Opportunities across academia
Job Summary
The Executive Director (ED) provides critical leadership, advocacy and advice for the Faculty of Science’s strategic relations with all levels of government and other high value external stakeholders. They will also lead the Faculty’s involvement in campus-wide government, NGO and industrial relations strategies in collaboration with other campus leaders to promote and advance major Faculty of Science initiatives.
The ED leads the development and implementation of targeted advocacy plans, provides leadership and guidance to operationalizing effective government and external relations in the overall advancement of academic mission and priorities of the Faculty of Science. These priorities include advancing large capital projects, advocating for provincial seat funding for Science programs, forging major industrial collaboration pathways and research partnerships, and any other priority initiatives as they arise.
The initial priority of the ED will be to establish and exploit pathways for high-level advocacy at all levels of government, with an initial emphasis on highlighting and strengthening existing alignments between the Faculty’s research and teaching mission with the Provincial and Federal Governments’ evolving economic and social priorities. This will also require high-level engagement with industry leaders across the province’s key economic sectors that are relevant to STEM education, research and innovation. At this time, priority areas include critical minerals, AI, defense, environment and climate change, quantum materials, and inter-Faculty initiatives.
Another priority will be to lead the advancement of a major new Science-led capital project (currently called ‘Science Central’) through the development and enhancement of the Faculty’s relationship with all levels of government. As the university’s new hub for interdisciplinary chemical sciences, Science Central will combine revitalized curriculum, advanced training, and a lab-to-market research ecosystem. It will be the epicenter for cutting-edge, challenge-oriented STEM education, research and industry engagement at UBC.
Organizational Status
Reports to the Dean of Science. Works closely with all leaders within the Faculty of Science, senior University administration and with teams in portfolios across the UBC Vancouver campus including Campus and Community Planning, Development & Alumni Relations, Marketing & Communications, External and Government Relations when appropriate.
This position is a senior leader in the Office of the Dean of Science. They lead the development and implementation of annual and long-term government relations plans for the Faculty. They are responsible for ensuring that all the Dean’s leadership team is aligned and aware of current and emergent external factors affecting issues of strategic and operational importance to the Faculty.
Work Performed
Environmental scanning, opportunity and threat analysis
- Proactively monitors, analyzes and interprets government programs, legislation and activities and provides advice on the implications and opportunities for the Faculty of Science
- Conducts detailed environmental scans of issues relevant to the Faculty as it pertains to government activity and the post-secondary sector
- Maintains current, reliable knowledge and intelligence of shifting government priorities in order to adapt engagement strategies effectively. Maintains awareness of key relevant initiatives and strategies set out by all orders of government relevant to the Faculty of Science (e.g., Environment & Climate Change Canada, MITACS, ISED [e.g., National Quantum Strategy, Federal and Provincial Critical Minerals Strategies, Pan-Canadian Artificial Intelligence Strategy]).
- Advises and leads the writing of briefing notes, presentations and reports for the Dean and Dean’s leadership team in advance of meetings with government officials or sector-wide meetings with government.
- Provides in-depth analysis through both written notes and formal briefings, along with recommendations on advocacy initiatives and emerging issues and opportunities.
Issues Management
- Evaluates emerging and longer-term opportunities and threats to the achievement of the Faculty’s priorities and the critical success factors for the Faculty as they relate to the government
- Responds to and meets with a broad range of internal and external individuals and groups, frequently related to matters of immediate concern, and coordinates responses, solutions and follow-up with the Dean and other University constituents as appropriate
Host for high-value guests to campus and the region
- Creates and/or identifies opportunities for government officials to participate in the Faculty and opportunities for the Faculty to engage in government initiatives
- Identifies opportunities and leads the development of new collaborative initiatives with external partners that advance the Faculty’s academic mission (e.g. research partnerships & contracts, MITACS and HQP recruitment, talent development, and new programming in support of the Science Central vision and mission).
- Leads government visits, Faculty tours and meetings
Leadership of government communications activities
- Develops high-level communications and briefing materials for dissemination to target audiences in collaboration with Faculty-level communications and other University units (e.g. External Relations, Development and Alumni Engagement, VP Research and Innovation)
- Provides leadership and advice on responses to inquiries from government officials, advocacy documents, proposals for funding and other initiatives as they arise
- Works with the Faculty-level communications on written materials in support of the Faculty’s strategic goals
Relationship Development & Management
- Establishes and manages high-value relationships with government constituents (Ministry of Jobs, Economic Recovery and Innovation; Ministry of Post-Secondary Education and Future Skills; and other sector-specific Ministries) as they relate to the Faculty’s research, innovation, education and workforce development mission.
- Establishes and manages high-value relationships with industry and community organizations aligned with major government and Faculty research and training priorities
- Builds and maintains a network of government and influential relationships in Victoria and Ottawa including politicians, public service officials, high-level staffers and thought leaders
- Develops and nurtures strategic relationships with key post-secondary advocacy organizations (Universities Canada, U15) and colleagues at other institutions, which can be leveraged in advocacy or partnership opportunities
- Advances relationships in areas of key strategic importance including clean energy, biotechnology and health, environment, mining and critical minerals, directly engaging with critical faculty as appropriate, coordinating with other campus leaders where needed
- Develops positive relationships within the University and with its primary stakeholders, both internal and external, and identifies opportunities for internal collaboration and consultation with other faculties on government relations
- Develops and builds strong relationships and maintains a network with University colleagues involved with external relations
- Develops and maintains a strong cross-campus network of senior administration and advocacy professionals, providing visibility to Faculty-wide GR opportunities, and leveraging the support of campus advocacy channels
- Identifies and leads opportunities to advance the Faculty’s image and reputation with targeted audiences including public and industry events, speaking opportunities, and consultations
Consequence of Error/Judgement
The role is responsible for handling potentially sensitive issues requiring strict confidentiality, judicious management of information and tactful decision-making. This position is integral to the Faculty’s relationships with external organizations, and to its reputation internally and externally. In addition to a high level of tact and discretion, the incumbent must have a strong knowledge and understanding of the issues facing the post-secondary education sector broadly. Because this area is charged with building and maintaining relationships with organizations external to UBC Science, improper advice or actions could result in severe damage to the Faculty’s ongoing external relations work, which could have reputational, financial and political repercussions.
Supervision Received
The position is independently responsible for day to day work, and receives strategic direction from the Dean of Science and Dean of Science leadership team.
Supervision Given
N/A.
Minimum Qualifications
Undergraduate degree in a relevant discipline. Minimum of ten years of related experience with at least two years of experience in a senior management position involving strategic business planning, or the equivalent combination of education and experience.
- Willingness to respect diverse perspectives, including perspectives in conflict with one’s own
- Demonstrates a commitment to enhancing one’s own awareness, knowledge, and skills related to equity, diversity, and inclusion
Preferred Qualifications
- Graduate degree much preferred.
- A minimum of ten years related experience in advocacy and / or government relations and lobbying work, or the equivalent combination of education and experience.
- Strong awareness of post-secondary issues and relevant external pressures, structures and initiatives
- Knowledge of capital projects and funding priorities
- Knowledge of Canadian innovation ecosystem, including issues around workforce and talent development intellectual property, R&D and commercialization
- Familiarity with the activities and culture of a major research university is desirable. Knowledge of UBC’s organization, structure and personnel is an asset.
- General knowledge of operations of mass media.
- Developed interest in public affairs, public policy and the operations of different levels of government.
- This position may require travel within BC and internationally.
Leadership Skills
Proven track record leading and being agile as priorities evolve.
High personal resilience. Has the ability to establish a high level of rapport with senior and executive level leadership within and outside the university community, stakeholders and opinion leaders; utilize judgment and sensitivity in presenting issues; provides leadership and direction in determining appropriate courses of action. Ability to think strategically and possess intellectual curiosity. Ability to effectively manage sensitive or controversial issues. Exercises a high degree of diplomacy and discretion in all internal and external interactions. Understands the dynamics of ever-changing environment and can prioritize effectively. Is flexible and can manage changing priorities within a dynamic work environment. Possesses a highly independent working style with interest to engage early with issues and opportunities. Self-sufficient and self-motivated. Takes ownership of programs and tasks. Takes a proactive stance in solving problems and exhibits understanding and empathy, particularly when dealing with topics of public concern. Able to quickly grasp the scope of specific issues and the potential impacts on the organization.
Galley cart locks aviation
Good question — there isn’t a single global rule that only covers “cabin cart locks.” Instead, compliance is a mix of airworthiness/certification rules, operator (airline) procedures, cabin safety guidance, and security/catering requirements. Below I’ve summarized the practical compliance landscape (what regulators require, what airlines must do, and what auditors should check), with authoritative sources so you can dig deeper.
Short summary (one-line)
- Galley/serving carts that are part of the aircraft design must meet airworthiness and certification requirements (FAA/EASA/CS certification rules); irrespective of certification, operators must secure and restrain carts during critical phases and when not in use per operations manuals and cabin-safety guidance; catering/security locks and seals are industry practice and included in airline procedures and security regulations.
What the main regulators say (high-level)
- FAA / United States: Airworthiness (14 CFR Part 25 / Part 121) and FAA advisory material require that cabin items (including galley equipment and service carts) be restrained or secured and that manuals and procedures address stowage and restraint. FAA cabin-safety guidance emphasizes restraining serving carts when not in use.
- EASA / Europe: Galley carts and containers are treated as part of the galley/“open galley surfaces” for certification/airworthiness purposes and subject to related means-of-compliance; EASA materials and rule compilations handle galley equipment in their certification/airworthiness guidance.
- ICAO / IATA (global guidance): ICAO/IATA guidance and industry best-practice documents instruct operators to secure galley equipment/carts (apply brakes, latch, stow) and to perform safety risk assessments for non-standard configurations (e.g., freighter conversions or ad-hoc cargo in cabin). IATA also provides carriage and cabin-cargo guidance.
Practical implications for airlines / operators
- Design vs. operational item
- If a cart is part of the aircraft type design (i.e., certified installation), it must comply with the aircraft certification requirements (airworthiness, occupant injury, latching under crash loads, flammability where applicable).
- If it’s an operational item (loose service cart not part of the type design), the operator must ensure safe restraining, procedural controls, and inclusion in their manuals (SOM/OMP/COM) and training.
- Locking / restraint mechanisms
- Carts must have functional brakes/latches and proper stowage provisions; many operators mandate both mechanical locks and procedural checks (apply brake, latch, orient wheels into guiders). Where a cart interfaces with a certified galley fixture, that interface must be certified.
- Operational procedures
- Procedures required in crew manuals and operations manuals for: preflight checks of galley carts, stowage during taxi/takeoff/landing, reporting/marking of inoperative (INOP) carts, and SOPs for in-flight service vs turbulence.
- Security / catering locks & seals
- Airlines commonly use seals/locks on catering carts (and catering trucks) to protect food integrity and comply with security requirements. These are generally mandated by operator security procedures and airport/country-security rules rather than aircraft certification rules.
Key compliance differences by region (typical)
- U.S. (FAA): Focus on inclusion in manuals, Part 25 airworthiness for certified installations, and safe stowage rules under Part 121 operations. Enforcement via oversight and audits.
- EU (EASA): Tight integration with certification standards and means-of-compliance for galley fittings; operator procedures mirror EASA OPS and national authority oversight.
- Other states (ICAO contracting states): Most follow ICAO/IATA guidance and apply equivalent national rules; some variations exist in how much of galley equipment is treated as certified aircraft equipment versus operator-supplied.
Five most important compliance points (for auditors / program owners)
- Certification status — confirm whether the cart (or attachment) is part of the aircraft’s certified installation (type-approved) or an operator-supplied loose item; certification triggers different requirements.
- Physical restraint / locking function — verify mechanical condition: brakes, latches, locks, and any interface fittings operate to specification and are maintained.
- Procedures & manuals — ensure SOPs describe stowage and locking steps for all phases (taxi, TO, landing, turbulence) and that crew are trained and assessed.
- Security seals / tamper evidence (catering) — ensure catering supply chain controls (seals, supervision) are documented and followed where required by security programs.
- Reporting & INOP handling — damaged or non-functional trolleys must be tagged/removed from service per operator policy (and not used if they cannot be secured).
Quick compliance / audit checklist (copyable)
- Is the cart certified as part of the aircraft installation? (Yes / No) — if Yes, check certification documents.
- Visual & functional: brakes engage; latches secure; wheels/guides align; no broken parts.
- SOPs & manuals: stowage/locking steps present in SOM/OMP/checked in recurrent training.
- Security: Are catering seals used where required and chain-of-custody documented?
- Maintenance records & defect handling: are inoperative carts removed/labelled per procedure?
Notes, nuances, and practical tips
- No single “global lock spec”: regulators require safe restraint and certified interfaces where applicable, but the exact lock type/brand is an operator/manufacturer decision.
- Industry guidance is influential: IATA/ICAO/industry compendia provide the practical operational expectations that airlines adopt into manuals.
- If changing use (e.g., cargo in cabin, or new smart locking systems) — you must perform a safety assessment and often seek authority approval for non-standard configurations.
If you want, I can (pick any and I’ll do it right away)
- Build a printable audit checklist template (Excel / PDF) tailored to FAA, EASA or a specific airline.
- Compare a specific airline’s published policy (e.g., Singapore Airlines, Emirates, Delta) for galley/cart restraints and show differences. (I’ll look up each airline’s published SOPs/policy and cite them.)
- Draft an operations paragraph you can insert into a flight-ops or cabin-safety manual describing required locking/stowage language.
Sources (selected): FAA Cabin Safety index & stowage guidance; EASA rules/“open galley surfaces” references; IATA/ICAO cabin operations and carriage guidance; industry cabin safety compendia and operator best practices.
Tell me which follow-up you want (audit template, airline comparison, or a manual paragraph) and I’ll produce it immediately.
🛫 1. Aero líneas Argentinas Flight 386 — Cholera from Contaminated Meal (1992)
One of the most significant food-contamination cases in aviation history occurred on 14 February 1992:
• Onboard Aerolíneas Argentinas Flight 386 from Buenos Aires (via Lima) to Los Angeles, a Boeing 747 flight served meals contaminated with cholera.
• 75 people fell ill, and 1 passenger died from the disease after eating the contaminated food.
• The outbreak was traced back to poor hygiene and contaminated food preparation in the catering supply chain. The public health risk resulted in a major investigation and heightened international focus on aviation catering standards and safety.
What this meant for industry practice:
After this incident and others like it, aviation stakeholders (airlines, caterers, health agencies, regulators) strengthened food-safety management systems (e.g., Hazard Analysis and Critical Control Points (HACCP)) and regular audits of catering providers to prevent biological contamination.
⸻
🍽️ 2. Japan Air Lines Food Poisoning (1975)
A classic historical case, often cited in aviation safety studies:
• In February 1975, aboard a Japan Air Lines Boeing 747, 144 passengers became ill after eating meals contaminated with Staphylococcus aureus toxins.
• Laboratory tests confirmed the pathogen in meals and passenger samples.
• The source was traced to improper food handling by catering staff, including an infected food handler.
• Investigation highlighted crew meal separation practices (to avoid simultaneous incapacitation) and food temperature controls as crucial safety measures.
⸻
🥘 3. Recent Report — Emergency Landing After Serving “Contaminated” Food (2024)
In July 2024, a Delta Airlines flight diverted to New York JFK after onboard meals were reportedly spoiled/contaminated:
• Flight 136 (Detroit → Amsterdam) was diverted when passengers received meals suspected to be spoiled.
• Medical crews attended onboard, and Delta initiated an investigation with its food safety team and suppliers to isolate the product.
While final investigative details may not yet be publicly released, the operational response (diversion, isolation, investigation) underscores how airlines treat food safety issues as potentially serious operational safety risks.
⸻
🧪 4. India Food Safety Authority Notice Over Blade Found in Airline Meal (2024)
This isn’t a classic contamination pathogen case, but it illustrates why pre-flight food screening and inspection controls are mandated:
• In June 2024, the Food Safety and Standards Authority of India (FSSAI) issued an improvement notice to a major airline catering contractor after a passenger found a metal blade in his in-flight meal.
• The foreign object was traced back to a vegetable-processing machine at the catering facility.
• Authorities ordered installation of x-ray machines and better inspection controls to prevent physical contamination of meals before loading.
Takeaway: Even non-biological contamination prompts regulators to tighten controls (e.g., metal detectors, X-ray scanning of food trays) to prevent harm from physical hazards.
⸻
📋 5. Industry and Medical Safety Guidance
Beyond specific accidents, food poisoning is recognized as a serious hazard that can affect crew performance and flight safety:
• Aviation medicine literature and safety databases note that foodborne illness can incapacitate crew and threaten safe flight operations, leading to calls for more stringent catering standards and crew meal policies (e.g., different meals for pilots).
⸻
🧐 Why These Matter for Compliance
These incidents and investigations have led the aviation industry and regulators to enforce or tighten:
✔ Catering facility standards and audits (HACCP, hygiene, temperature control).
✔ Security and safety checks of catering loads before boarding—e.g., verifying seals, metal detection, and inspection of carts.
✔ Crew procedures for food handling and symptom response, including medical reporting guidelines.
✔ Regulatory guidance on food safety and contamination prevention for in-flight meals and crew meals.
Airline food cart Safety
In the aviation industry, the plastic lock on a food cart is known as a tamper-evident seal or security seal. It plays an important role in safety, security, and compliance.
✈️
What It Is
A plastic tamper-evident seal is a small lock placed on airline meal carts (also called trolleys or ATLAS carts) to ensure they remain closed and untouched from the time they are loaded until they reach the aircraft.
🔐
Key Purposes
1. Security
- Prevents unauthorized access to food, alcohol, or duty-free items.
- Ensures no one tampers with the contents during transport from catering facility → aircraft.
2. Safety
- Reduces risks of contamination.
- Ensures items inside are untouched until opened by authorized crew.
3. Compliance
Airlines are required to follow:
- Airport security regulations
- Customs & duty rules (especially for alcohol)
- Inflight catering standards
4. Tracking & Auditing
Each seal normally has:
- A unique serial number
- Sometimes a barcode
- A color code (based on airline or catering shift)
Crew record the seal number during:
- Pre-flight safety checks
- Post-flight security clearance
This provides a traceable audit trail.
🧰
Types of Plastic Seals in Aviation
Type
Description
Pull-tight seal
Adjustable length; used for carts and containers
Fixed-length security seal
Breaks when cart is opened
Barcode/numbered seal
Used for tracking and audits
High-strength seals
Used for liquor carts or high-value item carts
🛠️
How Crew Use It
- Catering loads meals → seals the cart
- Seal number is documented
- Cart loaded onto aircraft
- Crew verifies the seal matches documentation
- To use the cart, crew breaks the seal
- Broken seal is kept for recordkeeping
🏢
Common Manufacturers
- TydenBrooks
- Versapak
- Unisto
- Mega Fortris
- Hansco
- (Used globally by airline catering companies like LSG Sky Chefs, dnata, Qatar Airways Catering, Gate Gourmet, etc.)
If you want, I can also provide:
✅ Diagram of a sealed cart
✅ Procurement list (models & prices)
✅ Safety procedure checklist for audits
Just tell me!
AI prompts
Create a handwritten study note about using Gemini with Python rest api to generate insights by creating a Python framework for input processing functions and output functionality. Use a messy but legible student handwriting font on lined paper. Use a yellow neon marker to highlight important terms so l can revise during exams and draw red circles around important dates. Add small doodles to explain the concepts better on A4 printable size paper.
Amos training and services opportunities
Aviation and Marine Industries
- Swiss AviationSoftware: Look for roles such as Cloud Engineer, Full Stack Software Engineer, or Application Support Specialist.
- Aviation Companies: Search for jobs like AMOS Technical Record Specialist, which involves data entry and management for aircraft maintenance records.
- Recruitment Agencies: Companies like Line Up Aviation and jobsDB specialize in placing candidates in AMOS-related positions, such as AMOS Software Solutions Co-ordinator or Business Consultant and Project Manager.
- AtSea.group: Offers training for engineers to work with AMOS, creating opportunities in the marine industry by improving their ability to interact with computerized systems.
Checklist for new Opportunities
On culture for this week.
At leadership level, saying yes to an opportunity too quickly can be career-limiting.
Saying no strategically can be career-defining.
Here are 15 sharp questions to assess any senior opportunity before you sign on the dotted line:
1. What problem am I really being hired to solve?
Not the polished job description.
The messy, real issue.
2. Who truly owns the decision-making power?
Org charts are fantasy.
Influence maps reveal reality.
3. Is the CEO or executive team aligned on expectations?
Conflicted leadership = constant firefighting.
4. Does the culture support transformation?
Airlines, OEMs, space ventures — culture eats strategy for breakfast.
5. What does success look like in 12 months?
Vague answers?
Red flag.
6. Are resources, budget, and talent proportional to ambition?
High targets + low support = stress and failure.
7. Who are the hidden blockers?
Sometimes the “problem” is politics, not strategy.
8. What happened to the last person in this seat?
Turnover tells a story that job descriptions hide.
9. How do teams handle bad news?
If honesty is punished, innovation dies.
10. Who do I need to influence outside my direct team?
Cross-functional alignment is everything.
11. Is there clarity on authority vs. autonomy?
Decision-making without empowerment is window dressing.
12. Does the market care about what the company is solving?
If the answer is “we hope so,” your impact will be limited.
13. Will this role stretch my leadership — or just fill my calendar?
Senior roles should amplify your influence.
Not just your tasks.
14. Does this opportunity align with my personal energy and values?
Passion matters — the aerospace sector is high-pressure and unforgiving.
15. How will this role define the next 5 years of my career?
Always think beyond the immediate.
Senior decisions compound.
At leadership levels, every opportunity is a high-stakes decision.
The wrong role doesn’t just cost you a job.
It costs years of impact, your reputation, and the energy you’ll never get back.
♻️ Share this to help your network.
#culturematters #hiring #aviation
From Blogger iPhone clientCore competencies in Data Engineering
Two Data Engineers interviewed at FAANG for a Data Engineering role.
One got rejected.
One got hired.
Same interviews.
Different grasp of fundamental data concepts.
Meta tests foundations because their stack is proprietary.
You can't learn their tools before joining. But you can master the core principles that translate to any data system.
Here's the fundamental Data Engineering stack you need to master, no matter the company you're aiming for.
Layer 1: Data Modeling & Schema Design
The foundation everything builds on.
- Normalization vs denormalization tradeoffs.
- Star and snowflake schemas.
- Slowly changing dimensions.
- Partitioning and bucketing strategies.
Poor modeling? Your queries will never scale.
Layer 2: SQL & Query Optimization
Your primary language for data.
- Complex joins and window functions.
- Query execution plans and indexes.
- Subquery vs CTE performance.
- Aggregation optimization techniques.
Can't write efficient SQL? You won't pass the technical.
Layer 3: Distributed Systems Fundamentals
How data systems actually work at scale.
- CAP theorem and consistency models.
- Partitioning and replication strategies.
- Distributed query processing.
- Fault tolerance and recovery.
Miss these concepts? You can't reason about production issues.
Layer 4: Data Pipeline Architecture
Moving data reliably at scale.
- Batch vs streaming tradeoffs.
- Idempotency and exactly-once processing.
- Backfill strategies and data quality.
- Orchestration and dependency management.
Bad pipelines? Data teams lose trust in your work.
Layer 5: Storage Systems & Formats
Where and how you store matters.
- Row vs columnar storage tradeoffs.
- Parquet, ORC, Avro characteristics.
- Data lake vs warehouse patterns.
- Compression and encoding strategies.
Wrong storage choices kill query performance.
Layer 6: Data Quality & Observability
Production data is messy.
- Schema validation and evolution.
- Data lineage and impact analysis.
- Monitoring pipeline health.
- SLA definition and alerting.
No observability? You're flying blind in production.
Layer 7: Performance & Scalability
The difference between junior and senior.
- Understanding data skew and hotspots.
- Memory vs disk tradeoffs.
- Caching strategies and materialization.
- Cost optimization techniques.
Can't optimize? Your pipelines won't survive scale
From Blogger iPhone clientBritish Airways Strategic Challenges
strategic, executive-level articulation of British Airways’ greatest strategic challenges for the next 3 years (2025–2028). This will help you prepare for interviews, assessments, or application questions.
Greatest Strategic Challenges for British Airways (Next 3 Years)
1. Modernising an Ageing Fleet While Controlling Costs
British Airways has one of the oldest fleets in Europe among major carriers.
Over the next three years, BA must:
- Replace ageing aircraft to reduce fuel burn, maintenance costs, and emissions.
- Manage long lead times for new aircraft (A350s, 787s, 777Xs) and supply-chain delays.
- Fund these investments while maintaining profitability and keeping fares competitive.
Why it’s a challenge: Fleet renewal is capital-intensive and constrained by global aircraft shortages.
2. Improving Operational Reliability and Customer Experience
BA has faced:
- Operational disruptions (cancellations, IT outages, ground-handling inconsistencies),
- Lower customer satisfaction versus leading European and Gulf competitors.
Next 3 years will require:
- Stabilising operations through better staffing, technology, and baggage/ground handling automation.
- Enhancing digital touchpoints (app, check-in, disruption management).
- Rebuilding trust and improving NPS.
Why it’s a challenge: Customer expectations are rising while BA’s legacy systems and workflows need major overhaul.
3. Digital Transformation + Legacy IT Replacement
BA is still heavily reliant on legacy IT systems across reservations, ops control, crew management, and MRO.
Strategic priorities:
- Migrating to cloud-based, integrated ops systems,
- Using predictive analytics for maintenance, crew planning, fuel efficiency and supply chain,
- Deploying AI and automation to reduce turnaround times and irregular operations impact.
Why it’s a challenge: Transformation at this scale risks disruption if poorly managed and requires major cultural and process change.
4. Workforce Relations, Skills Shortages & Cultural Shift
BA must address:
- Talent shortages in engineering, pilots, cyber, and operations.
- Post-pandemic labour relations tensions, pay negotiations, and union constraints.
- Need for a digitally-skilled workforce as BA modernises.
Why it’s a challenge: Airline performance depends on high workforce stability and alignment across diverse employee groups.
5. Margin Pressure From Competition
BA faces intense competition from:
- Gulf carriers (Qatar Airways, Emirates) on long-haul premium,
- Low-cost carriers (easyJet, Ryanair, Wizz) on short-haul,
- European flag carriers improving (Lufthansa Group, AF-KLM).
BA must:
- Differentiate on service, digital experience, loyalty, and connectivity.
- Maintain Heathrow slots while optimising yields.
Why it’s a challenge: Competition exerts pressure on both ends of BA’s product spectrum—premium long-haul and value short-haul.
6. Sustainability Commitments & Net-Zero Pressure
IAG/BA committed to:
- Net-zero by 2050
- 10% SAF by 2030
- This requires huge investment and regulatory coordination.
Challenges include:
- Limited SAF supply and high prices
- Slow global policy alignment
- Balancing sustainability with affordability
The Single Greatest Strategic Challenge
Synchronising fleet renewal, digital transformation, and operational reliability—while improving the customer experience in a highly competitive market.
BA must modernise fast enough to stay competitive, but not so fast that disruption, cost overruns, or service instability undermine performance.
This challenge is cross-functional and affects cost structure, customer trust, employee satisfaction, and long-term brand equity.
British Airways Analysis on Performance
Based on public data (especially IAG’s most recent results, since IAG is BA’s parent), here’s a breakdown of which segments look to be outperforming for British Airways / IAG, and where they may be underperforming — plus some risks.
What Segments Are
Outperforming
- Transatlantic Premium / Long-Haul Premium
- IAG points to very strong demand on North Atlantic / transatlantic routes, especially in premium cabins.
- In Q1 2025, IAG said unit revenue was strong, helped by “high yields … in our core markets” and specifically highlighted good performance in long-haul premium.
- The Guardian also notes that BA (via IAG) benefited from increased transatlantic ticket sales in 2024.
- This premium demand is likely contributing disproportionately to profit growth, given margins on premium long-haul are generally higher.
- Leisure Travel / Premium Leisure
- IAG says that while corporate (business) travel is recovering, the “premium leisure” segment is particularly strong.
- In its capital-return and financial planning, IAG emphasizes that “leisure demand remains robust.”
- BA Holidays (part of IAG Loyalty) is also growing: in H1 2025, revenue from BA Holidays grew, and a large portion is from “Club” (frequent) customers whose average booking revenue is notably higher than non-members.
- Cargo
- Cargo revenues for IAG rose in 2024: Morningstar reports a 6.7% increase.
- This helps diversify BA/IAG beyond purely passenger business and captures upsides in freight demand.
- Other Revenue: Loyalty, MRO, Holidays
- “Other Revenue” (which includes IAG Loyalty, BA Holidays, maintenance, etc.) grew in IAG’s 2024 results.
- IAG Loyalty (Avios program) seems resilient: in H1 2025, Avios issuance and redemption both climbed, helping drive profitability.
What Segments Are
Underperforming / Facing Risk
- Corporate / Business Travel (Especially Short-Haul)
- IAG has explicitly said that business travel “will not fully recover to pre-pandemic levels, particularly for short-duration and short-haul trips.”
- In Q2 2025, IAG reported that corporate (business) volume declined ~8% YoY; BA’s revenue from corporate bookings was almost flat, but volume dropped.
- This suggests that while premium long-haul might be recovering (or even growing), lower-margin business travel (especially short trips) is more challenged.
- North Atlantic Economy (or Weaker Transatlantic Unit Revenue)
- Despite strong premium transatlantic demand, there is some reported weakness: Bloomberg reports that IAG said “some softness” in North Atlantic routes, with a 7.1% decline in unit revenue on those routes.
- So, while premium may be doing well, certain economy segments (or key origin market softness) could be dragging.
- Cost Pressures / FX Risk
- Non-fuel unit costs are increasing, and IAG flags foreign exchange (FX) as a negative factor.
- Even if revenue is strong, rising costs could squeeze margins in some segments.
Interpretation: Why This Matters for British Airways
- Strength in premium long-haul and transatlantic is a big positive for BA: these are high-margin routes, and the demand recovery here supports BA’s profitability strategy.
- Leisure travelers trading up (to premium leisure) is a favorable trend: they are likely contributing more revenue per seat.
- Corporate short-haul is not fully back: if business travel doesn’t rebound fully, that could limit BA’s upside in some premium / mid-premium segments.
- Revenue volatility on key long-haul economy markets (like North Atlantic) could be a risk if pricing power weakens or competition intensifies.
- Diversification (cargo, loyalty, holidays) is helping BA / IAG hedge against cyclical passenger demand risks.
Automation or death
I asked 50 airline operations controllers the same question: “How many different systems do you log into during a 12-hour shift?”
Average answer: 11 systems.
One dispatcher told me something I can’t stop thinking about: “By hour 8, I spend more energy remembering passwords than making decisions.”
Another said: “I maintain my own Excel spreadsheet because it’s less painful than using the three ‘integrated’ systems that supposedly do the same thing.”
That’s when I realized: We’ve been solving the wrong problem.
We thought the problem was integration. Or training. Or change management.
But neuroscience just revealed what’s actually happening: When researchers put people in brain scanners and showed them prices, the insula activated. That’s your pain center. The same region that fires when you get hurt. Your brain experiences friction as pain.
Every login. Every system switch. Every piece of data manually transferred. Your brain doesn’t just recognize these as inconvenient. It processes them as losses. As injuries.
And like any rational organism, it learns to avoid the pain.
We call those workarounds “expertise” and “tribal knowledge.” What they actually are: neural adaptations to pathological system design.
Think about what this means: An ops controller at 3 AM, hour 10 of their shift, facing a complex decision. Their cognitive capacity is already compromised by circadian rhythm.
But before they can even apply judgment, they have to:
→ Navigate 4 different systems
→ Reconcile conflicting data
→ Remember which interface uses which logic
→ Manually synthesize what should be automatic
Every one of those steps triggers the brain’s pain response.
By the time they reach the actual decision, their cognitive account is overdrawn.
And we wonder why errors cluster during night shifts and irregular operations.
The real question isn’t “how do we get better integration?”
The real question is: How much are we paying in cognitive currency—and what’s the exchange rate to safety, efficiency, and optimal decisions?
Operations professionals: What’s the most painful system transition in your workflow?
Technology teams: What would you eliminate first if you could start from scratch?
From Blogger iPhone clientEnterprise Data Producer Framework and registry system
Enterprise Data Producer Registration Framework written entirely in narrative form (no tables), suitable for strategy documentation or executive presentation.
Enterprise Data Producer Registration Framework
1. Purpose
The purpose of this framework is to establish a consistent, organization-wide method to register and manage all data producers within the enterprise.
A “data producer” is defined as any system, application, pipeline, API, sensor, or analytical process that creates, collects, or transforms data.
The framework ensures that every data producer is discoverable, owned, governed, and quality-assured across all data domains.
2. Core Principles
This framework is built around four key principles:
- Accountability – Every dataset and producer must have a clearly defined owner and steward responsible for its quality and metadata.
- Transparency – All data producers should be visible through a centralized catalog or registry, eliminating hidden or duplicate sources.
- Governance by Design – Metadata, lineage, and quality indicators must be automatically captured during data creation or ingestion.
- Automation and Integration – Registration and updates should be integrated into development workflows, ensuring minimal manual effort.
3. Framework Layers
A. Policy and Governance Layer
The first layer defines what qualifies as a data producer and sets mandatory requirements for registration.
Standards are established for metadata capture, lineage documentation, and data quality expectations.
Each data producer must have a designated data owner and data steward.
A formal Data Producer Registration Policy mandates that all new data-producing systems or pipelines must be registered before going live in production environments.
B. Metadata and Catalog Layer
A central metadata repository or data catalog serves as the single source of truth for all registered producers.
Each producer’s profile includes essential metadata such as producer name, description, business domain, system type, data owner, data steward, frequency of data generation, data sensitivity classification, data quality service levels, and known lineage (upstream and downstream dependencies).
This metadata ensures that each producer is searchable and traceable, allowing teams to discover and evaluate data sources with full context.
C. Technical Integration Layer
Registration and updates should be automated through integration with the organization’s technology stack.
When a new data pipeline or API is deployed, the system automatically registers the producer and its metadata through CI/CD hooks or API-based onboarding.
Automated scanners can periodically identify new producers in databases, data lakes, or cloud storage and prompt teams to complete their registration.
This layer ensures the registry remains accurate and up to date without requiring excessive manual administration.
D. Governance and Control Layer
Once producers are registered, governance processes ensure continued compliance.
Each producer undergoes periodic certification or review, typically every six to twelve months, to confirm ownership, lineage accuracy, and data quality performance.
Data quality dashboards monitor each producer for issues such as missing values, anomalies, or SLA breaches.
When producers introduce schema or logic changes, the system performs impact analysis based on lineage to alert downstream data consumers and systems.
E. Business Enablement Layer
A user-friendly Producer Registry Portal or interface allows data owners, analysts, and engineers to search, view, and manage producer information.
Through this interface, users can explore available producers by business domain, view ownership details, request access to datasets, or initiate formal data-sharing agreements.
The portal also provides visibility into producer performance and quality metrics, empowering teams to make data-driven decisions with confidence.
4. Key Metrics
To evaluate success, several metrics are tracked continuously:
- The percentage of data producers that are registered.
- The percentage of producers with complete metadata profiles.
- The percentage of producers with active data quality monitoring.
- The average time required to register a new producer.
- The number of unregistered or uncertified producers identified through audits.
These metrics are reported to the Data Governance Council or Chief Data Office as part of the organization’s data governance maturity program.
5. Implementation Approach
The registration framework is implemented using a combination of metadata management, workflow automation, and governance tools.
Metadata and lineage are managed in a central catalog, while CI/CD systems, orchestration tools, and APIs handle automated onboarding and updates.
Governance responsibilities are clearly defined: the Data Office owns and maintains the framework, Data Stewards ensure compliance, Engineering Teams provide metadata and automate registration, and the Data Governance Council performs periodic reviews and audits.
6. Expected Outcomes
When fully implemented, this framework provides the enterprise with a comprehensive inventory of all data producers, complete lineage from source to consumption, and consistent metadata across all data domains.
It strengthens data governance, reduces duplication and data risk, improves quality monitoring, and supports regulatory compliance.
Ultimately, it lays the foundation for a trusted, discoverable, and well-managed data ecosystem across the organization.
Policy
Enterprise Data Producer Registration Policy and Governance Standard
Version: 1.0
Owner: Chief Data Office
Approved by: Data Governance Council
Effective Date: [Insert Date]
1. Purpose
This policy establishes the mandatory process for registering and maintaining all data producers within the enterprise.
It ensures full visibility, ownership, and governance of systems and processes that generate, collect, or transform data.
The goal is to improve data discoverability, quality, lineage transparency, and compliance across all business units.
2. Scope
This policy applies to all business areas, data domains, and technology platforms that:
- Generate, capture, or transform data through systems, pipelines, APIs, models, or sensors.
- Store or transmit data to enterprise data platforms (data lake, data warehouse, analytics, ERP, etc.).
- Create or maintain datasets that are consumed by internal or external stakeholders.
The policy covers all environments — development, testing, and production — across on-premises and cloud platforms.
3. Policy Statement
All data producers must be registered in the enterprise metadata catalog before being promoted to production.
Registration ensures that each data producer has a clearly defined owner, steward, and metadata record including technical, business, and governance attributes.
Unregistered or uncertified producers are not permitted to publish or distribute enterprise data.
4. Definitions
- Data Producer: Any system, application, ETL/ELT pipeline, API, or model that creates, collects, or transforms data.
- Data Owner: The accountable individual or team responsible for the producer’s integrity, security, and compliance.
- Data Steward: The individual responsible for maintaining metadata, lineage, and quality metrics associated with a producer.
- Metadata Catalog: The enterprise platform used to record, search, and manage producer information and lineage.
5. Registration Requirements
Each data producer must be registered with the following details:
- Name and description of the producer.
- Business domain and functional area.
- Source and target systems.
- Data owner and data steward information.
- Data sensitivity classification (PII, confidential, public, etc.).
- Data refresh frequency and integration schedule.
- Key quality metrics and SLAs.
- Upstream and downstream lineage information.
Registration must occur through the Producer Registration Portal or automated CI/CD onboarding workflows.
6. Governance Responsibilities
- Chief Data Office (CDO): Owns this policy, defines standards, and monitors compliance.
- Data Governance Council: Approves the policy, reviews metrics, and enforces adherence across business units.
- Data Owners: Ensure that producers under their control are registered, accurate, and compliant with classification and quality standards.
- Data Stewards: Maintain metadata completeness, monitor quality, and update lineage changes.
- Engineering Teams: Automate registration in CI/CD pipelines and provide technical metadata to the catalog.
7. Compliance and Auditing
Compliance will be monitored quarterly through governance dashboards and metadata completeness reports.
Unregistered producers or incomplete metadata will trigger remediation actions by the Data Governance Office.
Non-compliance may result in suspension of data publication or access until registration is completed.
8. Metrics for Success
The following metrics will be tracked to measure policy effectiveness:
- Percentage of producers registered in the metadata catalog.
- Percentage of producers with complete and validated metadata.
- Percentage of producers with active quality monitoring.
- Mean time to register a new data producer.
- Number of uncertified or inactive producers.
9. Review Cycle
This policy will be reviewed annually by the Chief Data Office and the Data Governance Council to ensure continued alignment with enterprise standards, data management frameworks, and regulatory requirements.
10. Expected Outcomes
Implementation of this policy will result in:
- A complete and trusted inventory of all enterprise data producers.
- Clear accountability for data ownership and stewardship.
- Consistent metadata and lineage visibility across the organization.
- Improved data quality and reduced duplication.
- Stronger compliance with data governance, privacy, and audit requirements.
Strategic risks in airline
Excellent — you’re thinking exactly like a strategist now.
If Qatar Airways launches a subscription-based travel ecosystem (a Blue Ocean move) and other airlines replicate it, there are several structural risks and economic downsides — unless Qatar Airways captures early market share through advance commitments and ecosystem lock-in.
Let’s break it down systematically:
⚠️ 1.
Commoditization Risk
What happens:
If Emirates, Etihad, or Turkish copy the model (even partially), “subscriptions” may become a standard product feature — similar to how loyalty programs lost exclusivity.
Downside:
- Price wars: Airlines compete on subscription price rather than experience.
- Margins shrink as customers cherry-pick the best deal each year.
- The brand loses its differentiation narrative (“We invented it”).
Why advance commitments help:
- If Qatar secures long-term members through annual or corporate travel subscriptions, it locks in high-value flyers before others can replicate.
- Early contracts create switching friction for customers (e.g., stored perks, loyalty tiers, personalized data profiles).
🧩 2.
Capacity and Cost Pressure
What happens:
Subscription users tend to fly more frequently to maximize their perceived value — a phenomenon seen with Surf Air and unlimited gym memberships.
Downside:
- Higher load factor but lower yield per seat.
- Increased operational costs (fuel, crew, airport charges).
- Risk of “super-users” eroding profitability.
Why advance commitments help:
- If users pre-pay or commit upfront (e.g., 6–12 month minimum term, limited seat quotas per tier), it stabilizes cash flow and prevents overuse.
- Qatar can control flight distribution dynamically, optimizing yield per flight.
💰 3.
Cash Flow Dilution if Replicated
What happens:
If others launch similar models (Emirates Pass, Etihad Plus, Turkish Unlimited), customers may split their spending across programs.
Downside:
- No airline secures meaningful upfront cash reserves.
- Predictable recurring revenue becomes fragmented.
- The market turns from “subscription loyalty” to “flexible cancellation loyalty.”
Why advance commitments help:
- Lock-ins or prepayment (annual instead of monthly) turn the model from subscription-as-service to subscription-as-capitalization — a cash infusion Qatar can reinvest before competitors catch up.
- Qatar can use that capital to subsidize better experiences (premium lounges, Doha stopovers, private transfers), making imitation less appealing.
✈️ 4.
Brand Dilution if Replicated by Budget Carriers
What happens:
If low-cost carriers (like Wizz Air or Ryanair) push aggressive subscription plans first, the concept may become associated with budget, not premium.
Downside:
- Premium travelers may perceive subscriptions as low-end or “discount” models.
- Qatar’s luxury brand equity risks erosion if not differentiated properly.
Why advance commitments help:
- Qatar can secure corporate and high-end customers early, setting the standard for “Luxury Mobility Subscriptions.”
- Once positioned at the top, competitors face “down-market” pressure — they can’t credibly match the prestige tier.
🧠 5.
Data & Ecosystem Disadvantage
What happens:
If others replicate quickly, data moats narrow. Airlines may build their own ecosystems around loyalty and subscriptions.
Downside:
- The advantage of data-driven personalization diminishes if everyone collects similar behavior data.
- Vendors (hotels, tourism boards, car rentals) might split their partnerships among multiple airlines.
Why advance commitments help:
- Early partnerships with travel ecosystems (Qatar Tourism, Booking.com, Mastercard, etc.) can lock in exclusive integrations — making Qatar Airways’ subscription not just about flying, but about owning the end-to-end travel graph.
- Data accumulated early becomes an AI-driven barrier to entry for competitors.
🔒 6.
Strategic Lock-In: Why Advance Commitments Matter
To protect first-mover advantage, Qatar Airways should:
- Launch with an advance commitment window — e.g. “Founding Member Global Pass” (limited seats, early adopter pricing).
- Secure multi-year corporate travel deals — turning business travel into predictable subscription revenue.
- Integrate vertically with hotels, airport lounges, and Discover Qatar — so replication requires ecosystem, not just flights.
- Build loyalty-based switching costs — reward history, data personalization, and custom experiences that can’t transfer.
- Create a financial lock-in — via wallet credits or travel credits that grow with loyalty (expiring only within Qatar ecosystem).
🔮 Strategic Framing Summary
Risk if Replicated
Impact
Defense via Advance Commitments
Price erosion
Loss of premium differentiation
Annual lock-in tiers & corporate contracts
Overuse of subscription
Margin compression
Usage caps, dynamic yield pricing
Brand dilution
Weakening luxury appeal
Premium-only membership tiers
Data parity
Loss of personalization edge
Early data moat from large base
Ecosystem fragmentation
Lost network effects
Exclusive tourism & hotel partnerships
To make a subscription-based travel model sustainable, Qatar Airways must act fast to secure advance customer commitments and ecosystem exclusivity before Emirates, Etihad, or Turkish can imitate it.
Replication will eventually happen — but Qatar’s defensive moat will be built on:
(a) early adopter loyalty,
(b) corporate lock-ins,
(c) superior data integration, and
(d) ecosystem depth beyond the aircraft.
Travel Blue Ocean
Excellent strategic question — and very high-level. Let’s think through it properly.
A Blue Ocean Strategy means creating new demand and uncontested market space, instead of fighting competitors (like Emirates, Etihad, or Turkish Airlines) in the existing red ocean (premium service, hub model, route network).
Here’s how Qatar Airways could apply a Blue Ocean Strategy to leapfrog and make competitors irrelevant — not by being better, but by being different.
🧭 BLUE OCEAN STRATEGY FOR QATAR AIRWAYS
1.
Reinvent “Travel as a Lifestyle Subscription”
Concept: Shift from “selling tickets” to “owning the travel experience.”
Why it’s Blue Ocean: None of the major legacy airlines have a Netflix-style travel membership model.
Execution Ideas:
- Introduce Qatar Airways Global Pass — a hybrid subscription offering:
- Tiered plans (Explorer, Business Nomad, Jet Executive)
- Predictable monthly payments covering certain miles or segments
- Add-ons for hotels (via Discover Qatar), lounge, and private transfers
- Partner with Qatar Tourism, hotels, and Qatar Duty Free to integrate a complete mobility lifestyle.
- Offer personalized travel data dashboards (CO₂ offset, destinations covered, experiences earned).
Impact:
Turns Qatar Airways from a carrier into a mobility ecosystem brand, appealing to global digital nomads, expats, and corporates managing distributed teams.
2.
Build the First “Data-Driven Airline Ecosystem”
Concept: Make data the core differentiator.
Why it’s Blue Ocean: Airlines today compete on hardware (fleet, routes, service). No one dominates on insight and personalization.
Execution Ideas:
- Create Qatar Airways Intelligence Cloud — a single data platform linking flight behavior, loyalty, and ancillary preferences.
- AI-driven pricing, proactive rebooking, and predictive comfort customization (meals, seat zones, entertainment).
- Use data as a service: Partner with tourism boards and airports to provide insights.
Impact:
Transforms customer loyalty into data intimacy — hard for Emirates or Turkish to copy quickly.
3.
Reposition Doha as a “Destination-Transit Hybrid”
Concept: Make Doha stopovers desirable, not incidental.
Why it’s Blue Ocean: Emirates and Etihad treat layovers as passive.
Execution Ideas:
- Offer “Qatar Micro-Vacations” (6–24-hour curated layovers with themes: wellness, art, desert, Islamic heritage).
- AI suggests itineraries automatically when a traveler books.
- Subscription travelers earn experience badges and perks with Discover Qatar.
Impact:
Generates ancillary revenue and builds tourism loyalty to Qatar itself, beyond flights.
4.
Lead in “Decarbonized Luxury Travel”
Concept: Define luxury around sustainability, mindfulness, and digital efficiency, not gold trim.
Why it’s Blue Ocean: Emirates and Turkish associate luxury with opulence, not purpose.
Execution Ideas:
- Launch the “Qatar Conscious Class” — carbon-neutral premium seats with sustainable materials, healthy cuisine, digital detox options.
- Integrate a CO₂ tracker for each trip in the mobile app.
- Partner with green technology companies to certify the airline’s footprint transparently.
Impact:
Owns the emerging niche of eco-luxury travel, differentiating from competitors’ material luxury.
5.
Monetize “Global Mobility-as-a-Service”
Concept: Expand beyond aviation into global travel mobility platforms.
Execution Ideas:
- Build a Qatar Travel Cloud offering combined airline + ground + hotel + experience subscription bundles.
- Integrate with digital nomad visas and global co-working providers.
- Use Qatar Airways’ brand trust to manage end-to-end lifestyle mobility.
Impact:
Positions the airline as a borderless lifestyle brand rather than a regional carrier.
⚔️ Strategic Summary Table
Dimension
Emirates / Etihad / Turkish
Qatar Airways Blue Ocean Move
Business Model
Premium flight sales
Subscription-based mobility
Customer Experience
Luxury inflight
Lifestyle ecosystem
Loyalty
Points system
Personalized, data-driven travel intelligence
Sustainability
Limited
Decarbonized luxury leadership
Transit
Layover hub
Experiential destination
Revenue Source
Seat + ancillaries
Recurring revenue + data + ecosystem monetization
From Blogger iPhone client
Travel Subscription Model
Yes — several airlines have tried variants of subscription or membership models for travel. Depending on how broadly you define “subscription,” there are loyalty programs, unlimited-flight passes, bundled perks, etc. Here are some examples, plus what has worked (and what challenges come with them):
Examples of airline subscription / membership travel models
- Wizz Air – “All You Can Fly”
- Wizz Air introduced a subscription that lets members fly “unlimited” across its network for a fixed annual fee (e.g. ~€499, rising to €599).
- There are limitations: fees per segment (~€9.99), booking window constraints (you must book a few days ahead), limited availability and seats, minimal baggage included, etc.
- Ryanair – Prime membership
- Ryanair launched “Prime,” a €79/year subscription giving reserved seat selection, travel insurance, plus access to monthly seat sales.
- Jazeera Airways – Jazeera Savers
- Based in Kuwait, this is a subscription club (annual fee) giving discounts on flights and ancillary services; also for families.
- Qatar Airways – Privilege Club Pro
- Rather than unlimited flights, this is a loyalty-subscription hybrid: members pay a monthly or annual fee to receive extra Avios (miles) and “Qpoints” to accelerate status in their loyalty programme.
- Surf Air
- This is a private / semi-private airline model in U.S./Europe that offers “all-you-can-fly” style membership with fixed monthly subscription, using private terminals etc.
- American Airlines – AAirpass
- Historically, AA had a very ambitious plan: the “AAirpass” offered “lifetime” or long-term unlimited first-class flights globally, but at a very high upfront cost. It’s been discontinued in the unlimited form.
What works and what are the challenges
What works:
- It’s attractive to frequent flyers or travellers who fly lots of short/medium-haul trips; subscription makes cost predictable.
- Helps airlines build loyalty, get recurring revenue up front.
- Can fill less popular flights and improve utilisation (especially for budget / low-cost carriers).
Challenges:
- Capacity constraints / availability: To avoid cannibalization of regular fare revenue, airlines often restrict which seats are available to sub-holders, or limit booking windows, days/times, etc.
- Cost vs usage: If someone flies “a lot,” the airline needs to ensure the subscription revenue covers costs (fuel, crew, wear & tear). Otherwise the model loses money.
- Pricing: Pricing it too low risks loss; too high and fewer people buy.
- Ancillaries (baggage, seat selection, etc.): Usually these are excluded or limited, or have extra fees.
- Operational complexity: Managing “unlimited” passes requires forecasting and balancing load, because overuse or clustering can create bottlenecks.
So in summary: yes, various airlines have already packaged travel in subscription-like models. Some are more “membership + perks,” others are closer to unlimited or semi-unlimited flights. If you want, I can survey a more exhaustive list, or look by region (Middle East, Asia, etc.) to see how common this is. Do you want me to pull that together?
Medallion layer management
Managing Medallion Architecture schemas (Bronze → Silver → Gold) dynamically using open-source tools is an efficient and scalable way to handle schema evolution, automation, and governance without relying on commercial tools.
The goal is to have a system that can read schema definitions dynamically (from YAML or JSON), automatically generate and manage schemas and views, support schema evolution when new columns are added or removed, and work consistently across all three layers — Bronze, Silver, and Gold — while remaining fully open-source and version-controlled.
Recommended open-source stack includes:
- Schema storage: YAML or JSON with Git for versioning
- Schema versioning: Liquibase or schemachange
- Data modeling: dbt Core (open source)
- Metadata and validation: Great Expectations or pydantic
- Workflow orchestration: Apache Airflow or Dagster
- Schema registry (optional): Apache Atlas or Amundsen for lineage tracking
For the Bronze layer, which handles raw ingestion, schemas can be inferred automatically from data sources such as Parquet, JSON, or CSV using Python libraries like pandas or pyarrow. The inferred schema is then stored as YAML for tracking and governance. Technologies such as Delta Lake or Apache Iceberg can be used to handle schema evolution and metadata automatically. The YAML file for Bronze would typically define the data source location, inference date, and the inferred schema fields.
In the Silver layer, which focuses on standardized and cleaned data, dynamic schema management comes into full use. YAML is used to define expected schemas, which can then be validated against the actual data before transformation. Great Expectations can enforce schema and data validation, while Jinja templating can be used to dynamically generate SQL that applies cleaning, casting, and normalization logic. For example, columns can be automatically cast to their expected types or cleaned using standard transformations defined in YAML. These transformations can then be executed using dbt Core or custom Python SQL runners.
The Gold layer is designed for aggregations and business-level transformations. Here, you can use YAML-driven metric definitions, specifying KPIs, calculations, and grouping logic. SQL views or materialized views are generated dynamically from these YAML definitions, providing a curated, business-ready data layer. These gold views can also be refreshed periodically using orchestration tools like Airflow.
Schema evolution is managed by automatically detecting changes, such as new columns in the Bronze layer, and propagating them downstream where appropriate. Every schema update is versioned in Git or logged in a changelog YAML with details like version number, added or dropped columns, and timestamps. Open-source tools like schemachange, Liquibase, or pyiceberg help track and apply these schema changes across environments.
Automation is typically handled through an orchestration pipeline such as Airflow or Dagster, which can run jobs in sequence: ingesting raw data and inferring schema for Bronze, storing or updating the schema YAML, validating and cleaning data for Silver, generating and updating Gold views, and finally notifying stakeholders of any schema changes.
A recommended folder structure for this approach includes three schema folders (bronze, silver, gold) each containing YAML files for datasets, a scripts folder with Python utilities for schema generation and validation, a dbt project folder for model definitions, and a folder for orchestration DAGs such as medallion_schema_dag.py.
Best practices summary:
For Bronze, schemas are inferred automatically using tools like pyarrow or delta-rs and stored as YAML for documentation. For Silver, schemas are defined explicitly in YAML and validated with Great Expectations, while dbt or templated SQL applies the cleaning and transformation. For Gold, schemas are YAML-defined and version-controlled, with SQL logic dynamically generated to build metrics or views for business consumption.
Combining open-source tools such as Delta Lake or Iceberg for data storage and evolution, dbt Core for transformations, Great Expectations for validation, Airflow or Dagster for orchestration, and YAML with Git for schema as code provides a fully open-source, dynamic, and maintainable framework for Medallion Architecture schema management.
If needed, this setup can be extended with a fully open-source schema governance framework by integrating Apache Atlas or Amundsen for lineage tracking and metadata discovery.
If you’d like, a complete open-source architecture diagram and folder template can be generated that illustrates how YAML, Python, dbt, and Great Expectations interact to implement this dynamic Medallion schema management approach.
If you want fully open-source dynamic schema governance, you can combine:
- Delta Lake or Iceberg for storage and schema evolution
- dbt Core for transformation
- Great Expectations for schema validation
- Airflow or Dagster for orchestration
- YAML + Git for schema as code
Folder structure
/schemas
/bronze
customers.yaml
orders.yaml
/silver
customers_clean.yaml
orders_enriched.yaml
/gold
customer_metrics.yaml
/scripts
schema_generator.py
schema_validator.py
/dbt_project
models/
bronze/
silver/
gold/
/airflow_dags
medallion_schema_dag.py
Dynamic handling of Medallion layer schema
Here’s a clean, single-copy version without code blocks or formatting boxes so you can easily copy it all at once:
Excellent question — this is a very relevant problem in data engineering pipelines following the medallion architecture (Bronze → Silver → Gold).
Let’s go through the best practices for using YAML + Python to define and generate table schemas and views for each layer (Bronze, Silver, Gold).
- Core Concept
- Instead of hardcoding SQL or schema definitions, you maintain YAML files that describe:
- Table name and layer (bronze/silver/gold)
- Columns and types
- Source or transformation logic
- Primary/foreign keys
- Partition and clustering details
Your Python code then reads the YAML and:
- Generates CREATE TABLE DDL for each layer
- Generates views for transformations (Silver, Gold)
- Optionally executes them (e.g., via BigQuery, Databricks, or Snowflake SDKs)
- Example YAML Schema Design
tables:
- name: customer_bronze
- layer: bronze
- source: gs://raw-data/customers.csv
- description: “Raw customer data from CRM system”
- columns:
- name: customer_id
- type: STRING
- nullable: false
- name: name
- type: STRING
- name: created_at
- type: TIMESTAMP
- metadata:
- partition_by: created_at
- format: parquet
- name: customer_silver
- layer: silver
- source_table: customer_bronze
- description: “Cleaned and deduplicated customer data”
- transformations:
- “SELECT DISTINCT * FROM {{source_table}}”
- columns:
- name: customer_id
- type: STRING
- name: name
- type: STRING
- name: created_at
- type: TIMESTAMP
- name: customer_gold
- layer: gold
- source_table: customer_silver
- description: “Customer aggregated metrics”
- transformations:
- |
- SELECT
- customer_id,
- COUNT(*) AS transaction_count,
- MAX(created_at) AS last_transaction
- FROM {{source_table}}
- GROUP BY customer_id
- columns:
- name: customer_id
- type: STRING
- name: transaction_count
- type: INTEGER
- name: last_transaction
- type: TIMESTAMP
- Python Script to Generate DDL and Views
import yaml
from jinja2 import Template
def load_yaml(yaml_file):
with open(yaml_file, ‘r’) as f:
return yaml.safe_load(f)
def generate_create_table_sql(table):
cols = “,\n “.join([
f”{col[‘name’]} {col[‘type’]}{’’ if col.get(‘nullable’, True) else ’ NOT NULL’}”
for col in table[‘columns’]
])
table_name = table[‘name’]
sql = f”CREATE OR REPLACE TABLE {table_name} (\n {cols}\n)”
if ‘metadata’ in table and table[‘metadata’].get(‘partition_by’):
sql += f”\nPARTITION BY {table[‘metadata’][‘partition_by’]}”
if ‘metadata’ in table and table[‘metadata’].get(‘format’):
sql += f”\nOPTIONS (format=’{table[‘metadata’][‘format’]}’)”
return sql + “;”
def generate_view_sql(table):
if ‘transformations’ not in table:
return None
source_table = table.get(‘source_table’)
transformation_sql = “\n”.join(table[‘transformations’])
sql_template = Template(transformation_sql)
rendered_sql = sql_template.render(source_table=source_table)
return f”CREATE OR REPLACE VIEW {table[‘name’]} AS\n{rendered_sql};”
if name == “main”:
schema = load_yaml(‘tables.yaml’)
for table in schema[‘tables’]:
if table[‘layer’] == ‘bronze’:
ddl = generate_create_table_sql(table)
else:
ddl = generate_view_sql(table)
print(f”\n– {table[‘name’].upper()} ({table[‘layer’]})\n{ddl}\n”)
- Best Practices Summary
YAML Design
- Keep each layer’s YAML separate (bronze.yaml, silver.yaml, gold.yaml) for modularity.
- Use Jinja templating inside YAML for reusable transformations.
- Add metadata for partitioning, file format, and owner/team.
Code Design
- Use a class-based model (Table, Column) to represent tables.
- Validate YAML schema using pydantic or cerberus.
- Add unit tests to verify SQL generation.
- Optionally, store schema history (for auditing schema evolution).
Layer Logic
Creating Schema at runtime using Python
Excellent — this is a powerful and very reusable data engineering utility. ✅
Let’s design a Python function that:
- Reads a YAML file describing a BigQuery table schema
- Generates a BigQuery CREATE TABLE SQL statement
- Supports all major BigQuery data types, including nested STRUCT and repeated ARRAY fields
Below you’ll find a clean, copy-friendly full script (no code cells), including an example YAML file and example output.
Example YAML (table_schema.yaml)
table_name: sales_data
dataset_name: analytics
columns:
- name: order_id
- type: INT64
- mode: REQUIRED
- name: customer_name
- type: STRING
- mode: NULLABLE
- name: order_date
- type: DATE
- mode: NULLABLE
- name: total_amount
- type: FLOAT64
- mode: NULLABLE
- name: is_priority
- type: BOOL
- mode: NULLABLE
- name: items
- type: RECORD
- mode: REPEATED
- fields:
- name: item_id
- type: STRING
- mode: REQUIRED
- name: quantity
- type: INT64
- mode: NULLABLE
- name: attributes
- type: RECORD
- mode: NULLABLE
- fields:
- name: color
- type: STRING
- mode: NULLABLE
- name: size
- type: STRING
- mode: NULLABLE
Python Script (generate_bigquery_create.py)
import yaml
def load_yaml_schema(file_path):
“”“Load table schema from YAML file.”””
with open(file_path, ‘r’) as f:
return yaml.safe_load(f)
def generate_field_sql(field):
“”“Recursively generate SQL for a field, including nested STRUCT and ARRAY.”””
name = field[“name”]
field_type = field[“type”].upper()
mode = field.get(“mode”, “NULLABLE”).upper()
# If this field is a RECORD/STRUCT
if field_type in ("RECORD", "STRUCT"):
nested_fields = field.get("fields", [])
nested_sql_parts = [generate_field_sql(f) for f in nested_fields]
struct_sql = f"STRUCT< {', '.join(nested_sql_parts)} >"
base_sql = f"{name} {struct_sql}"
else:
base_sql = f"{name} {field_type}"
# Handle ARRAY / REPEATED fields
if mode == "REPEATED":
base_sql = f"{name} ARRAY<{field_type if field_type not in ('RECORD', 'STRUCT') else f'STRUCT<{', '.join([generate_field_sql(f) for f in field.get('fields', [])])}>'}>"
return base_sql
def generate_create_table_sql(schema_dict):
“”“Generate BigQuery CREATE TABLE statement from schema dictionary.”””
dataset_name = schema_dict[“dataset_name”]
table_name = schema_dict[“table_name”]
columns = schema_dict[“columns”]
column_sql_parts = [generate_field_sql(col) for col in columns]
columns_sql = ",\n ".join(column_sql_parts)
sql = f"""CREATE OR REPLACE TABLE `{dataset_name}.{table_name}` (
column_sql_parts = [generate_field_sql(col) for col in columns]
columns_sql = ",\n ".join(column_sql_parts)
sql = f"""CREATE OR REPLACE TABLE `{dataset_name}.{table_name}` (
{columns_sql}
);”””
return sql
def main():
yaml_path = “table_schema.yaml” # path to your YAML file
schema = load_yaml_schema(yaml_path)
sql = generate_create_table_sql(schema)
print(sql)
if name == “main”:
main()
Example Output
CREATE OR REPLACE TABLE analytics.sales_data (
order_id INT64,
customer_name STRING,
order_date DATE,
total_amount FLOAT64,
is_priority BOOL,
items ARRAY<STRUCT<item_id STRING, quantity INT64, attributes STRUCT<color STRING, size STRING>>>
);
✅ Supported Data Types
- STRING
- INT64
- FLOAT64
- BOOL
- DATE, DATETIME, TIMESTAMP
- RECORD / STRUCT (nested)
- ARRAY / REPEATED (nested or primitive)
✅ Advantages
- Easy to update schemas by editing YAML instead of SQL
- Supports deeply nested JSON-like structures
- Ideal for data pipeline automation or metadata-driven design
Microsoft Power BI using Rest API Python
Excellent — connecting to a Power BI workspace using Python lets you automate publishing, refreshing, or managing datasets via the Power BI REST API.
Here’s a full, clean, copy-friendly guide (no code cells, no formatting issues).
You can select all and copy directly into your Python environment.
Connect to Power BI Workspace using Python
Step 1 — Install required Python libraries
pip install requests msal
Step 2 — Set up Azure AD app (Service Principal)
- Go to Azure Portal → Azure Active Directory → App registrations → New registration
- Note down:
- Application (client) ID
- Directory (tenant) ID
- Create a Client Secret under “Certificates & Secrets”.
- In Power BI Service → Admin portal → Tenant settings → Developer settings, enable:
- Allow service principals to use Power BI APIs
- Allow service principals to access Power BI workspaces
- Add your app to the target workspace:
- Power BI → Workspace → Access → Add → Enter app name → Assign role (Admin or Member)
Step 3 — Define authentication details in Python
import requests
import msal
Tenant ID, Client ID, and Client Secret from your Azure AD app
tenant_id = “YOUR_TENANT_ID”
client_id = “YOUR_CLIENT_ID”
client_secret = “YOUR_CLIENT_SECRET”
Power BI API scope and authority
authority = f”https://login.microsoftonline.com/{tenant_id}”
scope = [“https://analysis.windows.net/powerbi/api/.default”]
Create MSAL confidential client app
app = msal.ConfidentialClientApplication(
client_id,
authority=authority,
client_credential=client_secret
)
Get access token
token_result = app.acquire_token_for_client(scopes=scope)
access_token = token_result[“access_token”]
print(“Access token acquired successfully!”)
Step 4 — Connect to Power BI and list all workspaces
headers = {
“Authorization”: f”Bearer {access_token}”
}
response = requests.get(“https://api.powerbi.com/v1.0/myorg/groups”, headers=headers)
if response.status_code == 200:
workspaces = response.json()[“value”]
for ws in workspaces:
print(f”Name: {ws[‘name’]} | ID: {ws[‘id’]}”)
else:
print(“Error:”, response.status_code, response.text)
Step 5 — List all reports in a specific workspace
workspace_id = “YOUR_WORKSPACE_ID”
url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/reports”
response = requests.get(url, headers=headers)
if response.status_code == 200:
reports = response.json()[“value”]
for report in reports:
print(f”Report: {report[‘name’]} | ID: {report[‘id’]}”)
else:
print(“Error:”, response.status_code, response.text)
Step 6 — (Optional) Upload a new .pbix report to workspace
pbix_file_path = “C:\Reports\FinanceDashboard.pbix”
dataset_display_name = “FinanceDashboard”
url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/imports?datasetDisplayName={dataset_display_name}”
with open(pbix_file_path, “rb”) as pbix_file:
response = requests.post(
url,
headers={
“Authorization”: f”Bearer {access_token}”,
“Content-Type”: “application/octet-stream”
},
data=pbix_file
)
if response.status_code in [200, 202]:
print(“Report uploaded successfully!”)
else:
print(“Error:”, response.status_code, response.text)
✅ Notes
- The msal library handles secure Azure AD authentication.
- The access token is valid for about 1 hour — refresh when needed.
- You can perform additional actions using the Power BI REST API (refresh datasets, rebind reports, delete reports, etc.).
- For production automation, store secrets in Azure Key Vault.
Would you like me to extend this with dataset refresh automation (Python script that triggers a refresh and checks its status)?
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