting strategic shifts such as reducing unprofitable routes and enhancing premium offerings to attract higher-paying customers. 

Fleet and Technological Advancements

JetBlue’s fleet strategy emphasizes modern, fuel-efficient aircraft to improve operational performance and reduce environmental impact. Notable initiatives include:

• Modern Fleet Composition: The airline operates a young fleet, primarily consisting of Airbus A320 and A321 models, with an average age of approximately 5.5 years. This focus on newer aircraft enhances fuel efficiency and reliability. 

• Sustainable Practices: JetBlue has committed to purchasing sustainable aviation fuel and aims to achieve net-zero carbon emissions by 2040, a decade ahead of industry targets. 

• In-Flight Connectivity: The airline offers free high-speed Wi-Fi on all flights, recognizing the growing importance of connectivity for passengers. 

Competitive Standing

In the competitive airline landscape, JetBlue distinguishes itself through superior customer service and innovative offerings. However, it faces competition from both low-cost carriers and major airlines. To strengthen its market position, JetBlue is:

• Expanding Premium Services: The introduction of the ‘Mint’ business class and plans to open exclusive airport lounges in New York and Boston aim to attract premium travelers. 

• Strategic Partnerships: Codeshare agreements with international airlines expand JetBlue’s network and offer passengers more travel options. 

While JetBlue’s unique approach offers a competitive edge, the airline continues to navigate challenges related to profitability and market share. Ongoing efforts to optimize operations and enhance service offerings are central to its strategy in the evolving aviation industry.

As of early 2025, JetBlue Airways operates a fleet of approximately 290 aircraft, comprising the following types:

• Airbus A320-200: 130 aircraft

• Airbus A321-200: 63 aircraft

• Airbus A321neo: 24 aircraft

• Airbus A220-300: 15 aircraft

• Embraer E190: 48 aircraft

JetBlue is in the process of modernizing its fleet, focusing on enhancing fuel efficiency and passenger comfort. The airline has been phasing out its Embraer E190 aircraft, replacing them with the more efficient Airbus A220-300. Additionally, JetBlue has introduced the Airbus A321 Long Range (A321LR) to support its transatlantic services, featuring 114 seats, including 24 Mint Suites®. 

This strategic fleet renewal aims to improve operational performance and align with JetBlue’s commitment to sustainability.

comparison

As of December 2024, Qatar Airways operates a diverse fleet of approximately 255 aircraft, comprising both narrow-body and wide-body models. The fleet includes:

Narrow-Body Aircraft:

• Airbus A320-200: 28 aircraft

• Boeing 737 MAX 8: 9 aircraft

Wide-Body Aircraft:

• Airbus A330-200: 3 aircraft

• Airbus A330-300: 7 aircraft

• Airbus A350-900: 34 aircraft

• Airbus A350-1000: 24 aircraft

• Airbus A380-800: 8 aircraft

• Boeing 777-200LR: 7 aircraft

• Boeing 777-300ER: 57 aircraft

• Boeing 787-8: 31 aircraft

• Boeing 787-9: 19 aircraft

Qatar Airways has also placed orders for additional aircraft to further modernize and expand its fleet:

• Airbus A321neo: 50 orders, with deliveries expected to begin in 2026. These will replace the existing A320-200s.

• Boeing 737 MAX 10: 25 orders, with options for an additional 25.

• Boeing 777-9: 60 orders, with deliveries anticipated by 2026.

This strategic expansion underscores Qatar Airways’ commitment to maintaining a modern and efficient fleet, enhancing passenger comfort, and optimizing operational performance.

 As of early 2025, here’s a comparative overview of the fleet composition for JetBlue Airways, Qatar Airways, United Airlines, Air Canada, American Airlines, and Emirates:

Aircraft Type

JetBlue Airways

Qatar Airways

United Airlines

Air Canada

American Airlines

Emirates

Airbus A220-300

44

—

—

—

—

—

Airbus A320-200

11

—

96

—

48

—

Airbus A321-200

35

—

65

—

218

—

Airbus A321neo

10

—

23

—

70

—

Airbus A330-200

—

3

—

8

—

—

Airbus A330-300

—

7

—

12

—

—

Airbus A350-900

—

34

—

—

—

—

Airbus A350-1000

—

24

—

—

—

—

Airbus A380-800

—

8

—

—

—

118

Boeing 737-800

—

—

141

39

303

—

Boeing 737 MAX 8

—

9

30

28

42

—

Boeing 737 MAX 9

—

—

70

—

30

—

Boeing 737 MAX 10

—

—

—

—

—

—

Boeing 747-8

—

—

—

—

—

—

Boeing 757-200

—

—

40

—

34

—

Boeing 757-300

—

—

21

—

—

—

Boeing 767-300ER

—

—

37

—

24

—

Boeing 767-400ER

—

—

16

—

—

—

Boeing 777-200

—

—

19

—

47

—

Boeing 777-200ER

—

—

55

—

47

—

Boeing 777-200LR

—

7

—

—

—

—

Boeing 777-300ER

—

57

22

19

20

133

Boeing 787-8

—

—

12

8

24

—

Boeing 787-9

—

—

38

29

25

—

Boeing 787-10

—

—

21

—

20

—

Note: The numbers above are approximate and based on available data as of early 2025. For the most current and detailed fleet information, please refer to the respective airlines’ official communications or financial disclosures.

This matrix provides a snapshot of the diverse aircraft types and their distribution across these major airlines, reflecting their strategic choices in fleet composition to meet various operational and market demands.

 # As of early 2025, here’s a comparative overview of the fleet composition for JetBlue Airways, Qatar Airways, United Airlines, Air Canada, American Airlines, and Emirates:

Aircraft Type

JetBlue Airways

Qatar Airways

United Airlines

Air Canada

American Airlines

Emirates

Airbus A220-300

44

—

—

—

—

—

Airbus A320-200

11

—

96

—

48

—

Airbus A321-200

35

—

65

—

218

—

Airbus A321neo

10

—

23

—

70

—

Airbus A330-200

—

3

—

8

—

—

Airbus A330-300

—

7

—

12

—

—

Airbus A350-900

—

34

—

—

—

—

Airbus A350-1000

—

24

—

—

—

—

Airbus A380-800

—

8

—

—

—

118

Boeing 737-800

—

—

141

39

303

—

Boeing 737 MAX 8

—

9

30

28

42

—

Boeing 737 MAX 9

—

—

70

—

30

—

Boeing 737 MAX 10

—

—

—

—

—

—

Boeing 747-8

—

—

—

—

—

—

Boeing 757-200

—

—

40

—

34

—

Boeing 757-300

—

—

21

—

—

—

Boeing 767-300ER

—

—

37

—

24

—

Boeing 767-400ER

—

—

16

—

—

—

Boeing 777-200

—

—

19

—

47

—

Boeing 777-200ER

—

—

55

—

47

—

Boeing 777-200LR

—

7

—

—

—

—

Boeing 777-300ER

—

57

22

19

20

133

Boeing 787-8

—

—

12

8

24

—

Boeing 787-9

—

—

38

29

25

—

Boeing 787-10

—

—

21

—

20

—

Note: The numbers above are approximate and based on available data as of early 2025. For the most current and detailed fleet information, please refer to the respective airlines’ official communications or financial disclosures.

This matrix provides a snapshot of the diverse aircraft types and their distribution across these major airlines, reflecting their strategic choices in fleet composition to meet various operational and market demands.

# # As of early 2025, here’s a comparative overview of the fleet composition for JetBlue Airways, Qatar Airways, United Airlines, Air Canada, American Airlines, and Emirates:

| **Aircraft Type** | **JetBlue Airways** | **Qatar Airways** | **United Airlines** | **Air Canada** | **American Airlines** | **Emirates** |

|:-:|:-:|:-:|:-:|:-:|:-:|:-:|

| Airbus A220-300 | 44 | — | — | — | — | — |

| Airbus A320-200 | 11 | — | 96 | — | 48 | — |

| Airbus A321-200 | 35 | — | 65 | — | 218 | — |

| Airbus A321neo | 10 | — | 23 | — | 70 | — |

| Airbus A330-200 | — | 3 | — | 8 | — | — |

| Airbus A330-300 | — | 7 | — | 12 | — | — |

| Airbus A350-900 | — | 34 | — | — | — | — |

| Airbus A350-1000 | — | 24 | — | — | — | — |

| Airbus A380-800 | — | 8 | — | — | — | 118 |

| Boeing 737-800 | — | — | 141 | 39 | 303 | — |

| Boeing 737 MAX 8 | — | 9 | 30 | 28 | 42 | — |

| Boeing 737 MAX 9 | — | — | 70 | — | 30 | — |

| Boeing 737 MAX 10 | — | — | — | — | — | — |

| Boeing 747-8 | — | — | — | — | — | — |

| Boeing 757-200 | — | — | 40 | — | 34 | — |

| Boeing 757-300 | — | — | 21 | — | — | — |

| Boeing 767-300ER | — | — | 37 | — | 24 | — |

| Boeing 767-400ER | — | — | 16 | — | — | — |

| Boeing 777-200 | — | — | 19 | — | 47 | — |

| Boeing 777-200ER | — | — | 55 | — | 47 | — |

| Boeing 777-200LR | — | 7 | — | — | — | — |

| Boeing 777-300ER | — | 57 | 22 | 19 | 20 | 133 |

| Boeing 787-8 | — | — | 12 | 8 | 24 | — |

| Boeing 787-9 | — | — | 38 | 29 | 25 | — |

| Boeing 787-10 | — | — | 21 | — | 20 | — |

# Note: The numbers above are approximate and based on available data as of early 2025. For the most current and detailed fleet information, please refer to the respective airlines’ official communications or financial disclosures.

# This matrix provides a snapshot of the diverse aircraft types and their distribution across these major airlines, reflecting their strategic choices in fleet composition to meet various operational and market demands.

From Blogger iPhone client

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

From Blogger iPhone client

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).



From Blogger iPhone client

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.

From Blogger iPhone client

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?

From Blogger iPhone client

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!

From Blogger iPhone client

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