docs: describe downstream Spark consumers generically

src/about/whats-new-23.md

@@ -109,7 +109,7 @@ DataJoint's `<blob@>` codec stores arbitrary Python values as serialized binary
 
 - It's a small, opt-in `@runtime_checkable` `Protocol` — no inheritance, no abstract-method burden on existing codecs.
 - Generic codecs (`<blob@>`, `<hash@>`) remain non-adapting by design; typed codecs (`<float_array@>`, `<image_2d@>`, and future shapes) opt in.
-- The downstream driver is Databricks Linked Delta Tables (the "silver layer"), where every column must render to a Spark-native type to be queryable.
+- The downstream driver is publishing to a Databricks silver layer, where every column must render to a Spark-native type to be queryable.
 
 See the [SparkAdapter Codec Protocol Specification](../reference/specs/spark-adapter.md) and the [explainer](../explanation/spark-adapters.md).
 

src/assets/stylesheets/extra.css

@@ -55,8 +55,8 @@
   /* links */
   /* Links: body-size text needs AA (4.5:1); DataJoint Blue on white is 2.96:1.
      #00537A is the brand-blue family's AA text value (7.35:1) — the same value the
-     diagram theme uses for Imported-tier text. Provisional under dj-brand#22
-     (the working-palette ruling); swap to the ratified token when it lands. */
+     diagram theme uses for Imported-tier text. Provisional until the brand's
+     working palette is ratified; swap to the ratified token then. */
   --md-typeset-a-color: #00537A;
   /* single interactive emphasis: hover, focus, highlights */
   --md-accent-fg-color: var(--dj-orange);

src/explanation/spark-adapters.md

@@ -86,4 +86,3 @@ For pipelines that are predominantly internal (no analytic publish), `<blob@>` e
 - Spec: [Codec API](../reference/specs/codec-api.md) — the base `Codec` interface.
 - How-to: [Use Plugin Codecs](../how-to/use-plugin-codecs.md) — installing and registering codec plugins.
 - How-to: [Create Custom Codecs](../how-to/create-custom-codec.md) — writing your own codec.
-- Strategic background: Databricks integration's Linked Delta Tables — covered in `datajoint-databricks/DECISIONS.md` (internal repo; summary on request).

src/reference/specs/spark-adapter.md

@@ -5,15 +5,15 @@ This document specifies the `SparkAdapter` Protocol — an opt-in contract that
 The Protocol is intentionally small: a single method, no inheritance, no abstract-method surface area on the existing `Codec` base class. Consumers detect support via `isinstance(codec, SparkAdapter)`.
 
 !!! version-added "New in DataJoint 2.3"
-    Introduced for the Databricks Linked Delta Tables ("silver layer") integration. Independent of any consumer: any codec author can implement `to_spark()` and any downstream tool can check for it.
+    Introduced for publishing DataJoint tables to a Databricks silver layer. Independent of any consumer: any codec author can implement `to_spark()` and any downstream tool can check for it.
 
 For the `Codec` base class itself, see [Codec API Specification](codec-api.md). For background on why the protocol is needed, see [Spark Adapters and Silver-Layer Publishing](../../explanation/spark-adapters.md).
 
 ## Why this exists
 
 DataJoint's `<blob@>` codec stores arbitrary Python values — numpy arrays, lists, dicts, custom objects — as serialized binary. That generality is the right call for most use cases, but it makes the column **opaque** to consumers that need typed access at query time: SQL engines, dataframe libraries, BI tools, federated query systems. Such consumers can only treat blob columns as `BINARY` and rely on the application to decode.
 
-The downstream concrete case is **Databricks Linked Delta Tables** (the "silver layer"): every column must render to Spark-native types so the data is queryable with Spark SQL, exposed through Delta Sharing, and usable by Genie / BI without round-tripping through DataJoint. A `BINARY` blob doesn't qualify.
+The downstream concrete case is a **Databricks silver layer**: every column must render to Spark-native types so the data is queryable with Spark SQL, exposed through Delta Sharing, and usable by Genie / BI without round-tripping through DataJoint. A `BINARY` blob doesn't qualify.
 
 `SparkAdapter` is the minimum framework-side contract to make typed rendering possible **without** forcing every codec to support it. Generic codecs (`<blob@>`, `<hash@>`) remain unsupported by design. Typed codecs (`<float_array@>`, `<image_2d@>`, future shapes) opt in by implementing the method.
 
@@ -155,7 +155,7 @@ class PointWithLabelCodec(dj.Codec):
 
 ## Consumer pattern
 
-A simplified silver-layer publish loop (the actual `datajoint-databricks` consumer is more elaborate):
+A simplified silver-layer publish loop (a production consumer is more elaborate):
 
 ```python
 def publish_row_to_silver(table, key, target_table):

src/reference/specs/trace.md

@@ -289,7 +289,7 @@ Teams adopt the read surface incrementally:
 ## What is not in this specification
 
 - **Enforcement of the `make()` contract.** `trace` and `self.upstream` make the contract easy to follow and to inspect, but the DataJoint Library does not check it at runtime. Confirming that a `make()` reads only from its declared ancestors and writes only to its target, across every access path, is a code-inspection problem — performed by the DataJoint Platform's code-deployment CI/CD (static analysis where sound, agentic review for the dynamic cases), backed by commit-pinned governed execution. See the [make() reproducibility contract](autopopulate.md#43-the-make-reproducibility-contract).
-- **Row-level lineage metadata in storage**. The features here provide the *graph operation* and the *per-`make()` read surface*; persisting per-row upstream projections (the dj-delta-style silver-layer features) is a downstream consumer concern, tracked separately.
+- **Row-level lineage metadata in storage**. The features here provide the *graph operation* and the *per-`make()` read surface*; persisting per-row upstream projections (as silver-layer publishing does) is a downstream consumer concern, tracked separately.
 
 ## References