Official company update
Topics to Tables: From JSON on a Topic to Queryable Columns
ApplyDjinn context
What else is visible for VAST Data?
These are observations in our catalog around the company, not claims made by the article. A publication does not establish why hiring, search interest, or share prices moved.
Current roles
30
30-day current-market scope
Company stories · 30d
10
10 official · 0 independent
Attention records · 7d
0
0 Google · 0 Polymarket
Coverage mix
Attention sources
No matching record in this window.
Open roles
30
Technical roles
16 of 30
Observed hiring change
-3 roles
Hiring and market history
Daily vacancy snapshots show this company’s observed hiring; no standalone public ticker is assigned.
Vacancy history
Current role mix
16 technical · 14 other
53% of currently observed roles are technical.
Vacancy history uses saved daily snapshots through 2026-10-15. News counts are dated publications; attention counts are matched records, not search volume or prediction probabilities.
What the source says
Publisher previewIn Part 1of this blog we described the basic model: point a Blob Expansion at a Kafka topic, declare the columns you need, and VAST parses each message into a typed table as the message is ingested. The expansion runs in the same transaction as the produce operation. Here, we’ll walk through the setup and the results. We will produce order records as JSON to a Kafka topic and query them back as typed columns. There is no consumer group or separate processing pipeline. We will then send malformed messages to the same topic and look at the diagnostic columns, followed by a schema change without recreating the expansion. The expansion converts JSON on the topic into columns in the target table as the message is ingested. The examples below use output from a release-5.5.0-sp1 cluster. The setup is small and does not require a separate streaming engine or consumer group. On the client side, we use the kafka-python and VAST DataBase Python packages. Blob Expansion is a first-class database object, managed through the GUI, CLI, REST API or Python SDK like the topic and target tables. Most of the setup here uses code and CLI commands, with GUI screenshots for the expansion configuration.
This is a short preview supplied by the publisher. The charts above are separate ApplyDjinn observations. The complete story, including images and updates, remains with the publisher.