[In preview] Public Preview: AI pipelines in Azure HorizonDB
Ce que dit la source
AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
Qui est concerné : The audience described by the Microsoft update is represented by: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Pourquoi c'est important : The change matters because the official source describes: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Urgence : Surveiller
Prochaine action : Review affected accounts with the customer using the official announcement.
Opportunités commerciales
Use the verified change to open a scoped customer conversation.
Actions techniques
Assess whether the documented change intersects the customer's current stack.
Questions à poser au client
Does this documented change affect a product or workload in scope?
Risques et objections
The official source does not establish facts beyond the quoted material.
Points à confirmer
The effective date is unknown and must be confirmed before scheduling action.
The affected customer scope is unknown and must be confirmed before action.
Lecture pour it_manager_dsi
Qui est concerné : The audience described by the Microsoft update is represented by: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Pourquoi c'est important : The change matters because the official source describes: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Urgence : Surveiller
Prochaine action : Assess technical dependencies and ownership against the official update.
Opportunités commerciales
Offer a scoped technical-readiness assessment if the customer confirms impact.
Actions techniques
Assess whether the documented change intersects the customer's current stack.
Questions à poser au client
Does this documented change affect a product or workload in scope?
Risques et objections
The official source does not establish facts beyond the quoted material.
Points à confirmer
The effective date is unknown and must be confirmed before scheduling action.
The affected customer scope is unknown and must be confirmed before action.
Lecture pour partner_channel
Qui est concerné : The audience described by the Microsoft update is represented by: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Pourquoi c'est important : The change matters because the official source describes: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Urgence : Surveiller
Prochaine action : Prepare a source-backed customer conversation and confirm the affected estate.
Opportunités commerciales
Qualify a partner-led assessment only after customer scope is confirmed.
Actions techniques
Assess whether the documented change intersects the customer's current stack.
Questions à poser au client
Does this documented change affect a product or workload in scope?
Risques et objections
The official source does not establish facts beyond the quoted material.
Points à confirmer
The effective date is unknown and must be confirmed before scheduling action.
The affected customer scope is unknown and must be confirmed before action.
Lecture pour sales_manager
Qui est concerné : The audience described by the Microsoft update is represented by: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Pourquoi c'est important : The change matters because the official source describes: [In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed ste
Urgence : Surveiller
Prochaine action : Review portfolio exposure with account owners using the official source.
Opportunités commerciales
Prioritize accounts that confirm affected products or workloads.
Actions techniques
Assess whether the documented change intersects the customer's current stack.
Questions à poser au client
Does this documented change affect a product or workload in scope?
Risques et objections
The official source does not establish facts beyond the quoted material.
Points à confirmer
The effective date is unknown and must be confirmed before scheduling action.
The affected customer scope is unknown and must be confirmed before action.
Preuves et traçabilité
Chaque extrait est relié à la source primaire. Version brute courante : 495.
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .
[In preview] Public Preview: AI pipelines in Azure HorizonDB AI pipelines for Azure HorizonDB is now available in public preview. AI pipelines let you describe an AI data ingestion workflow, chunking, embedding, extraction, generation, and ranking, declaratively in SQL, and run it as a fault-tolerant pipeline inside the database. Execution is durable: pipelines survive crashes, retry failed steps automatically, checkpoint incremental work, and resume long-running jobs from the last completed step. This removes the boilerplate ingestion service that many GenAI applications rebuild today. Pipelines run on change, so embeddings stay in sync as source rows are inserted or updated, and only new or changed rows need to be re-embedded. When the embedding model, dimensions, or chunk size change, backfill reprocesses every row as a single durable instance that resumes from the last checkpoint instead of restarting from zero. AI pipelines are part of the azure_ai extension, built on pg_durable , and work with AI model management, pgvector, DiskANN vector search, and full-text search to provide an end-to-end retrieval path inside one database. Learn more .