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Your entire data stack, in one platform.

Nodeberries is a unified data engineering and analytics platform. Model, ingest, transform, orchestrate, analyse and explore in one connected workspace, with catalog, lineage and governance across all of it.

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Nodeberries platform architecture. Sources (databases, warehouses, lakehouses) feed the Build layer: data model, ingestion, ETL, analytics and orchestration around the Nodeberries core, which serves Explore: the semantic catalog, analytics, governance, data chat, the AI assistant and lineage.
Figure 1Platform architecture: connect your sources, build in one place, explore with governance.

Table 1 · The lifecycle

From source tables to decisions, with one catalog and lineage across every stage.

Stage What you do In code

Model

Introspect, or start from scratch
  • Introspect an existing database to start at once, or create a model from scratch.
  • Design in the editor or in YAML, or a mix of both.
  • Enrich the model with business and technical metadata.
 
# models/sales/customers.yaml
table: customers
columns:
  - { name: id,     type: bigint, key: pk }
  - { name: email,  type: text }
  - { name: region, type: short_text }

Ingest

Simple pipelines
  • Land source tables as staged tables, one pipeline per table, with keys and audit columns.
  • Activate only the tables that should be ingested.
  • Create one pipeline, or many at once from a source database.
 
# pipelines.yaml
pipelines:
  - source: public.orders
    target: stage.stg_orders
    mode: incremental
    unique_key: [id]
    updated_at_column: updated_at

Transform

SQL jobs
  • Turn staged tables into curated tables with SQL jobs.
  • Add semantic metadata to the curated tables.
  • Build data marts for analytics and dashboards.
 
-- jobs/marts/fct_sales.sql
SELECT o.order_date AS date,
       c.region,
       o.amount - o.discount AS net_amount
FROM {{ ref('stg_orders') }} o
JOIN {{ ref('stg_customers') }} c ON c.id = o.customer_id
WHERE o.status <> 'cancelled';

Orchestrate

Workflows and schedules
  • Wire ingestion, ETL jobs and job sets into one graph.
  • Read it as a graph; define it in YAML.
  • Schedule workflows and monitor their runs.
 
# workflows/etl-daily-load.yaml
schedule: { cron: "15 6 * * *", tz: America/New_York }
edges:
  - load_orders → dim_date → end

Analyse

Queries, charts, pages, dashboards
  • Row filters and column masks apply when the query runs.
  • Share, schedule and certify what you build.
  • Follow lineage from a source table to a dashboard.
 
-- Daily revenue (certified)
SELECT date, SUM(net_amount) AS revenue
FROM marts.fct_sales
GROUP BY date ORDER BY date;

Explore

Data chat, catalog, lineage
  • Ask questions in plain language; data chat works over your warehouse through MCP.
  • The same roles, masks and filters apply as everywhere else.
  • Open the catalog entry and lineage behind any answer.
 
you  What are the top 10 customers by revenue?

tool query → marts.fct_sales
tool 10 rows, masks applied for your role

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