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Data and AI engineering, scoped to business outcomes.

ByteLoom designs, builds and modernizes Databricks lakehouses, enterprise data pipelines, AWS cloud architecture and production AI applications. Each engagement starts from the business problem and ends with systems your own team can run and extend.

Discuss Your Project How engagements work

Start from the problem

Databricks consulting

Databricks & Lakehouse Engineering

A lakehouse that is governed by default, fast where it matters and sized to its workload, built so it does not need retrofitting a year from now.

When this is the right engagement

Most Databricks estates start with a few teams and a few workspaces. By the time the platform carries production reporting, data products and machine learning, early shortcuts show up as inconsistent table design, permissions granted by hand, clusters sized by habit and pipelines nobody wants to change.

  • Databricks spend is growing faster than the workloads it supports, and nobody can say which jobs drive it.
  • Tables still live in the workspace Hive metastore, and the move to Unity Catalog keeps slipping.
  • Jobs meet their deadlines because more compute was added, not because the jobs were fixed.
  • Teams follow different conventions for layers, naming and table layout, so every integration is bespoke.
  • A new streaming or near-real-time requirement does not fit the current batch design.
  • The platform was set up quickly and now has to meet enterprise security, audit and environment standards.

What you end up with

A documented lakehouse architecture
Layers, environments, workspace and catalog topology, with each significant decision and its trade-offs recorded in an architecture decision record.
A Unity Catalog governance model
Catalogs, schemas, groups and privileges mapped to how your organization owns data, and defined in code rather than set by hand.
Pipelines built to one standard
Production ingestion and transformation pipelines with tests, data quality expectations and CI/CD deployment, written so your engineers can extend them.
A measured performance and cost baseline
Run times and costs for the workloads that matter, the changes made to them, and the measured effect of each change.

Capabilities

Lakehouse architecture
Medallion layers, environment isolation and catalog topology designed around data ownership, so the platform grows by adding domains rather than exceptions.
Spark optimization
Diagnosis from query plans and the Spark UI: shuffle, skew, spill, small files and join strategy. The aim is the same output in less time, on less compute.
Delta Lake
Table design that holds up as data grows: liquid clustering or partitioning chosen per access pattern, compaction, MERGE patterns for change data and deliberate retention settings.
Unity Catalog
One place for access control, lineage and audit across workspaces, including row filters, column masks, external locations and a planned migration from the Hive metastore.
Streaming and batch pipelines
Incremental ingestion with Auto Loader, Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) and Structured Streaming where latency requires it, with one design for both modes where that is simpler.
Databricks platform modernization
Older workspaces moved onto current foundations: Unity Catalog, serverless or right-sized compute, deployment with Declarative Automation Bundles (formerly Databricks Asset Bundles), and system tables for cost and usage visibility.

How the work is shaped

Three common starting points. Each can stand alone or lead into the next.

Platform assessment
A fixed-scope review of workspace configuration, compute, governance and the most expensive or fragile workloads, ending in findings with evidence and a prioritized plan.
In development A structured version of this assessment, the ByteLoom Lakehouse Health Check, is being built as a reusable accelerator.
Lakehouse build or modernization
Design and implementation of a new platform, a new domain or a modernized workspace estate, from catalog setup to the first production pipelines, delivered alongside your engineers.
Performance and cost engagement
Focused work on the workloads that cost the most or miss their deadlines, with a before-and-after measurement for every change.

Enterprise data engineering

Enterprise Data Platform Modernization

Legacy ETL estates moved to modern, observable pipelines, with reconciliation evidence the business can sign off on before anything is switched off.

When this is the right engagement

Legacy ETL platforms rarely fail all at once. They become expensive to license, hard to staff and risky to change, while the business still depends on every job they run. A migration has to replace the platform without breaking the reports, extracts and downstream systems built on it, and it has to prove equivalence with data rather than assurances.

  • A license renewal or end-of-support date for Talend or another ETL tool is forcing a decision.
  • Hundreds of jobs encode business rules that are documented nowhere else.
  • An earlier migration stalled because nobody could prove the new outputs matched the old ones.
  • Overnight failures are fixed by hand, and on-call depends on the few people who know the history.
  • Schedules and dependencies are split across cron, the ETL tool and manual steps.
  • Data quality problems are found by the people reading the reports, not by the pipelines.

How a migration runs

Jobs move in waves. Each wave is converted, run in parallel with the legacy platform and reconciled before anything is switched off, so risk stays contained and progress is visible.

  1. Inventory

    Jobs, sources, targets, schedules and owners, cataloged.

  2. Map

    A conversion pattern chosen for each group of similar jobs.

  3. Convert

    Logic rebuilt as tested code on the target platform.

  4. Run in parallel

    Legacy and new pipelines fed the same inputs.

  5. Reconcile

    Outputs compared, and every difference explained.

  6. Cut over

    Consumers switched, then the legacy job retired.

What you end up with

A complete migration inventory
Every job, source, target and dependency cataloged and grouped into waves by risk and business value, with a conversion pattern chosen for each group.
Converted, tested pipelines
Legacy logic rebuilt as maintainable Spark and SQL with automated tests: a rebuild for the new platform, not a line-by-line transliteration of the old tool.
Reconciliation evidence
Counts, aggregates and key-level comparisons between legacy and new outputs across parallel runs, presented so data owners can review and sign off.
A controlled cutover and decommission
Cutover wave by wave against agreed rollback criteria, then retirement of the legacy jobs once equivalence is accepted.

Capabilities

Legacy ETL migrations
A repeatable method for moving job estates off older platforms: inventory, pattern mapping, wave planning and parallel running, with progress measured job by job.
Talend and ETL modernization
Talend jobs, other ETL tools and stored-procedure estates analyzed for the business logic they carry, then rebuilt as version-controlled code on Databricks or your chosen target.
Data integration and orchestration
Ingestion from databases, files, APIs and change data capture feeds, with schedules, dependencies and retries managed in one orchestrator, such as Lakeflow Jobs or Apache Airflow.
Data quality
Expectations defined in code at each layer, covering schema, completeness, validity and freshness, so bad data is stopped or quarantined before it reaches consumers.
Reconciliation and validation
Automated comparison of legacy and target outputs on every parallel run, so equivalence is demonstrated with data and every difference is explained before cutover.
Production pipeline reliability
Idempotent, restartable jobs, alerts for failures and for late or missing data, and runbooks, so incidents are rare and recovery is routine.

How the work is shaped

Migration assessment
A fixed-scope inventory of the legacy estate with a conversion approach, a wave plan and effort ranges, so the program can be budgeted on evidence.
Wave-based migration program
Conversion, parallel running, reconciliation and cutover, one wave at a time, with each wave signed off before the next begins.
Pipeline reliability review
For estates that are staying where they are: failure patterns, orchestration and monitoring reviewed, and the fixes that remove the most manual effort put first.

Cloud data architecture

Cloud & Data Architecture

AWS foundations and data platform architecture expressed as code: secure by design, reproducible across environments and sized to the workloads they serve.

A data platform is only as dependable as the cloud underneath it. Network, identity, storage and encryption decisions made early are expensive to undo, and environments built by hand drift apart. The architecture is designed deliberately and captured in Infrastructure as Code, so it can be reviewed, audited and rebuilt on demand.

When this is the right engagement

  • Environments were built in the console and no longer match each other.
  • Security and audit reviews keep raising the same findings on access, encryption or network exposure.
  • Data platform costs cannot be attributed to teams, products or workloads.
  • A new data platform or Databricks deployment is planned, and the AWS foundation is not ready for it.
  • Architecture decisions live in people's heads, so every new project argues them again.

Capabilities

AWS architecture
Multi-account structure, VPC design and private connectivity, IAM, S3 layout and KMS encryption for data platforms, including Databricks deployments on AWS.
Infrastructure as Code
Terraform for AWS resources and the Databricks Terraform provider for workspaces, catalogs, permissions and jobs, with shared modules, remote state and CI/CD.
Data platform architecture
The end-to-end design across ingestion, storage, processing, serving and consumption, with clear boundaries between platform and domain teams.
Performance and cost optimization
Compute matched to each workload, storage lifecycle policies, the right mix of on-demand, spot and serverless capacity, and cost attribution through tags and billing data.
Security and governance
Identity federation, least-privilege roles, network isolation, key management and audit trails, designed together with the data governance model rather than separately.

What you end up with

A target architecture, written down
Account structure, network layout, identity model, storage and data flows, with architecture decision records that explain each choice.
Infrastructure as Code
Terraform modules and pipelines for the platform, so environments are created and changed through reviewed pull requests.
A security baseline
Least-privilege access, encryption, private networking and audit logging, mapped to the controls your security team needs to evidence.
A cost model
Tagging, budgets and a view of cost by workload, with optimization opportunities ranked by effort and expected saving.

How the work is shaped

Architecture review
An independent review of a current or proposed architecture for security, reliability, cost and operability, with written findings and a prioritized remediation plan.
Foundation build
Design and Terraform implementation of the AWS foundation for a data platform, from accounts and networking to the first workspace, handed over as code your team owns.
Cost and performance review
A focused analysis of platform spend and workload performance, with changes ranked by saving and effort, and a baseline to measure them against.

AI and machine learning engineering

AI Engineering & Observability

Retrieval-augmented and LLM applications taken from prototype to production, with tracing, evaluation, cost controls and governance built in from the start.

When this is the right engagement

An LLM prototype can be built in days. Running it in production, where answers must be grounded, costs predictable, data access governed and quality measured over time, is an engineering problem. AI applications are treated like any other production system: observable, tested and owned.

  • A promising RAG or assistant prototype now needs to become a supported service.
  • Nobody can say what an AI feature costs per request, per user or per month.
  • Answer quality is judged by anecdote, with no evaluation set and no way to catch regressions.
  • Security and compliance teams are asking what data the model can see and how outputs are reviewed.
  • Several teams are building AI features with different models, prompts and tools, and no shared standard.
Illustrative
  1. request

    Caller, application version, input and final response

  2. retrieve

    Query, documents returned, relevance scores, latency

  3. assemble

    Prompt template version, context size in tokens

  4. generate

    Model and version, input and output tokens, cost, latency

  5. evaluate

    Quality checks such as groundedness and relevance, user feedback

What one traced request through a RAG application records. The spans and fields shown are an example; they depend on the application.

Capabilities

RAG architectures
Parsing, chunking, embeddings, vector search and reranking designed around the content and the questions, with retrieval quality measured separately from answer quality.
Enterprise LLM applications
Assistants, document extraction, summarization and agent workflows integrated with enterprise systems, identity and data permissions.
AI observability
Tracing of prompts, retrieved context, tool calls and responses, so a poor answer can be traced to its cause rather than guessed at.
Token usage and cost tracking
Token counts and cost attributed per request, feature, team and model, with budgets and alerts before spend becomes a surprise.
MLflow
Experiment tracking, MLflow Tracing, evaluation runs and models registered in Unity Catalog, giving one record of what was deployed and how it performed.
Production AI governance
Approval workflows, audit trails, data access controls and human review, proportionate to the risk of each use case.

What you end up with

A production RAG or LLM application
Retrieval, prompts, model access and the application itself, built, tested and deployed with the same discipline as the rest of the platform.
End-to-end observability
A trace for every request through retrieval and generation, with latency, token usage, cost and errors on dashboards and alerts.
An evaluation framework
A curated evaluation set, automated quality checks and a release gate, so changes to prompts, models or retrieval are measured before they ship.
An AI governance model
Versioned prompts and models, data access through Unity Catalog permissions, and review steps for outputs where the risk warrants it.

How the work is shaped

Production readiness review
An assessment of a prototype or live service covering architecture, evaluation, observability, cost and governance, with the gaps to close before launch.
Prototype to production
An AI application engineered into a supported production service, including the evaluation and observability it needs to stay that way.
Observability and evaluation setup
Tracing, cost tracking and evaluation added to AI applications that are already running, so quality and spend become measurable.

Engagement model

From first conversation to handover.

Every engagement follows the same arc, scaled to its size. It starts with a conversation with the architect who would do the work, and each step ends with something you keep, whether or not the work continues.

  1. Assess

    Understand the platform, the constraints and the outcome that matters, from the code, job history and cost data as well as the diagrams.

    You receive

    • Findings, backed by evidence
    • A prioritized plan with effort ranges
    • A scoped proposal for the next step

  2. Design

    Agree the target architecture and the route to it, with trade-offs made explicit and decisions written down before the build starts.

    You receive

    • A target architecture
    • Architecture decision records
    • A delivery plan with acceptance criteria

  3. Build

    Implement in your environment and your repositories, alongside your engineers, with progress shown as working software and measured results.

    You receive

    • Production code and Infrastructure as Code
    • Tests, CI/CD and monitoring
    • Regular demos against the acceptance criteria

  4. Hand over

    Make sure your team can run, change and extend what was built without depending on ByteLoom.

    You receive

    • Runbooks and documentation
    • Walkthroughs and pairing sessions
    • A clear record of what is done, what is not, and why

Ways to engage

Choose the shape that fits your team.

The same senior, hands-on engineering in four commercial shapes. Start with the one that fits the problem and the people you already have; one often leads into another.

  • Outcome-based project

    A defined outcome with agreed scope and acceptance criteria, such as a migrated job estate, a Unity Catalog rollout or an AI service in production.

    Suits a clear goal and the need for one accountable owner of delivery.

  • Embedded architect

    A senior architect inside your team for an agreed period, setting direction, reviewing designs and code, and building alongside your engineers.

    Suits a capable team that needs architecture leadership more than extra hands.

  • Architecture or platform review

    A fixed-scope, independent review of a platform, a design or a migration plan, ending in written findings and a prioritized plan.

    Suits a major decision ahead, or a problem whose cause is unclear.

  • Specialist capacity for delivery partners

    Hands-on Databricks, data engineering and AI engineering capacity for system integrators and consultancies, working inside your delivery structure.

    Suits a program that needs depth on a specific workstream.

How ByteLoom fits

Working alongside Databricks account teams and system integrators

Hands-on capacity that fits around an existing program and an existing relationship, with a clear scope and no surprises.

Discuss specialist capacity
For system integrators and consultancies
ByteLoom can work as a subcontractor under your statement of work, follow your delivery methods and tooling, and stay within the scope you set. The client relationship stays with you.
For Databricks account teams
When a customer needs hands-on architecture or delivery help, ByteLoom contracts with the customer directly, as an independent consultancy, for a clearly defined workstream.
In every case
Written scope, documentation your teams can use after the engagement, and no ambiguity about who is doing the work.

Independence

An independent practice.

ByteLoom Consulting is an independent consultancy. It is not a Databricks partner, and the company holds no Databricks partner status, accreditation or endorsement.

The founder is individually qualified as a Databricks Resident Solutions Architect through the Databricks Delivery Provider Program. That qualification belongs to the founder personally, not to the firm.

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.

Discuss Your Project hello@byteloomconsulting.com