Direct access to the architect
You work with the founder throughout: in workshops, in code review, and when something breaks. Questions reach the person who made the decision, with no account manager in between.
About
A founder-led boutique for enterprise data and AI engineering.
ByteLoom Consulting designs and builds enterprise data platforms on Databricks and AWS, and takes AI workloads into production. It is run by its founder, a senior data architect who leads and engineers every engagement personally.
- Founder-led boutique consultancy
- Databricks and lakehouse engineering, data platform modernization, cloud data architecture on AWS, AI engineering
- Architecture and hands-on engineering by the same senior practitioner
- Ananke Consulting Inc.
- Independent. Not a Databricks partner.
The practice
Small by design, senior throughout.
Running a boutique practice is a decision about how the work gets done: senior judgment on every decision, one accountable person from the first conversation to the final handover, and no gap between the advice and the code.
ByteLoom exists because of a pattern that shows up across enterprise data programs: architecture and delivery are often separated. One group designs the platform, another builds it, and the distance between the two is where assumptions get lost, estimates drift and rework accumulates.
The practice closes that distance by keeping architecture judgment and hands-on engineering in the same person. Staying small is what makes that possible, so it is a deliberate choice. It also comes with trade-offs, and they are worth stating plainly.
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One accountable lead
The person in the first conversation designs the architecture, writes and reviews the code, and stays through handover. There is no hand-off from a pre-sales architect to a delivery team you have not met.
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Senior attention on every decision
Table design, access models, pipeline code and production incidents all get the same level of experience. Nothing is passed down to a junior bench, because there is no junior bench.
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Capacity stated up front
ByteLoom takes on a small number of engagements at a time and is clear about availability before work starts. If a piece of work needs more hands than one practitioner can give, that is discussed at the outset, with options such as pairing with your own engineers, rather than discovered halfway through.
Messages to hello@byteloomconsulting.com reach the founder directly.
The founder
Founder & Principal Data Architect
ByteLoom's founder is a senior data architect with extensive enterprise experience designing and delivering data platforms on Databricks and AWS. The work spans the whole life of a platform: the architecture and governance model, the pipelines and infrastructure that implement it, and the performance and cost tuning that keeps it dependable once real workloads arrive.
Much of that experience is in modernization: moving legacy ETL estates, including Talend, and traditional data warehouses onto the lakehouse, with Delta Lake, Lakeflow pipelines and Unity Catalog governance, and with reconciliation evidence the business can sign off on. The work also covers cloud architecture on AWS, expressed as Terraform, and AI engineering: retrieval-augmented generation, MLflow, and the observability that production AI workloads need.
The founder works as an architect and as an engineer within the same engagement: leading design reviews and technical decisions with enterprise stakeholders, then writing and reviewing the code that puts those decisions into production. ByteLoom was set up to offer that combination to clients directly.
Qualifications
These belong to the founder personally, not to ByteLoom as a company.
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Databricks Resident Solutions Architect (individually qualified)
An individual qualification held by the founder. ByteLoom Consulting is not a Databricks partner.
Areas of expertise
The ground the founder covers day to day, from the storage layer up to the people and decisions around the platform.
- Data platform and lakehouse
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- Databricks lakehouse architecture
- Delta Lake table design
- Unity Catalog governance and access control
- Lakeflow pipelines (formerly Delta Live Tables)
- Auto Loader ingestion
- Data engineering and migration
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- Apache Spark, PySpark and SQL
- Enterprise ETL modernization
- Talend and legacy ETL migration
- Data warehouse modernization
- Data quality and reconciliation
- Cloud and infrastructure
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- AWS cloud architecture
- Infrastructure as Code with Terraform
- Platform security and access governance
- Performance and cost optimization
- AI engineering
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- Generative AI applications
- Retrieval-augmented generation (RAG)
- MLflow experiment tracking and model lifecycle
- AI observability and evaluation
- Architecture leadership
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- Enterprise data architecture
- Technical leadership
- Design reviews and architecture decisions
- Platform standards and reusable patterns
How ByteLoom works
Design decisions, tested in working code.
Architecture advice is only as good as its fit with real data, real workloads and real budgets. Combining architecture expertise with hands-on delivery means each decision is checked against all three before it hardens into a platform.
Why the combination matters
Most of the expensive problems on a data platform surface during implementation, not design. What matters is whether the person who finds them can also change the design.
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Designs are tested before they are recommended
A proposed table layout, governance model or ingestion pattern is prototyped against representative data first. The recommendation then comes with what was learned: the query plan, the measured cost, the pipeline that already runs.
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Implementation feeds back into the architecture
When the build exposes a constraint, such as a skewed join, an access model that does not match how teams actually work, or a cost profile that will not scale, the person who made the original decision sees it first and revises it.
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Fewer hand-offs, fewer lost assumptions
No architecture document is passed to a separate team to interpret. The reasoning behind each choice stays attached to the work, and is written down for the people who will own it.
What to expect when you work with ByteLoom
The same commitments apply whether the engagement is an architecture review, a migration or a production AI build.
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Decisions in writing
Significant choices are recorded as short architecture decision records: the context, the options considered, the trade-offs and the reason for the choice. Your team keeps the reasoning as well as the result.
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Working code in your repositories
Pipelines, Terraform modules, Unity Catalog grants and tests are delivered as reviewed code in your version control, and deployed through your own release process.
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A handover your team can run with
Runbooks, operating notes and walkthroughs with the engineers who will own the platform. An engagement is designed to end with your team running and changing the system without ByteLoom.
Direction
Where ByteLoom is going
These are intentions, not current capabilities. They are written down so that clients and partners can see the direction of the practice, and judge its progress against it.
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Planned A small network of vetted engineers
As engagements require it, ByteLoom intends to add capacity through a small network of qualified engineers, each vetted for the specific work, rather than by building a large bench. The founder will remain the accountable architect, and anyone who joins an engagement will be introduced to the client before starting.
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In development Reusable accelerators
Repeatable parts of the work, such as assessing the health of a Databricks environment, are being turned into reusable accelerators that make an engagement quicker to start and easier to verify. The first, the ByteLoom Lakehouse Health Check, is in development.
See the accelerators -
Long-term goal Formal Databricks partner status
Over time, ByteLoom intends to work toward becoming a Databricks consulting partner. That is a goal, not a current status: ByteLoom is not a Databricks partner today, and this page will say so until that changes.
Independence
Independence and affiliations
A plain statement of what ByteLoom is and is not, for clients, partners and Databricks account teams who need to know exactly who they are working with.
In short: ByteLoom is independent, it is not a Databricks partner, and the founder's Databricks qualification is an individual one.
- ByteLoom Consulting is operated by Ananke Consulting Inc.
- ByteLoom is an independent consultancy. It is not owned by, affiliated with or endorsed by Databricks, Inc.
- ByteLoom Consulting is not a Databricks partner, and is not approved as a company under the Databricks Delivery Provider Program.
- The founder is individually qualified as a Databricks Resident Solutions Architect through the Databricks Delivery Provider Program. The qualification belongs to the founder personally. It is not a company accreditation and does not imply any endorsement of ByteLoom by Databricks.
- Databricks, Apache Spark, Delta Lake, Unity Catalog, MLflow, AWS, Terraform and other product names are trademarks of their respective owners. They are used on this site only to describe the technologies the practice works with.
Start a conversation
Let's Build What's Next.
Tell us about the platform you are modernizing, the migration you are planning, or the AI workload you need in production. You will hear back from an engineer, not a sales team.