Data engineering
Lakehouse, streaming and governed pipelines — built to feed analytics and AI.
Most AI projects stall on the data, not the model: it sits in silos, arrives late, and nobody can say where a number came from. We build the data platform underneath — an open-format lakehouse, batch and streaming pipelines, and a catalog that governs who and what can read it — so dashboards stay fresh and your AI agents and RAG pipelines work from data you can stand behind.
01 / What it solves
Your data in Apache Iceberg or Delta Lake tables that Databricks, Snowflake, BigQuery and Spark can all read — no lock-in, no copies for every tool.
Kafka and Flink pipelines that land events straight into lakehouse tables, so operational dashboards and AI agents see what happened minutes ago, not yesterday.
Clean, versioned, documented datasets, embeddings and features that RAG pipelines, agents and models can use safely — including documents, images and other multimodal data.
A catalog as the single control point: access policies, lineage from source to report, data contracts and quality checks that catch broken data before your users do.
02 / Stack
Industry-standard, boring in the good way: tools with a future, that the next engineer will know too.
Lakehouse & table formats
Apache Iceberg
Open table format, readable by every major engine
Delta Lake
Open table format native to Databricks
Databricks
Lakehouse platform for data and AI
Snowflake
Cloud data platform with Iceberg tables
BigQuery
Serverless warehouse on Google Cloud
Streaming
Apache Kafka
Event streaming backbone
Apache Flink
Stateful stream processing
Spark Structured Streaming
Spark Structured Streaming for unified batch and streaming
Amazon Kinesis
Managed streaming on AWS
Transformation & orchestration
dbt
Tested, versioned SQL transformations
Apache Airflow
Workflow orchestration at scale
Dagster
Asset-based orchestration with lineage
Apache Spark
Distributed processing for large datasets
Governance & quality
Unity Catalog
Access control, lineage and discovery
Apache Polaris
Open Iceberg REST catalog
Great Expectations
Automated data quality tests
OpenLineage
Open standard for lineage across tools
Data for AI
Vector stores
pgvector, Qdrant, Pinecone and lakehouse-native vector search
Feature stores
Feature stores for consistent training and serving
Document pipelines
Document and multimodal ingestion for RAG
MCP servers
Governed data access for AI agents via MCP servers
03 / Approach
Iceberg or Delta tables you own, so you can switch or mix engines later without migrating data again.
The same tables serve nightly reports and real-time use cases, instead of two pipelines that disagree.
Data contracts between producers and consumers, and quality tests on every run, so a schema change upstream doesn’t silently break reports or AI answers.
Compute sized to the workload, storage tiering and usage visibility, so the platform’s bill grows with value, not by surprise.
04 / Standards
We adhere to industry standards and proven methods on every project — they are what makes the estimate hold.
05 / Infrastructure
Good questions
Straight answers to what buyers ask before they start.
It depends on your workloads. WNC usually recommends storing data in an open table format such as Apache Iceberg or Delta Lake, so a warehouse (Snowflake, BigQuery) and a lakehouse engine (Databricks, Spark) can read the same tables. You keep the warehouse experience where it works and avoid locking your data into one engine.
Related work
Projects and products of ours that involved this service.
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Tell us where your data lives today and what it needs to power. A senior engineer writes back with an honest scope and price.
Let’s manage See RAG pipelines
