The Databricks Metric Views alternative with proved rules and the same SQL every time
SynaLemma answers questions on your business data with rules that are proved before they run, then compiled to SQL. The agent never writes SQL, and the same question always gives the same SQL. It compiles to SQL on Databricks.
SynaLemma vs Databricks Metric Views: key differences
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Same question, same SQL
The same question always compiles to the same SQL, and every figure cites the rule it came from. Your team can read, correct or lock that rule.
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The agent never writes SQL
The agent writes rules, not SQL. The engine checks every rule, then writes SQL that only reads your tables: no raw SQL and no injection, formally proved.
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Every rule proved before it runs
A symbolic verifier proves each rule well-formed, safe and terminating. An invalid rule is rejected with the reason, before any SQL runs.
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Graph questions on Databricks
Hierarchies, bills of materials and supply chains are recursive rules, verified to terminate and compiled to SQL on Databricks, as well as PostgreSQL, BigQuery, Trino, Presto, DuckDB and SQLite. No graph database, no migration.
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Assertions that name the faulty rows
Keys, references, totals or transitivity are written in first-order logic. The engine searches the whole table and returns the rows that break them.
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Sovereign by construction, not by contract
Data, rules and model stay on your infrastructure. The engine compiles the SQL, so a small open model is enough, with no per-token fees. The Synalog engine is open source, under Apache-2.0.
Example: a recursive query with assertions
A bill of materials at any depth, with two assertions. The data contains a cycle; the engine reports it before any answer is returned.
# The bill of materials, read from the database.
Component(assembly:, part:, quantity:) :- bom(assembly:, part:, quantity:);
# A part is inside an assembly directly, or inside one of its parts.
@Recursive(Contains, 20);
@Assert(Contains,
transitive: "∀ a b c, Contains a b → Contains b c → Contains a c",
no_cycle: "∀ a, ¬ Contains a a");
Contains(assembly:, part:) distinct :- Component(assembly:, part:);
Contains(assembly:, part:) distinct :-
Contains(assembly:, part: sub),
Component(assembly: sub, part:);SynaLemma vs Databricks Metric Views at a glance
| Criterion | SynaLemma | Databricks Metric Views |
|---|---|---|
| How an agent answers | It writes a rule; the engine proves it and compiles the SQL | Genie turns each question into SQL; responses are nondeterministic |
| What checks the definitions | Every rule proved well-formed, safe and terminating before it runs | YAML validation; declared join cardinalities are not checked at runtime |
| Checks on the data | Assertions in first-order logic that return the rows breaking them | None on metric views: Genie benchmarks compare answers with reference SQL |
| Hierarchies and multi-hop chains | Native recursion at any depth, verified to terminate | No recursion in metric views; recursive CTEs by hand |
| Definitions written in | Synalog, an open-source logic language with a compiler | YAML, created with SQL DDL, Catalog Explorer or Genie Code |
| Joins | Any join the rule states, as far as the question needs | Left outer joins from the fact table; many-to-many keeps the first matching row |
| Language model | The one you choose: a small open model is enough, no per-token fees | Databricks-hosted or partner models (OpenAI, Anthropic) |
| Deployment | On-premises, on your infrastructure | Databricks on AWS, Azure or Google Cloud |
Why teams look for a Databricks Metric Views alternative
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Unchecked cardinalities
A declared at-most-one-match relationship is not validated at runtime; if it is wrong, SUM and COUNT return incorrect results (YAML reference).
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Silent many-to-many
When a dimension matches several rows, the first matching row is selected (YAML reference).
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No recursion in metric views
Metric views have no recursive rules; hierarchies need hand-written recursive CTEs (CTE reference).
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Cloud models
Genie runs on Databricks-hosted or partner models; bringing your own model is not documented (AI trust).
Databricks Metric Views alternative: FAQ
What is the best Databricks Metric Views alternative for AI agents?
SynaLemma. It compiles to SQL on Databricks, and the same question always gives the same SQL. Every rule is proved before it runs, assertions name the faulty rows, and recursion is native.
Do Databricks metric views support recursive queries?
Not in metric views. Org charts and bills of materials need hand-written recursive CTEs in Databricks SQL. In Synalog, recursion is native and verified to terminate.
Are Genie benchmarks the same as assertions?
No. Benchmarks score Genie answers on a list of questions, against reference SQL or with a model as judge. Assertions check a property of every result and return the rows that break it.
Can SynaLemma run with a local model?
Yes. A small open model on your own hardware is enough, because the engine compiles the SQL.
Sources
- Databricks docs: metric views
- Databricks docs: metric view YAML reference
- Databricks docs: query a metric view
- Databricks docs: recursive CTEs
- Databricks docs: Genie concepts
- Databricks docs: tune Genie quality
- Databricks docs: Genie benchmarks
- Databricks docs: AI trust and models
- Synalog on GitHub (Apache-2.0)
Checked against each product's public documentation on the review date. To report an error: contact@synalinks.com.
Other comparisons
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