Data and analytics family — first release

Added

Document	What it carries
`reference/data/bigquery.md`	separated compute and storage, slots, two compute models, four storage models, four load paths
`reference/data/dataflow.md`	Apache Beam, one model for batch and streaming, the named features, exactly-once by default
`reference/data/dataproc.md`	the rename, the frameworks, three deployment shapes, the 90-second figure
`reference/data/errors.md`	the BigQuery reason strings, the retryable set, and the three rows that are the cost model talking
`guides/data/query-bigquery-from-typescript.md`	a keyless client, parameterised queries, and exactly the documented retries
`guides/parity/data.md`	why a node count does not convert, and where a lift genuinely exists

What this release discovered

The page served at the Dataproc overview URL is titled "Managed Service for Apache Spark." Both names are live — URLs, IaC resource types and CLI surfaces still say dataproc — and a document is recorded saying so, because an agent that assumes one name is stale will mistrust correct documentation.

The two clean lifts

Apache Beam pipelines onto Dataflow — Dataflow is a Beam runner, so a Beam pipeline changes runner.
Spark, Hadoop, Hive and Pig onto Dataproc — the same frameworks. With HBase going to Bigtable and HDFS

to Cloud Storage, a Hadoop estate decomposes rather than being rewritten.

Both are recorded with their converse, which matters as much: a Flink or Stream Analytics pipeline does not lift onto Dataflow.

Absences recorded rather than filled in

No vendor error table was read for Dataflow or Dataproc, so none is invented. The error catalogue says so

and gives the two facts from their own pages that carry the most diagnostic weight.

The Dataproc overview does not state whether clusters are zonal or regional, and names no image versions.
The BigQuery introduction names the storage billing models without stating thresholds; no figure is

quoted.

Known missing

BigQuery partitioning, clustering, materialized views and reservations/assignments.
BigQuery ML, BigQuery Omni, and external tables.
Dataflow pipeline options, Dataflow Prime detail, and the Google-provided template catalogue.
Dataproc Serverless configuration and image-version policy.
Cloud Data Fusion, Dataplex Universal Catalog, Looker.