Data Lake wording improvements

Author: PatrickMiraP

docs/quix-cloud/managed-services/blob-storage.md

@@ -16,15 +16,15 @@ Connect your cluster to a bucket/container so Quix can enable **Quix Lake** or a
 ???+ info "Quix Lake at a glance"
     **Summary** - Quix Lake persists Kafka topic data as **Avro/Parquet** in your own bucket (S3, GCS, Azure Blob, MinIO), partitioned for fast discovery and full-fidelity **Replay**.
 
-    **Why it exists** - Preserve exact Kafka messages (timestamps, headers, partitions, offsets, gaps) with indexed metadata so **Catalog**, **Replay**, **Sinks**, and future services operate on open formats you control.
+    **Why it exists** - Preserve exact Kafka messages (timestamps, headers, partitions, offsets, gaps) with indexed metadata so **API**, **Replay**, **Sinks**, and future services operate on open formats you control.
 
     **Key properties**
     - **Portable** - open Avro & Parquet
     - **Efficient** - Hive-style partitions + Parquet metadata
     - **Flexible** - historical + live workflows
     - **Replay** - preserves order, partitions, timestamps, headers, gaps
 
-    **Flow** - **Ingest** (Avro) → **Index** (Parquet metadata) → **Discover** (Data Catalog & Metadata API) → **Replay** (full fidelity back to Kafka) → **Use** (explore, combine historical + live, run queries/export).
+    **Flow** - **Ingest** (Avro) → **Index** (Parquet metadata) → **Discover** (Data Lake API & Metadata API) → **Replay** (full fidelity back to Kafka) → **Use** (explore, combine historical + live, run queries/export).
 
     [Learn more about Quix Lake →](../quix-cloud/quixlake/overview.md)
 

docs/quix-cloud/managed-services/replay.md

@@ -123,15 +123,15 @@ Per-file reads, computed waits, gap trimming, queue length, and throttling-usefu
 
 ## How it works (high-level)
 
-1. Queries the **Quix Lake Catalog** to locate Avro segments for your topic, keys, partitions, and time window.
+1. Queries the **Quix Lake API** to locate Avro segments for your topic, keys, partitions, and time window.
 2. Streams records in order, applying **timestampsType** and **replaySpeed**.
 3. Optional controls adjust gaps, throughput caps, key remapping, and partition routing.
 4. Produces records to the **destinationTopic** in Kafka.
 
 
 ## Configuration
 
-Managed services use simplified config. Quix maps these keys to underlying environment variables and wiring (including the **Quix Lake Catalog API URL**, which is injected automatically).
+Managed services use simplified config. Quix maps these keys to underlying environment variables and wiring (including the **Quix Lake API URL**, which is injected automatically).
 
 ### Required
 

docs/quix-cloud/managed-services/sink.md

@@ -19,7 +19,7 @@ Identifier: `DataLake.Sink`
 
 * Consumes from a Kafka topic (single or many sinks per environment)
 * Rolls **Avro** segments and writes them under the **Raw** prefix using a stable, partitioned layout (topic, key, partition, date)
-* Emits **Parquet** index files under **Metadata** so the **Quix Lake Catalog** and APIs can list and filter datasets without scanning Avro
+* Emits **Parquet** index files under **Metadata** so the **Quix Lake UI and API** can list and filter datasets without scanning Avro
 * Optionally accepts **custom metadata** you attach later via the Metadata API
 
 **Example object names**
@@ -130,7 +130,7 @@ deployments:
 * **Parquet (Index)**
   Compact descriptors with `Path`, `Topic`, `Key`, `Partition`, `TimestampStart/End`, `OffsetStart/End`, `RecordCount`, `FileSizeBytes`, `CreatedAt`, `DeletedAt?`.
 * **Parquet (Custom metadata, optional)**
-  Your key–value annotations (`Topic`, `Key`, `MetadataKey`, `MetadataValue`, `UpdatedUtc`) used for search and grouping in the Catalog.
+  Your key–value annotations (`Topic`, `Key`, `MetadataKey`, `MetadataValue`, `UpdatedUtc`) used for search and grouping in the UI.
 
 See [Open format](../quixlake/open-format.md) for full schemas and layout.
 

docs/quix-cloud/quixlake/api.md

@@ -13,7 +13,7 @@ See the UI page: [Quix Lake User Interface](./user-interface.md).
 
 * Authenticate with a **Bearer** JWT in the `Authorization` header.
 * Most routes are namespaced by **workspace** and **topic**.
-* You can open the in-product Swagger from the Catalog header in the UI.
+* You can open the in-product Swagger from the catalog header in the Data Lake UI.
 
   ![Open API](./images/user-interface-open-swagger.png)
 
@@ -34,7 +34,7 @@ Together, these endpoints back the catalog’s search grid, topic/key lists, and
 ### Search stream metadata
 
 `POST /{workspaceId}/{topic}/search`
-Searches stream metadata and returns matches with a total count for paging and analytics. Results mirror the catalog grid. Supports free text, exact/prefix/suffix/fuzzy matching, time windows, sorting, paging, and optional inclusion of all tag fields.
+Searches stream metadata and returns matches with a total count for paging and analytics. Results mirror the catalog grid in the Data Lake UI. Supports free text, exact/prefix/suffix/fuzzy matching, time windows, sorting, paging, and optional inclusion of all tag fields.
 
 **Behavior notes**
 
@@ -85,9 +85,6 @@ Returns workspace identifiers that have discoverable data for the caller.
     The search response includes a total-count header so clients can page results consistently with the UI.
 
 
-Here’s a refined introduction for the **Data** section that aligns in tone and clarity with the improved **Catalog** section:
-
-
 
 ## Data endpoints
 

docs/quix-cloud/quixlake/open-format.md

@@ -53,7 +53,7 @@ Quix Lake stores Kafka messages and metadata as **open files** in your blob stor
 
 === "Index metadata (Parquet)"
 
-    Compact **Parquet** descriptors summarize where raw Avro files live so Catalog/UI/APIs can **discover datasets without scanning Avro**.
+    Compact **Parquet** descriptors summarize where raw Avro files live so UI and APIs can **discover datasets without scanning Avro**.
 
     **Columns**
 

docs/quix-cloud/quixlake/overview.md

@@ -53,7 +53,7 @@ See **[open format](./open-format.md)** for the full layout and schemas.
 * **Query externally** using DuckDB, Spark, Trino, Athena, or BigQuery over Avro and Parquet
 
 !!! tip "Cross-environment access"
-    With the right permissions, you can browse datasets written by other environments using the Environment switcher in the Catalog.
+    With the right permissions, you can browse datasets written by other environments using the Environment switcher in the Data Lake UI.
 
 ## How it works