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Data Observability Reliability deeply integrated with Databricks

Build data reliability workflows in Databricks that ensure dependable tables, notebooks, and dashboards, delivering trustworthy AI models and Data Products with confidence.

Augment your Databricks workflows with robust anomaly monitoring

SYNQ’s advanced anomaly monitoring automatically detects freshness, volume, schema and other unexpected changes across pipelines, ensuring that anomalies are flagged before they disrupt critical workflows.

Code-level lineage that goes deeper than Unity Catalog

SYNQ enhances data visibility and quality within your Databricks ecosystem, giving you the insights needed to proactively manage and improve data reliability across all your data pipelines, models, and workflows to identify issues before they impact your analytics.

customer_segmentation

orders AS (
  SELECT
    order_id,
    customer_id,
    order_date
  FROM orders
),

order_items AS (
  SELECT
    order_id,
    product_id,
    quantity,
    unit_price
  FROM order_items
),

order_items_total AS (
  SELECT
    order_id,
    SUM(quantity * unit_price) AS total
  FROM order_items
  GROUP BY order_id
),

SELECT
  customer_id,
  SUM(total) AS value,
  NTILE(4) OVER (ORDER BY value) AS value_quartile
FROM orders
JOIN order_items_total USING (order_id)
GROUP BY customer_id

orders

...

SELECT
  order_id,
  customer_id,
  order_date
FROM fivetran.orders

order_items

...

SELECT
  order_id,
  product_id,
  quantity,
  unit_price
FROM fivetran.order_items

Synq lineage understands how data flows through layers of CTEs and subqueries and where in code the logic exactly is, accelerating planning, refactoring and debugging workflows.

End-to-end ownership workflow for your Databricks ecosystem

SYNQ offers streamlined ownership workflows that powers your entire Databricks ecosystem with enhanced visibility and control. SYNQ enables seamless assignment, tracking, and accountability across all your data assets—from pipelines to models and beyond.

By metadata

Tracks data size changes, quickly spotting and alerting on anomalies.

By folder structure

Ensures data growth follows expected patterns, guarding against irregularities.

By smart filters

Checks that data updates arrive on schedule, ensuring timeliness.

End-to-end Incident Management delivering trustworthy AI Models

SYNQ streamlines your incident workflow—covering everything from issue detection and impact assessment to resolution and post-incident analysis. Minimise downtime and reduce Mean Time to Resolution (MTTR)

Operationalise Data Products on Databricks & SYNQ

Manage your critical data use cases. Create tightly-defined data products in Databricks & SYNQ that are semantically connected to specific business use cases & teams. Leverage existing metadata and deliver a use-case centric view of your data.

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