Platform
Built native on Snowflake, not bolted on.
A data & AI practice needs one home, not five vendors to reconcile. So everything Viewnear builds for it lives inside Snowflake (ingestion, transformation, governance, AI): one governed home for the data, one lineage to audit, one context every team and agent shares.
How it comes together
One build, start to finish
What actually happens when we stand up a data foundation: who does what, where each product earns its place, and where the team takes over. dbt is the one external framework we run, natively against Snowflake.
The data starts arriving
In the first sprints we wire Openflow into the ERP, CRM, and SaaS systems the business already runs, and Snowpipe Streaming carries the live feeds. Nothing detours through a middleman: every source lands inside Snowflake, governed from the first table.
It gets shaped into something trustworthy
Our engineers model it with dbt and Snowpark, next to the data, in code the team can read and one day own. Dynamic Tables keep the derived views fresh with no scheduler to babysit.
It lives once, in an open format
The result is one governed copy on Apache Iceberg, readable by any engine the business ever chooses through Open Catalog. The foundation outlives any tool decision, ours included.
Governance runs through everything
The whole way, Horizon Catalog is recording lineage and enforcing policy, and Semantic Views pin down what “revenue” actually means. It is the unglamorous work that makes the AI trustworthy later, and it is where our senior people spend real time working inside the team.
Then the AI has something to stand on
Cortex Analyst answers questions against those shared definitions, Snowflake CoWork gives teams cited answers, and agents act inside AI Agent Identity policies. The results land where people already work: dashboards in Snowsight, apps in Streamlit, answers flowing back into the tools teams already use.
One team answers for the whole arc
Snowflake runs the platform. We design, build, and tune what runs on it with the team in the room, then hand over the keys: documented, and theirs to extend. Most foundations reach production in 8–16 weeks, because discovery fixes the scope up front and every sprint ends with working software in a demo.
On screen
What a governed build looks like
Screens from our build environments, not mockups: the surface the business sees, the engineering behind it, and the governance that makes both trustworthy.
Our conviction
We could stitch five tools together. We won't.
Snowflake sells the platform; we answer for what gets built on it. Every extra tool is another copy of the data, another system to secure, and another place lineage breaks, so building native is how we keep that promise:
One governed copy
No exports, no shadow stacks: the data stays in one place with governance attached.
One lineage to audit
Lineage, access, and policy enforced in a single place that stands up to an audit.
Grounded AI
Agents inherit trusted, certified business context instead of guessing from copies.
Open by design
Apache Iceberg keeps the same governed copy portable and queryable by any engine.
For architects
The parts list
Every product we reach for, and the one-line reason. dbt is the lone external framework; everything else stays native.
Ingestion & movement
Get every source in, batch or streaming, without bolting on a separate ETL vendor.
- OpenflowManaged integration on Apache NiFi; batch + streaming, BYOC or Snowflake-managed.
- Snowpipe StreamingLow-latency, continuous ingestion straight into governed tables.
- DatastreamManaged, Kafka-compatible streaming; data inherits Snowflake governance automatically.Preview
- Zero-Copy IntegrationsNative links to SAP, Salesforce, Workday & more, with no data duplication.Public Preview
Transformation & engineering
Model and transform in-platform, in the languages the team already uses.
- dbtOur transformation framework of choice, run natively against Snowflake.
- SnowparkPython/Java/Scala pipelines and UDFs executing next to the data.
- Dynamic TablesDeclarative, incremental pipelines that keep derived data fresh.
Open storage & interoperability
One governed copy of the data, open to every engine.
- Apache Iceberg v3Open table format with deletion vectors, row lineage, and variant types.
- Open Catalog (Polaris)Bi-directional read/write catalog so Snowflake and external lakes share one source.
- Snowflake-managed IcebergA single live, governed copy across Snowflake and the lake, with no movement.
Governance, security & context
Governance, lineage, and trusted business context built in: the foundation AI actually needs.
- Horizon CatalogLineage, access history, classification, and policy across the estate.
- Horizon ContextCollect, enrich, and activate metadata with semantic + keyword search.Private Preview
- Cortex SenseRuntime layer that assembles data + business definitions for AI agents.
- Semantic ViewsShared business definitions so every query and agent speaks the same language.
- AI Agent IdentityVerifiable identity and per-agent RBAC for every AI agent, with full audit trails.
AI & agents
Production AI that runs securely next to governed data; models never see ungoverned copies.
- Cortex AISQLCall LLMs and ML functions directly from SQL: COMPLETE, SENTIMENT, SUMMARIZE.
- Cortex AnalystNatural-language questions answered against governed semantic models.
- Snowflake CoWorkAgentic, cited answers and governed dashboards for knowledge workers.
- Snowflake CoCoCoding agent for enterprise AI development, validated before production.
- Cortex TrainingFine-tune open-weight models on managed GPUs; data never moves.Preview
Consumption & apps
Put insight where people work: on governed data, not a separate BI stack to secure.
- SnowsightGoverned dashboards and ad-hoc exploration out of the box.
- Streamlit in SnowflakeInteractive data apps shipped right next to the data.
- Snowflake CoWorkAsk-in-plain-language analytics for the whole business.
Proof
This stack, in production
From hand-sorted documents to 95% accurate claims classification in seconds
A claims processing company was sorting documents from many insurance providers by hand, causing delays, errors, and lost files. Using Snowflake Cortex AI functions like AI_EXTRACT, Viewnear automated classification and data extraction, lifting accuracy from 60% to 95% and cutting per-document handling to four seconds.
Read more
Real-time insight into 20,000+ students across campuses, live in seven weeks
A group of universities serving 20,000+ students across Miami and Latin America had academic records and LMS learning events siloed across campuses. Viewnear built a governed, real-time student data pipeline on Snowflake: a governed environment, native Blackboard Data Share integration, and real-time Caliper event streaming, unifying academic and learning-activity data into one governed source.
Read more
One trusted golden record across eight business domains
A commercial-vehicle dealer group ran sales, service, parts, and the back office on separate systems, with no single version of a customer, vehicle, part, or supplier. Viewnear built a corporate master data foundation on Snowflake: a progressive multi-source golden record across eight business domains, using a Medallion architecture, Openflow ingestion, dbt transformations, and per-domain Snowflake CoWork agents.
Read morePut the practice on one governed stack.
Tell us what runs today. We'll map the fastest path to a single, governed foundation on Snowflake, what to retire along the way, and where the team takes over.