Frequently Asked Questions
Straight answers on data platforms, lakehouse & medallion architecture, AI & machine learning, and governance — from a team that builds them.
General
What data engineering and AI services does Acacia Tech Group offer?
We design and build modern data platforms (Snowflake, Databricks, Microsoft Fabric), implement Bronze/Silver/Gold medallion architectures on open table formats like Apache Iceberg and Delta Lake, deliver AI/ML and generative AI solutions, modernize legacy ERP/HR systems, and stand up cloud and DevOps automation across AWS, Azure, and GCP.
Which industries do you typically work with?
We partner with healthcare, financial services, insurance, retail, and Fortune 500 enterprise technology teams. Our work spans regulated environments where data governance, security, and compliance (HIPAA, SOC 2, PCI) are non-negotiable.
How long does a typical data platform engagement take?
A focused proof-of-value can be delivered in 4–6 weeks. A production-ready medallion data platform with Silver and Gold layers, governance, and BI integration typically lands in 3–6 months depending on source complexity and the number of business domains in scope.
Can you work with our existing data team?
Yes. We operate in three modes: advisory (architecture reviews and roadmaps), embedded (senior engineers working alongside your team), and full delivery (we build, you operate). Most clients start advisory and expand from there.
What does engagement pricing look like?
Engagements are scoped by outcome, not hours. We offer fixed-price discovery sprints, milestone-based delivery for platform builds, and retainer models for ongoing optimization. After a free 30-minute consultation we provide a written proposal with clear deliverables and pricing.
How do we get started?
Book a free 30-minute consultation through our scheduling page. We'll review your current state, discuss target outcomes, and outline the fastest path to measurable value — typically a discovery sprint that produces an architecture, roadmap, and ROI estimate within two weeks.
Data Platforms
Do you build on Snowflake, Databricks, or both?
Both — and increasingly, both at the same time. We design composable, multi-engine architectures where Bronze can land in either platform and Silver/Gold are built with dbt (Core, Cloud, or Fusion) on open formats. That lets you query the same Gold tables from Snowflake, Databricks, Trino, DuckDB, or Microsoft Fabric without rebuilding.
What is the Medallion architecture and why do you use it?
Medallion is a layered design pattern — Bronze for raw ingested data, Silver for cleaned and conformed data, and Gold for business-ready aggregates. It gives every table a clear contract, makes lineage and quality testing tractable, and lets analytics, ML, and operational use cases share the same trusted foundation instead of building duplicate pipelines.
Should we use a data warehouse, a data lake, or a lakehouse?
For most enterprises today the answer is a lakehouse on open table formats (Apache Iceberg or Delta Lake). You get warehouse-grade SQL performance and ACID guarantees, lake-scale storage economics, and the freedom to plug in multiple compute engines — Snowflake, Databricks, Trino, DuckDB, Fabric — against the same physical tables. Pure warehouses still win for tightly scoped BI workloads; pure lakes rarely make sense anymore.
How do you handle real-time and streaming data?
We use Kafka, Kinesis, or managed equivalents for ingestion, and tools like Flink, Spark Structured Streaming, or Snowflake/Databricks streaming tables for processing. The same Bronze/Silver/Gold model applies — streams land in Bronze, are conformed in Silver, and are exposed as low-latency Gold views or materializations for dashboards, alerts, and ML features.
AI/ML
How do you approach AI and generative AI projects?
We start with the data foundation. Most AI initiatives fail because the underlying data isn't trustworthy. We make sure your Silver and Gold layers are clean, governed, and observable, then layer in ML models, RAG pipelines, and LLM applications with proper evaluation, guardrails, and cost controls.
How do we know if we're ready for AI or machine learning?
Readiness is mostly a data question, not an algorithm question. You need consistent identifiers across systems, a governed Silver/Gold layer for the domain you want to model, basic data quality monitoring, and a clear business metric the model is supposed to move. If those exist, you're ready. If not, a 4–6 week data foundation sprint usually pays for itself before any model is trained.
What's the difference between predictive ML and generative AI?
Predictive ML learns patterns from your historical data to forecast outcomes — churn, demand, fraud, risk. Generative AI (LLMs) produces new content — summaries, answers, code, drafts — usually grounded in your documents through retrieval-augmented generation (RAG). They solve different problems and we frequently combine them: predictive models drive decisions, generative models explain them in natural language.
How do you keep LLM and generative AI costs under control?
We instrument every call with token, latency, and cost telemetry; route requests to the cheapest model that meets quality thresholds; cache aggressively at the prompt and embedding layer; and gate expensive features behind evaluation suites. Most clients see 40–70% cost reduction versus an unoptimized first deployment without sacrificing answer quality.
Governance
How do you approach data governance without slowing teams down?
Governance has to be embedded in the platform, not bolted on as a review committee. We codify ownership, classifications, access policies, and quality contracts directly in the catalog (Unity Catalog, Snowflake Horizon, Purview, or open-source equivalents) and enforce them in CI. Engineers ship faster because the rules are automated; auditors are happier because everything is observable.
How do you secure sensitive data — PII, PHI, financial records?
Defense in depth. Tokenization or column-level encryption for the most sensitive attributes, attribute-based access control and row-level security in the warehouse/lakehouse, masked Gold views for analytics, full audit logging, and short-lived credentials for every workload. We design to HIPAA, SOC 2, PCI, and GDPR requirements from day one rather than retrofitting them.
What does AI governance and responsible AI look like in practice?
Concretely: a model registry with owner, purpose, training data lineage, and approved use cases; pre-deployment evaluation suites for accuracy, bias, and safety; runtime guardrails for prompt injection, PII leakage, and toxicity on generative systems; ongoing drift and quality monitoring; and a documented human-in-the-loop process for high-stakes decisions. We map all of this to NIST AI RMF and the EU AI Act categories.
Related guides
Bronze/Silver/Gold built with dbt on open table formats.
How the two transformation frameworks compare in practice.
Streaming source changes into a governed medallion platform.
Real-time ingestion into low-latency Gold views.
Data platforms, AI/ML, and cloud modernization.
How we protect PII/PHI and meet HIPAA, SOC 2, PCI.
Still have questions?
Book a free 30-minute consultation and we'll map the fastest path to measurable value for your data & AI initiative.
Book a consultation