ETL tools

Several tools compete with Alteryx in the data preparation, analytics, and ETL (Extract, Transform, Load) space. Depending on your use case—whether it’s no-code/low-code analytics, data wrangling, workflow automation, or machine learning—the main competitors include:





🔝 

Top Alteryx Competitors (Grouped by Focus Area)




🧩 

Visual ETL & Data Prep Platforms



These are closest to Alteryx in terms of drag-and-drop UI and use cases:


  • Microsoft Power BI (with Power Query / Dataflows) – especially strong for business users in the MS ecosystem.
  • Tableau Prep – good for visual data prep, integrates tightly with Tableau for BI.
  • Knime – open-source, node-based workflow platform for analytics and ML; very similar in structure to Alteryx.
  • RapidMiner – visual data science workflows, especially for machine learning.
  • Dataiku – collaborative data science platform with visual flows and code support (Python, R, SQL).
  • Talend – strong in ETL, data integration, and governance; more enterprise-focused.






☁️ 

Cloud-Native & Big Data Platforms



For cloud-first or data engineering workloads:


  • Apache NiFi – open-source, for real-time data flow management.
  • AWS Glue – serverless data integration for the AWS ecosystem.
  • Google Cloud Dataflow / Dataprep (by Trifacta) – similar to Alteryx, visual UI for data cleaning.
  • Azure Data Factory – good for large-scale pipeline orchestration in Azure.
  • Databricks – especially powerful for advanced analytics, big data, and ML; more code-centric.






🔍 

AI-Powered or Advanced Analytics Platforms



  • SAS – established player in analytics and data prep with visual tools.
  • IBM Watson Studio – enterprise ML and data science platform.
  • Domino Data Lab – enterprise-grade model development and deployment.






🤔 

Choosing the Right Competitor Depends On:



  • User skill level (Business user vs. Data Scientist vs. Engineer)
  • Cloud vs. On-prem
  • Cost and licensing model
  • Integration needs (ERP systems, data lakes, BI tools)
  • Governance and scalability





Would you like a side-by-side comparison table (e.g., Alteryx vs. Knime vs. Dataiku) or recommendations based on your company’s stack?


From Blogger iPhone client

AI detect motion





✅ Step 4: Postprocessing & Labeling



After model prediction:


  • Annotate video with cv2.putText
  • Save to disk with cv2.VideoWriter
  • Stream via Flask or Streamlit for live dashboards






🧪 4. 

Datasets for Training / Fine-Tuning








💡 5. Sample Use Cases



From Blogger iPhone client

Ai machine vision analytics

https://social-sharing-platform.web.app/c006a2788455495ba97984a795795a16_6Dq6l2yRkpsthkwB4ltz_qatar

From Blogger iPhone client

Build

Guess what I built at the AI Quick Build experience powered by Gemini and Imagen!


https://social-sharing-platform.web.app/c006a2788455495ba97984a795795a16_6Dq6l2yRkpsthkwB4ltz_qatar

From Blogger iPhone client

Nano bot drones

https://youtube.com/shorts/G3EeG470h9c?si=SrVQLOTgVs9Uc5mk

From Blogger iPhone client

Flight Watching Data

Using data from FlightWatching, which specializes in real-time aircraft operational monitoring (e.g., engine data, component status, flight telemetry), can help reduce financial costs for airlines by enabling predictive maintenance, operational efficiency, and smarter logistics. Below are strategic use cases—especially valuable to airlines like Qatar Airways with complex technical supply chains.



✈️ Use Cases for Financial Cost Reduction Using FlightWatching Data






1. 

Predictive Maintenance to Avoid AOG (Aircraft on Ground)



Goal: Reduce costs due to unplanned maintenance and aircraft downtime.


How:




  • Use real-time engine and systems data to predict component failure (e.g., hydraulic pump, actuator, APU).
  • Trigger just-in-time part replacement, avoiding unnecessary checks or premature replacements.



Financial Impact:




  • Avoid emergency repairs and high-cost AOG incidents (each AOG event can cost $10,000–$150,000/day).
  • Reduce unnecessary inventory holding of rarely failing parts.






2. 

Parts Logistics Optimization



Goal: Avoid high-cost expedited freight.


How:




  • Match component health data (from FlightWatching) with logistics planning.
  • If a part shows signs of degradation, trigger regular freight instead of urgent shipping.



Bonus: Combine with cargo & freight forwarder data to schedule parts delivery with existing cargo routes.





3. 

Warranty and SLA Management



Goal: Enforce warranties and reduce costs from out-of-contract repairs.


How:




  • Use sensor and maintenance data to track operational hours, failure patterns, and usage history.
  • Identify early failures to claim warranty from OEMs (e.g., for engines, landing gear components).



Financial Impact:




  • Improve warranty recovery rates.
  • Support root cause analysis to avoid recurring repair costs.






4. 

Flight Efficiency & Fuel Cost Monitoring



Goal: Reduce fuel and operational inefficiencies.


How:




  • Monitor fuel burn rate trends and anomalies in engine performance using telemetry.
  • Detect unnecessary APU usage or idling patterns during taxiing or delays.



Financial Impact:




  • Identify inefficient flight legs or aircraft.
  • Reduce fuel wastage through better engine tuning and operational policies.






5. 

Optimizing Spare Parts Inventory



Goal: Reduce working capital tied up in unused inventory.


How:




  • Use degradation trends to estimate actual demand for parts.
  • Build dynamic stocking models using usage and wear-rate analytics.



Result:




  • Avoid overstocking parts with long lead times but low failure probability.
  • Prioritize stocking for high-wear parts.






6. 

Enhanced Reliability Reporting to Reduce Lease Penalties



Goal: Avoid penalties or high redelivery costs at end of lease.


How:




  • Use FlightWatching data to show compliance with engine performance and maintenance metrics.
  • Prove the component/aircraft is within operational specs.






7. 

Route & Fleet Planning Insights



Goal: Assign the right aircraft to the right mission.


How:




  • Combine environmental/flight data with system strain insights.
  • Avoid assigning heavily used or degrading aircraft to long-haul flights.



Financial Impact:




  • Reduce wear/tear and maintenance costs by optimizing aircraft utilization.






🚀 Strategic Integration Example



From Blogger iPhone client


Data Source

How to Use

FlightWatching API

Pull live maintenance telemetry

ERP / Maintenance CMS

Match part status with procurement and logistics

Freight Forwarder API

Route parts cost-effectively based on urgency and availability

BI Dashboard

Visualize trends: part failures, cost savings, delay causes

Supply Chain Monitoring system for Airline

Certainly. Here’s a structured breakdown of the advantages and requirements of implementing spend analytics for the supply chain department (technical parts) of an airline like Qatar Airways, with an added emphasis on integrating cargo and freight forwarder data to reduce expedition costs.





✅ 

Advantages of Spend Analytics in Supply Chain (Technical Parts - Airline)




1. 

Cost Optimization



  • Identify excessive or non-strategic spend on technical parts and maintenance services.
  • Consolidate suppliers to negotiate better pricing and payment terms.
  • Reduce dependency on emergency procurement or last-minute orders, which often cost more.




2. 

Inventory Management Efficiency



  • Analyze historical consumption patterns to optimize stock levels.
  • Avoid overstocking or stockouts of high-value aircraft components.




3. 

Supplier Performance Insights



  • Assess delivery times, quality issues, compliance, and cost trends per supplier.
  • Support decision-making for supplier rationalization or diversification.




4. 

Category Management



  • Segment spend by part category (e.g., avionics, hydraulics, engine components).
  • Identify opportunities for bundling or volume purchasing.




5. 

Strategic Sourcing



  • Use analytics to drive sourcing strategies based on total cost of ownership (TCO).
  • Identify alternative suppliers for critical components to minimize risk.




6. 

Reduction in Expedition Costs



  • By forecasting needs and aligning logistics proactively, avoid urgent shipments (air freight, charter).
  • Minimize “AOG” (Aircraft on Ground) scenarios due to parts unavailability.






📈 

Linking Cargo & Freight Forwarders Data




Key Benefits:



  1. Proactive Logistics Planning
  3. Align parts procurement with real-time cargo capacity and freight schedules.
  4. Reduce reliance on expedited or chartered logistics.

  6. Visibility & Control
  8. Track shipment statuses and adjust based on criticality and delivery windows.
  9. Match shipping lead times with aircraft maintenance schedules.

  11. Cost Avoidance
  13. Identify inefficient routes or costly freight decisions.
  14. Optimize for bulk or consolidated shipments instead of fragmented urgent orders.

  16. Vendor Coordination
  18. Improve collaboration with freight forwarders on optimal transport modes and warehouse availability.
  19. Predict congestion or seasonal delays and reroute accordingly.






🧩 

Requirements for Implementing Spend Analytics




1. 

Data Integration



  • Integrate ERP (Oracle Fusion or EBS), MRO systems (like AMOS or Ramco), procurement systems, and logistics data sources.
  • Real-time or batch data flow from freight forwarders, cargo divisions, and customs.




2. 

Data Cleansing & Standardization



  • Normalize supplier names, part numbers, units of measure, and currency.
  • Ensure consistency in historical spend data across cost centers and GL codes.




3. 

Category Taxonomy



  • Develop a clear and standardized part classification schema (e.g., ATA chapter-based).
  • Assign spend to categories like engines, avionics, consumables, etc.




4. 

Analytics Tools & Dashboards



  • BI platforms (Power BI, Tableau, or Oracle Analytics Cloud) to visualize spend patterns.
  • KPI dashboards for lead time, cost per shipment, supplier scorecards.




5. 

Cross-Functional Collaboration



  • Align supply chain, engineering, finance, and cargo departments on data governance.
  • Define ownership of insights and actions (e.g., procurement savings, logistics planning).




6. 

Predictive Capabilities



  • Use machine learning to forecast demand for parts and anticipated freight needs.
  • Simulate cost impacts of sourcing vs. delivery trade-offs.






🛫 Example: Use Case in Qatar Airways



  • Problem: Frequent AOG situations due to delayed delivery of critical engine components, requiring last-minute expedited freight at a premium.
  • Solution:
  • Use spend analytics to identify patterns in emergency shipments and their root causes.
  • Integrate freight forwarders’ route data to anticipate delays or congestion.
  • Create a predictive replenishment model to pre-position parts at hubs based on aircraft routing.





Would you like this formatted into a presentation slide or summarized for a report/proposal?


From Blogger iPhone client

Fuel efficiency project using higher altitude

https://youtube.com/shorts/zeZddwimLc8?si=1iG1CdI0_Uo9zfnP

From Blogger iPhone client

2025 Airline Initiatives

Airlines worldwide are launching a variety of career initiatives in 2025 and 2026 to address workforce shortages, enhance diversity, and prepare for future growth. Here’s an overview of notable programs and strategies:





✈️ Pilot Training & Career Pathways




Delta Air Lines – Propel Career Path Program



Delta’s Propel program offers a direct path to a pilot career, providing mentorship from Delta pilots, a qualified job offer with Delta Air Lines and its subsidiary Endeavor Air, and a seamless transition through a single interview. 



United Airlines – Aviate Academy



United’s Aviate Academy, located in Goodyear, Arizona, is a flight training school owned and operated by United Airlines. It serves as the primary training facility for United Aviate, United’s pilot career development program, aiming to train 5,000 pilots by 2030, with at least 50% women or people of color. 



Aer Lingus – Future Pilot Programme



Aer Lingus has reopened its Future Pilot Programme, offering aspiring aviators the chance to train and fly for the airline. The 14-month training course is fully sponsored, aiming to develop 90 pilots over five years. Applications are open until February 10, 2025. The airline is encouraging more female applicants to improve diversity. 





👩‍💼 Graduate & Internship Programs




Delta Air Lines – Student & Early Careers



Delta offers internship, MBA, co-op, and rotational opportunities for students and recent graduates. These programs provide exposure to various departments and include benefits like travel privileges and professional development. 



Southwest Airlines – Campus Reach Program



Southwest’s Campus Reach program identifies and engages future employees at an early age, offering internships and full-time opportunities across various departments. Interns receive compensation, travel benefits, and housing stipends. 



Jetstar – 2026 Graduate Program



Jetstar is recruiting new graduates for its 2026 Graduate Program, focusing on developing future leaders within the airline. Applications have closed, but the program reflects Jetstar’s commitment to nurturing talent. 





🧰 Technical & Maintenance Training




American Airlines – Aviation Maintenance Partnership



American Airlines has partnered with George T. Baker Aviation Technical College in Miami to support students pursuing careers in aviation maintenance, addressing the growing demand for skilled technicians. 



Cathay Pacific – Engineering Graduate Trainee Programme



Cathay Pacific offers a 36-month Engineering Graduate Trainee Programme with four different pathways, providing unique experiences and exposure to becoming a professional engineer within the airline. 





🌍 Diversity & Inclusion Initiatives




United Airlines – Commitment to Diversity



United Airlines’ Aviate Academy aims to train 5,000 pilots by 2030, with at least 50% women or people of color, addressing the need for greater diversity in the cockpit. 



EasyJet – Recruitment Drive for Armed Forces Veterans



EasyJet has initiated a recruitment drive aimed at employing veterans, encouraging them to utilize their highly transferable skills in various roles, including engineering and cabin crew. This initiative is part of EasyJet’s effort to hire more older workers. 





📈 Industry-Wide Hiring Trends




Lufthansa Group – Hiring Plans for 2025



Lufthansa Group aims to hire approximately 10,000 new employees in 2025, focusing on flight attendants, ground staff, technical experts, administrative staff, and pilots. The recruitment emphasis is on subsidiaries like Lufthansa Technik, Austrian Airlines, and Eurowings. 



Pilot Hiring Outlook



The airline industry is navigating a period of transformation marked by fluctuating hiring patterns, production delays, and a persistent pilot shortage. After a significant slowdown in pilot recruitment in 2024, industry experts project a return to more normalized hiring levels in 2025. 




If you’re interested in specific programs or opportunities, feel free to ask for more details!


From Blogger iPhone client


The global adoption of technological innovation and robotics in 2025 and 2026 is accelerating across industries, driven by advancements in artificial intelligence (AI), automation, and the need for sustainable solutions. Here’s an overview of key trends and developments:





🤖 Key Robotics Trends in 2025–2026




1. 

AI-Driven Robotics



Robots are increasingly integrating AI capabilities, enabling them to perform complex tasks with greater autonomy. This includes analytical AI for data processing, physical AI for real-world interactions, and generative AI for adaptive learning. Such advancements are enhancing robot efficiency in manufacturing, healthcare, and service industries. 



2. 

Humanoid Robots



Humanoid robots are being developed to operate in environments designed for humans, such as warehouses and factories. Companies like Tesla, Agility Robotics, and Figure are leading in this space, aiming to address labor shortages and improve operational efficiency. 



3. 

Sustainable Robotics



Robotics is contributing to sustainability goals by improving energy efficiency and reducing waste. For instance, robots are being used in precision agriculture to minimize chemical use and in manufacturing to optimize resource utilization.



4. 

Collaborative Robots (Cobots)



Cobots are designed to work alongside humans, enhancing productivity and safety. Their adaptability makes them suitable for small and medium-sized enterprises, facilitating automation without extensive infrastructure changes. 



5. 

Digital Twins



The use of digital twins—virtual replicas of physical systems—is enabling real-time monitoring and optimization of robotic operations. This technology is particularly beneficial in manufacturing and logistics for predictive maintenance and process improvements.





🌍 Global Market Outlook



  • Market Growth: The global robotics market is projected to grow from $71.78 billion in 2025 to $150.84 billion by 2030, reflecting a compound annual growth rate (CAGR) of 16.0%.  
  • Intelligent Robotics: The intelligent robotics segment is expected to expand from $13.99 billion in 2025 to $50.33 billion by 2030, with a CAGR of 29.2%.  
  • Service Robots: The service robot market, including applications in healthcare and hospitality, is anticipated to reach $24.64 billion by 2026.  






🏭 Industry Applications



  • Manufacturing: AI and robotics are being utilized for predictive maintenance, reducing unplanned downtime and saving costs. Companies like Coca-Cola and Siemens Energy are adopting these technologies to enhance operational efficiency.  
  • Healthcare: Robotic systems are increasingly used in surgeries and patient care, improving precision and reducing recovery times. The medical robotics market is projected to reach $20.6 billion by 2026.  
  • Agriculture: Robots are aiding in tasks like planting, harvesting, and monitoring crop health, contributing to sustainable farming practices and increased yields.






🚀 Future Outlook



As technological advancements continue, the integration of robotics across various sectors is expected to deepen, leading to increased efficiency, sustainability, and innovation. However, challenges such as high initial costs, workforce adaptation, and ethical considerations will need to be addressed to ensure equitable and effective adoption.





Sky runner payload system robot

https://youtube.com/shorts/c7Vo3ZEXfYU?si=M7bvyZJnYSsas1jB

From Blogger iPhone client


https://youtube.com/shorts/ttS0GwHszWU?si=d4smtiYhv-dOnUe7




Bradford KPI for HR

The Bradford Factor is a widely used HR tool for measuring employee absenteeism, particularly short-term and frequent absences. Its reliability and effectiveness depend on how it’s used and the context of the organization. Here’s a breakdown of its reliability:





What the Bradford Factor Measures



It calculates a score based on the formula:


B = S² × D


  • S = number of absence instances (spells)
  • D = total number of days absent



This emphasizes frequent short absences more than occasional long ones.





Reliability: Pros



  1. Objective and Simple: Provides a clear, numeric view of absence patterns.
  2. Highlights Frequent Absences: Helps identify patterns that might indicate issues like disengagement, burnout, or abuse.
  3. Consistency: If applied uniformly, it can support fair policy enforcement.






Reliability: Cons and Limitations



  1. Lacks Context: It doesn’t account for legitimate or medically necessary absences (e.g., chronic illness, disability, mental health).
  2. Can Be Misused: Rigid application can lead to unfair disciplinary actions and deteriorate morale.
  3. Not Predictive: It tracks past absences but doesn’t explain why they occurred or predict future behavior.
  4. One-size-fits-all Risk: Doesn’t differentiate between job roles, health statuses, or personal circumstances.






Best Practice for Use



  • Supplement, don’t replace manager judgment and HR discretion.
  • Use in combination with other data (e.g., performance reviews, wellness reports).
  • Apply with sensitivity, especially for employees with protected medical conditions (to avoid legal risks).






Summary



The Bradford Factor is moderately reliable as an early warning or flagging system for absenteeism patterns but is not sufficient on its own for making disciplinary or performance decisions. Its value comes from how thoughtfully and fairly it’s applied within an organization’s broader HR strategy.


Cleaning robots

https://youtube.com/shorts/7aoWJ_chjxw?si=0pmrcifMnyzi8hY-

From Blogger iPhone client

Generating energy

https://youtube.com/shorts/M54tK3lFcVs?si=hjySUgbFiNuAOZCs

From Blogger iPhone client

Synthetic data modeling

In addition to faker, there are several Python libraries and tools that can generate reliable synthetic data, depending on the type (tabular, time-series, image, text, etc.) and the level of realism or constraints you need. Here’s a breakdown by use case:





1. General Tabular Data



  • SDV (Synthetic Data Vault)
  • State-of-the-art for realistic synthetic tabular data.
  • Uses statistical modeling or deep learning (GANs, CTGAN, TVAE).
  • Ideal for privacy-preserving data generation.

  • scikit-learn’s make_classification, make_regression, make_blobs
  • Useful for ML model prototyping.
  • Generates labeled synthetic data for classification/regression problems.

  • [Synthpop (via RPy2 for Python)]
  • If you want to use R’s synthpop from Python.
  • Great for statistical privacy-preserving synthetic data.






2. Time-Series Data



  • Timesynth
  • For generating synthetic time series data.
  • Supports autoregressive signals, harmonic signals, and noise.

  • Sktime
  • Has utilities for time series generation, often used in research.







3. Image / Vision Data








4. Text / NLP Data








5. Domain-Specific Libraries



  • Pydbgen
  • Like Faker, but tailored for fake databases.


  • DataSynthesizer
  • Generates synthetic data with differential privacy guarantees.


  • Mimesis
  • Similar to Faker, supports multiple locales and categories.






Would you like suggestions based on a specific type of data or project goal (e.g., simulating customers, financial records, health data)?


From Blogger iPhone client

Find phone

https://youtube.com/shorts/Y0zxyvZdgCc?si=BgpaMPtNnJm2aOy5




From Blogger iPhone client

Talent and hard work


https://youtube.com/shorts/NCz2XTk-Mlw?si=8BsDdi2RxfndKX_T




From Blogger iPhone client

Airline maintenance demand and capacity

To measure demand and capacity for aircraft maintenance, allocate technical staff efficiently, and minimize overall downtime, you need to gather data across several categories: aircraft operations, maintenance needs, workforce management, and resource availability.





1. 

Aircraft Operations Data (Demand Drivers)



These determine when and how often maintenance will be needed.


  • Flight hours and flight cycles per aircraft
  • Scheduled route plans (frequency, duration, utilization)
  • Aircraft type and age
  • Regulatory maintenance intervals (A-check, C-check, D-check, etc.)
  • Unscheduled maintenance events (e.g., faults reported by pilots or systems)






2. 

Maintenance Task Data



Helps define what needs to be done, and how long it takes.


  • Task duration estimates (man-hours)
  • Task precedence constraints (dependencies between tasks)
  • Required qualifications/certifications per task
  • Special tools or equipment required
  • Parts and materials needed
  • Task deadlines (hard or soft constraints)






3. 

Technical Staff Data (Capacity)



Used for staff planning and allocation.


  • Headcount by shift and location
  • Staff skill levels and certifications (e.g., A&P, avionics)
  • Work-hour availability per person (after accounting for leave, training, fatigue limits)
  • Multi-tasking ability or crew specialization
  • Labor cost per technician/crew






4. 

Maintenance Facility & Resource Data



Defines physical capacity and constraints.


  • Number of bays/hangar slots by aircraft size/type
  • Tool and equipment availability
  • Storage and handling constraints for parts
  • Availability of ground support staff and systems






5. 

Scheduling and Planning Data



Helps in integrating the above into schedules.


  • Aircraft availability windows (downtime allowed without disrupting operations)
  • Priority rules (e.g., safety-critical before cosmetic)
  • Slack time between scheduled checks
  • Lead times for procurement of parts/tools
  • Historical delay/cancellation data due to maintenance






6. 

Performance and Risk Metrics (for optimization)



Used to evaluate and improve the scheduling model.


  • Total downtime per aircraft
  • Turnaround time per maintenance event
  • Technician utilization rate
  • On-time performance (OTP) impact
  • Deferred maintenance events count
  • Maintenance-induced delays or cancellations





Would you like a visual summary or a data model template in Excel or diagram format for these?


From Blogger iPhone client

Sandbox benefits

Great question. Here’s a breakdown of top use cases for a Data Analyst working with:


  1. BigQuery Sandbox, and
  2. Raw Layer of a Data Lake (e.g., DataLake, GCS, or raw zone in a data lakehouse)






1. BigQuery Sandbox – Use Cases for Data Analysts



The BigQuery Sandbox is a free, no-credit-card-required environment, ideal for prototyping and learning. It has usage limits but supports real SQL capabilities.



Top Use Cases:



  • Ad-hoc SQL Analysis
  • Run quick queries against public datasets or connected sources for exploratory analysis.
  • Data Cleaning and Transformation
  • Use SQL to apply filters, remove duplicates, standardize formats (e.g., dates, currency).
  • Data Joins Across Tables
  • Combine datasets using JOIN to enrich or correlate data.
  • Custom Metric Calculation
  • Create derived metrics like conversion rates, retention, churn, etc.
  • Visualization Prototyping
  • Connect BigQuery Sandbox to tools like Looker Studio (free) for dashboard mockups.
  • Query Optimization Practice
  • Analyze execution plans and learn how to optimize SQL using partitioning, clustering, and caching.
  • Public Dataset Exploration
  • Leverage Google’s public datasets (e.g., COVID, Census, StackOverflow) for practice and insights.






2. Raw Layer of Data Lake – Use Cases for Data Analysts



The raw layer stores unprocessed, large-volume data — often in formats like JSON, Parquet, or CSV — usually on cloud storage (like GCS, S3, or Azure Data Lake).



Top Use Cases:



  • Schema Discovery & Data Profiling
  • Use tools like bq load, bq show, or data catalog to inspect structure, nulls, ranges, outliers.
  • Data Ingestion Validation
  • Check if all expected files landed, and validate record counts, file sizes, or timestamps.
  • Raw-to-Curated Transformation
  • Write SQL or Spark queries (depending on infra) to shape raw data into usable curated/clean tables.
  • Change Detection
  • Compare raw file drops over time for changes in volume, structure, or anomalies.
  • Data Lineage & Traceability
  • Understand source system behavior by inspecting raw logs or events (e.g., API payloads, user events).
  • Staging for Snapshotting
  • Pull in the raw data to BigQuery staging tables for snapshotting/archival analytics.






Combined Workflow Example:



  1. Analyst pulls raw JSON logs from GCS into BigQuery temporary table.
  2. Cleans and joins with dimension data in curated layer.
  3. Creates KPIs and dashboards in Looker Studio using the sandbox or scheduled queries.





Would you like a visual diagram or cheat sheet to map these layers and use cases?


From Blogger iPhone client

Using big query schedule

To schedule queries in BigQuery, you use Scheduled Queries in the BigQuery UI, the bq command-line tool, or Terraform/API. Here’s how to do it via the UI (most common approach):





Step-by-Step: Schedule a Query in BigQuery UI



  1. Open BigQuery Console
  2. Go to: https://console.cloud.google.com/bigquery
  3. Write Your Query
  4. Open the SQL editor and write your query (e.g., a snapshot insert query).
  5. Click on “Schedule”
  7. You’ll see a “Schedule” button just above the query editor.
  8. Click it and then select “Create new scheduled query”.

  10. Set Job Details
  12. Name your job.
  13. Choose Project and Location (typically US or EU).

  15. Set Frequency
  17. Choose from predefined intervals: Hourly, Daily, Weekly, Monthly, or Custom using cron expressions.
  18. Example (daily at midnight): 0 0 * * *

  20. Set Start Time
  22. Define when the schedule should start.


  25. Destination (optional)
  27. If your query writes results to a table, you can configure the destination table and write behavior (e.g., append or overwrite).


  30. Notification Settings (optional)
  32. Set up email notifications or connect to Cloud Logging.


  35. Save
  37. Click Save to create the scheduled query.







Optional Tips



  • Permissions Needed:
  • You need bigquery.jobs.create permission (usually included in roles like BigQuery Admin or Editor).
  • Monitor Jobs:
  • Navigate to BigQuery > Scheduled Queries to view or manage existing schedules.





Would you like a CLI version (bq command) or cron-style examples?


From Blogger iPhone client

Asana sync devops


To synchronize Azure DevOps and Asana, you can use integration platforms or custom APIs. Here are your main options:





1. Use a Third-Party Integration Platform




a. Unito




  • Features: Two-way sync of work items, comments, assignees, status, due dates.
  • How it works:

  • Connect Azure DevOps and Asana.
  • Set rules for how work items/tasks map (e.g., DevOps bugs → Asana tasks).

  • Ideal for: Teams needing live sync between platforms.




b. Zapier or Make (Integromat)




  • Zapier example:

  • Trigger: New work item in Azure DevOps.
  • Action: Create task in Asana.

  • Note: Mostly one-way automation (not full sync), but good for simple workflows.






2. Use Azure Logic Apps




  • Built-in connectors for both Azure DevOps and Asana.
  • Can build custom workflows, e.g.:

  • When a new task is created in Asana → create a work item in Azure DevOps.
  • When a DevOps item is updated → update Asana task status.






3. Build a Custom Integration with APIs




  • Use Azure DevOps REST API and Asana API.
  • Sync based on:

  • Task/work item creation
  • Status updates
  • Comments

  • Ideal if you need full control and have specific logic/fields to sync.






Recommendation




  • For most use cases, Unito is the fastest and most flexible option.
  • For enterprise workflows or data governance concerns, Azure Logic Apps may offer better control and security.



Would you like a visual comparison or a step-by-step guide for any of these tools?

From Blogger iPhone client

Bloomberg rest api data cost

Accessing Bloomberg data via their REST API is available through Bloomberg’s Enterprise API (BBG API), but it’s not publicly priced like a typical SaaS service. Pricing depends on your use case, data volume, and licensing agreements. However, here’s a general breakdown:



1. Bloomberg Terminal Subscription (Required for API Access)



  • Cost: ~$2,000–$2,500 USD/month per terminal
  • Includes access to Bloomberg Desktop API (Excel and limited programmatic use)
  • Not sufficient for large-scale or automated use






2. Bloomberg Enterprise Data License (for REST API & scalable access)




  • Base Cost: Starts around $10,000–$25,000 USD/year or more
  • Additional costs depend on:

  • Data types (real-time, delayed, historical)
  • Number of tickers
  • Fields requested (e.g., last price, market cap, volume, etc.)
  • Update frequency (snapshot vs. streaming)
  • Redistribution rights (if you serve data to clients or apps)






3. Bloomberg B-PIPE (Streaming Data for Enterprises)




  • For real-time market data with low latency
  • Cost: Starting from $100,000+/year, typically used by large financial institutions






Alternatives for Developers/Smaller Firms



If you only need limited financial data:



  • Refinitiv, Xignite, Quandl, or Polygon.io may offer more affordable REST API access.
  • Some of them have freemium tiers or pay-per-ticker pricing.



Would you like a comparison table of Bloomberg vs. these alternatives for REST API use?

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Cybersecurity

try hack me


https://youtube.com/shorts/W5vAMx4diK0?si=TjjMwYf-q4NrJMk8

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