The bill grows. The reasons are unclear.
Repeated queries, full reloads, reprocessing or resources that no longer fit the workload.
Understand the costBIGQUERY / COMPOSER / AIRFLOW
Reliable pipelines. A better data platform.
For teams already using BigQuery: resolve costly queries, unreliable Composer/Airflow pipelines and improvements your team cannot get to. Hands-on engineering that considers cost, reliability and when your business needs the data.
Direct specialist support · Start by email · Scope agreed before payment
YOUR PLATFORM / A CONNECTED VIEW
From source to the data you need
- 01Sources & loads
Frequency · volume · changes
- 02Composer / Airflow
Dependencies · retries · recovery
- 03BigQuery / SQL
Queries · models · capacity
Cost, reliability and freshness, together.
BigQuery & Composer/AirflowDirect specialist deliveryEmail-first collaborationAgreed scope & access
START WITH THE PROBLEM
What is holding your team back?
Specialist help for teams already using BigQuery who need their Google Cloud data and pipelines to work better.
Pipelines need too much attention.
Failed loads, late data and manual recovery keep interrupting your team.
Improve reliabilityThe improvement backlog keeps growing.
New sources, model changes and architecture decisions need specialist time your team cannot spare.
Move the platform forwardWAYS TO WORK TOGETHER
Start with what matters. Keep improving.
A focused review, a hands-on improvement or ongoing specialist capacity. The right starting point depends on your platform and the problem.
UNDERSTAND
Focused platform review
Understand what drives a cost, a failure or an architecture constraint. Review selected workflows and leave with documented next steps.
For understanding causes and choosing the next step
Explore a focused reviewIMPROVE
Focused implementation
Optimise SQL, fix or redesign a load and improve Composer/Airflow orchestration. Testing and handoff are part of the agreed scope.
For a change with a testable outcome
Explore implementationKEEP IMPROVING
Ongoing care & evolution
Reserved specialist capacity to look after critical workflows and work through improvements. Monthly priorities and capacity agreed with you.
For keeping workflows healthy and moving forward
Explore ongoing careYou can start with any of these options. A well-defined fix can go straight to implementation. How scope and pricing work →
TECHNICAL EXAMPLE / SYNTHETIC DATA
From a troublesome pipeline to a measurable improvement.
How I would approach an intervention. This demonstration uses fictional data; it is not a client case or a claim of savings.
Situation
An orders workflow appends batches to its target table. A retry creates duplicates, and an order-date filter misses late corrections.
Investigation
Connect Airflow runs to BigQuery jobs. Review keys, partitions, late data, dependencies and when the business needs the output.
Proposed change
Validate immutable batches and update by key and version. Confirm update and deletion rules before changing the real workflow.
Validation
Compare rows, keys and amounts with an independent reference; replay the batch and test late data. BigQuery SQL execution, Airflow, cost and recovery still need validation.
View a sample handoff
DELIVERABLE SAMPLE / FICTIONAL DATA
IllustrativeA documented decision your team can review.
An extract showing how a single-workflow review could be presented. A sample of the format and reasoning, without client results.
- Question
- How can retries avoid duplicates and preserve late corrections?
- Evidence to review
- SQL, job history, DAG runs, keys and when the business needs the table.
- Illustrative finding
- Appending the same batch twice duplicates orders; filtering only by order date misses later corrections.
- Proposed decision
- Validate immutable batches and update by key and version, comparing the result with an independent reference.
- Acceptance
- Reconcile keys and values against the reference; replay without duplicates; include a late correction.
- Risk and next step
- Confirm update and deletion semantics. Agree the late-data window and rollback before implementing.
A real handoff identifies sources, period, uncertainty, priorities, owners and agreed checks. Contents depend on scope; the review may recommend implementing, investigating further or stopping.
What a focused review includesTechnical detail: updating and replaying a batch
Illustrative GoogleSQL. Assumes non-null keys, one valid version per key in the batch and a confirmed version contract. It has not been run in BigQuery; conflict, schema and deletion checks must be defined before use.
MERGE demo.orders AS target
USING demo.validated_batch AS source
ON target.order_id = source.order_id
WHEN MATCHED AND source.source_version > target.source_version THEN
UPDATE SET amount_cents = source.amount_cents,
source_version = source.source_version,
updated_at = source.updated_at
WHEN NOT MATCHED THEN
INSERT (order_id, amount_cents, source_version, updated_at)
VALUES (source.order_id, source.amount_cents,
source.source_version, source.updated_at);The DAG should read a stable batch or interval, check the result and publish only after the checks pass. Late-data windows, partitioning, deletions and recovery are designed for the actual workflow.
HOW IT WORKS
A clear first step. Room to build trust.
Start by email. Agree the work. Keep decisions and documentation in your hands.
- 01
Describe the problem
Share the context in writing. The initial fit check by email is free; a technical conversation can be arranged when useful.
- 02
Agree a first engagement
Get scope, deliverables, access, dates, acceptance criteria and the total price. Accept the proposal and pay online through a private link.
- 03
Check the improvement
Receive documentation of the work and how to validate it. Continue with planned support if useful. Production changes require authorisation, tests and rollback.
WORK DIRECTLY WITH THE SPECIALIST
Someone who understands the workflow and does the work.
Hands-on experience with BigQuery, SQL, Python and Composer/Airflow. Review sample decisions, testable changes and documented handoffs before deciding how to work together. Discuss relevant experience directly in a technical conversation.
Expertise and approachSELECTED GUIDES
Useful before you commit.
Pipeline reliability
Make a pipeline change testable
A synthetic workflow, a sample decision and replay checks.
Read example · EnglishOperations
Know what to maintain after launch
A practical checklist for a running warehouse.
Read guide · EnglishScope & cost
Scope a BigQuery engagement
What to include before comparing proposals.
Read guide · EnglishLET’S START WITH THE CONTEXT
What would you like your data platform to do better?
Tell me what is happening and the outcome you need. My first reply will confirm the fit and a next step, or ask for the details needed to define it.
- No mandatory sales call
- No credentials needed to inquire
- No payment before an agreed scope