NeoTek Solutions designs and builds data lakehouse architecture so your reporting, machine learning and generative AI all run on one trusted set of data. A data lakehouse combines the low-cost, flexible storage of a data lake with the reliability and fast queries of a data warehouse. Data lives in open file formats on cloud storage, under a table layer that adds transactions, schemas and version history. Our Nashville data engineers build these platforms on Azure, AWS, Google Cloud and open-source tools.
When to Use This Architecture
Lakehouse vs. Data Warehouse vs. Data Lake
If you run a classic data warehouse, it stores structured, cleaned data in a proprietary database for business intelligence (BI) reporting. It is reliable and fast, but it handles images, documents and raw logs poorly.
A classic data lake stores any file cheaply in cloud storage. Without transactions and governance, we see lakes turn into “data swamps” that people do not trust. We add a table layer and a catalog to the lake, so one platform supports both your BI and your AI.
Good Fits and Simpler Options
A lakehouse fits when you:
- Combine structured data with documents, logs, images or sensor data
- Need the same data for dashboards, ML models and retrieval-augmented generation
- Need versioned, reproducible datasets for model training and audits
A simpler option is often better. If your needs are mainly structured reporting from a few systems, a cloud data warehouse may be enough.
Core Components
Ingestion: Batch, CDC and Streaming
We load files and database extracts on a schedule. For busy source systems we use change data capture (CDC), which reads a database’s change log and copies only inserts, updates and deletes. We add streaming ingestion for continuous events from your applications, devices and sensors.
Cloud Object Storage
All data lands in low-cost, durable object storage in open file formats such as Parquet. Storage is separated from compute, so each workload scales on its own.
Open Table Formats
We build on open table formats, such as Delta Lake and Apache Iceberg, which add a metadata layer over the files. They give you ACID transactions, so reads and writes stay consistent even when jobs run at once. They also support schema enforcement and time travel, which lets you query data as it was at an earlier point.
Medallion Layers: Bronze, Silver and Gold
We organize your data by quality in three layers. Bronze holds raw data exactly as received, so you can replay and audit it. Silver holds cleaned, deduplicated and conformed data. Gold holds the business-ready tables, aggregates and features we build for specific uses.
Processing and Orchestration
Distributed engines such as Apache Spark and SQL engines transform data between layers. An orchestrator schedules jobs, manages dependencies and retries failures. Data quality checks run at each step and stop bad data from moving forward.
Governance Catalog and Lineage
We set up a catalog that lists every dataset with its owner, description, sensitivity and access rules. We record lineage too, so you can see where data came from and every transformation along the way.
Semantic and BI Layer
We define your business metrics, like revenue or on-time delivery, once in a semantic layer. Your BI tools and analysts then query those shared definitions instead of rebuilding their own logic.
Serving Layer for ML and RAG
Gold tables feed feature pipelines and model training. For generative AI, curated documents and records are chunked, converted to embeddings and loaded into a vector index. Embeddings are numeric representations of meaning that let a system find related content. That index powers retrieval-augmented generation (RAG), where an LLM answers using your own data.
How It Works
- Source systems send data through batch loads, CDC or event streams.
- Raw data lands unchanged in bronze tables, with load time and source recorded.
- Pipelines clean, deduplicate, validate and join data into silver tables.
- Business logic builds gold tables, metrics and model features.
- The catalog records schemas, owners, sensitivity tags and lineage for every table.
- BI tools query gold tables through the semantic layer.
- ML pipelines read versioned gold data to train and score models.
- Document and text pipelines create embeddings and refresh vector indexes for RAG applications.
- Monitoring tracks freshness, quality, cost and access across the platform.
Security, Governance and Guardrails
Access control is defined centrally in the catalog, down to table, row and column level where needed. Sensitive fields are tagged, masked or tokenized before they reach broader audiences. Data is encrypted in transit and at rest, and network access stays private.
Lineage and time travel support audits and model reproducibility. Retention and deletion policies are applied consistently across layers, including vector indexes.
For AI use, permissions on source data should carry through to RAG retrieval. Users should never retrieve content they cannot access directly. Our AI governance and security practice helps define these policies.
Reference Stack by Cloud
| Component | Microsoft Azure | AWS | Google Cloud |
|---|---|---|---|
| Object storage | Azure Data Lake Storage | Amazon S3 | Google Cloud Storage |
| Batch ingestion and orchestration | Azure Data Factory | AWS Glue | Cloud Composer and Dataflow |
| Change data capture | Azure Data Factory or Debezium | AWS Database Migration Service | Datastream |
| Streaming ingestion | Azure Event Hubs | Amazon Kinesis | Pub/Sub |
| Processing and table layer | Azure Databricks with Delta Lake | Amazon EMR or AWS Glue with Apache Iceberg | Dataproc or BigQuery with Apache Iceberg |
| Catalog and lineage | Microsoft Purview or Unity Catalog | AWS Glue Data Catalog and AWS Lake Formation | Dataplex |
| SQL and BI | Azure Databricks SQL and Power BI | Amazon Athena or Amazon Redshift with Amazon QuickSight | BigQuery and Looker |
| Vector index for RAG | Azure AI Search | Amazon OpenSearch Service | Vertex AI Vector Search |
Open-source and on-premises options also fit, such as Apache Spark, Delta Lake or Apache Iceberg, Apache Kafka, MLflow and Kubernetes. NeoTek Solutions is vendor-neutral and designs around your existing platforms and skills.
Common Pitfalls
- Loading everything into the lake without owners, descriptions or quality checks
- Skipping the bronze layer, which removes the ability to replay and audit
- Letting each team define core metrics differently
- Building RAG indexes from uncurated documents with no refresh or deletion process
- Applying security per tool instead of centrally in the catalog
- Migrating every source at once instead of starting with one high-value use case
Where We Apply It
In healthcare, a lakehouse unifies clinical, operational and financial data with strong access controls. It supports analytics, forecasting and generative AI over approved content.
In manufacturing and automotive, streaming sensor data joins production, quality and supply data for predictive maintenance and yield analysis.
Example scenario: A health system wants a staff assistant that answers policy questions. The lakehouse curates approved policy documents in gold tables, tracks versions and refreshes the RAG index when policies change.
How Can NeoTek Solutions Help You Build a Lakehouse?
Most AI projects stall on data, not on models. We build the foundation and the pipelines that keep it trustworthy.
- What we buildBatch, CDC and streaming ingestion, bronze, silver and gold layers, a governance catalog, semantic definitions and the feature and vector pipelines AI needs.
- Built on your dataWe map your sources, quality issues and access rules, then scope the platform around a use case with real business value.
- How we workShort cycles with AI-assisted delivery and human review, data quality targets agreed early and usable tables in each cycle, so scope and cost stay visible.
- Skills on the teamData engineers, AI and machine learning engineers, cloud and security specialists and QA in one team, covering ingestion, modeling, governance and testing.
- What makes us differentWe are vendor-neutral across Azure, AWS, Google Cloud and open-source tools, and we work with the warehouse you already run rather than replacing it.
- Governed from the startAccess down to table, row and column level, sensitive fields tagged, source permissions carried through to AI retrieval and no training on your data.
Tell us where your data sits today and what you want it to support. Book a free AI consultation and we will outline a practical first phase.
Frequently Asked Questions
How do you organize the data in our lakehouse?
We use three layers. Bronze holds raw data as received, so you can replay and audit it. Silver holds cleaned and conformed data. Gold holds the business-ready tables and model features your dashboards and AI actually read.
Would you pick Delta Lake or Apache Iceberg for us?
Both are mature open table formats with transactions, schema evolution and time travel. We choose based on your main compute engine and cloud. Delta Lake is native to Databricks, while Iceberg has broad support across many engines, and we are not tied to either.
How does the lakehouse you build feed our AI?
We curate and govern the documents and records your generative AI uses. Our pipelines chunk that content, create embeddings and refresh a vector index. We carry lineage and access rules through, so the AI retrieves only current, approved content each user is allowed to see.
Do we need to replace our existing data warehouse?
Usually not. Many of our clients keep their warehouse for established reporting and add a lakehouse for new AI and unstructured data. We plan a migration path around business value, rather than replacing what works.
Build a Data Foundation Ready for AI
A lakehouse gives your reporting, models and AI assistants one trusted source of data. Our machine learning and data engineering team designs and builds lakehouses on Azure, AWS and Google Cloud. Talk to an AI architect