Microsoft Azure DataLakeStoreFileSystemManagementClient
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For Agents
Append data concurrently, set file expiry, and check access on files in an Azure Data Lake Store Gen1 account using its WebHDFS-compatible endpoint.
Use for: Append a batch of records to a streaming file in Data Lake Store, Set an expiry time on a temporary file in Data Lake Store, Check whether the current identity can read a Data Lake Store path, I need to mark a Data Lake Store file for automatic deletion
Not supported: Does not provision Data Lake Store accounts, manage U-SQL jobs, or run analytics queries — use for the WebHDFS extension operations on a Data Lake Store Gen1 account only.
Jentic publishes the only available OpenAPI specification for DataLakeStoreFileSystemManagementClient, keeping it validated and agent-ready. The API exposes the WebHDFS-compatible filesystem layer of an Azure Data Lake Store Gen1 account, focused on the extensions that matter for agent workflows: concurrent append, file expiry, and access checks. It is reached at the per-account endpoint and complements the standard WebHDFS verbs published by the underlying service.
Install Jentic One Beta
Connect the DataLakeStoreFileSystemManagementClient to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the DataLakeStoreFileSystemManagementClient, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with DataLakeStoreFileSystemManagementClient API.
Append data to a Data Lake Store file using the concurrent append extension
Set or clear an expiry time on a file in Data Lake Store
Check whether the caller has a given access permission on a file path
Reach the WebHDFS-compatible endpoint of a Data Lake Store Gen1 account
Use Data Lake Store extensions that go beyond standard WebHDFS verbs
Use Cases
Patterns agents use DataLakeStoreFileSystemManagementClient API for, with concrete tasks.
★ Concurrent ingestion to a Data Lake file
Multiple writers (a fleet of edge nodes or a parallel ingestion pipeline) need to append events to the same Data Lake Store file without coordinating writes. The concurrent append endpoint accepts batches from each writer and orders them server-side, so an ingestion controller can drive parallel writes safely.
Append a 4 KB batch of newline-delimited JSON events to '/raw/events-2026-06-10.jsonl' in the target Data Lake Store account using the ConcurrentAppend extension
Time-bound scratch files
Pipelines often write intermediate scratch files that should be cleaned up automatically. The file expiry endpoint lets the pipeline stamp a deletion time on a file at write time so the lake garbage-collects the artefact without an explicit delete step.
Set the expiry on '/scratch/job-1234/output.tsv' to 24 hours from now using the SetFileExpiry endpoint
Pre-flight access check
Before submitting a long-running U-SQL job that reads a list of paths, the controller verifies the calling identity has the required permission on each path. The CheckAccess endpoint reports whether the caller has the requested access mode on a path so the job is rejected early instead of failing mid-run.
Call CheckAccess on '/protected/finance' with mode 'r--' and confirm the current identity has read permission
Agent-driven safe write
An AI agent loading data into a partner's Data Lake Store first checks read access on the target folder, then concurrently appends the payload into a daily file, then stamps an expiry to enforce retention. Through Jentic, the agent chains these three operations using the same scoped credential without leaving the SDK.
Verify read access on '/inbound/partner-x', concurrently append the supplied payload to today's partner-x file, and set a 30-day expiry
Key Endpoints
3 endpoints — jentic publishes the only available openapi specification for datalakestorefilesystemmanagementclient, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/WebHdfsExt/{path}
Concurrent append to a Data Lake Store file
PUT
/WebHdfsExt/{path}
Set the file expiry on a path
GET
/webhdfs/v1/{path}
Check access on a Data Lake Store path
POST
/WebHdfsExt/{path}
Concurrent append to a Data Lake Store file
PUT
/WebHdfsExt/{path}
Set the file expiry on a path
GET
/webhdfs/v1/{path}
Check access on a Data Lake Store path
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Azure AD bearer tokens for Data Lake Store are stored encrypted in the Jentic vault. Agents receive a scoped token at execute time and the underlying refresh credential never enters the agent's context.
Intent-based discovery
Agents search by intent (e.g., 'concurrent append data lake store') and Jentic returns the matching WebHDFS extension operation with the path parameter and required op flag.
Time to first call
Direct integration with the Data Lake Store filesystem endpoint, including per-account host resolution and AAD setup, typically takes 0.5-1 day. Through Jentic: under 30 minutes to first successful append.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using DataLakeStoreFileSystemManagementClient API through Jentic.
Why is there no official OpenAPI spec for DataLakeStoreFileSystemManagementClient?
Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call DataLakeStoreFileSystemManagementClient via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.
What authentication does the Data Lake Store Filesystem API use?
The Data Lake Store Gen1 filesystem endpoint is gated by Azure Active Directory bearer tokens issued for the Data Lake Store resource. Through Jentic, the AAD credential is held in the encrypted vault and a scoped token is supplied at execute time so the bearer token never appears in the agent's context.
Can I append data to a single file from multiple writers with this API?
Yes. POST /WebHdfsExt/{path} with op=CONCURRENTAPPEND accepts payloads from concurrent writers and orders them server-side. This is the recommended pattern for parallel ingestion into a single Data Lake Store file without coordinating offsets between writers.
What are the rate limits for the Data Lake Store Filesystem API?
Data Lake Store Gen1 throttles by ingress and egress throughput per account rather than by request count, and returns 503 with a Retry-After hint when an account exceeds its provisioned throughput. Size individual append payloads to the documented 4 MB chunk limit and back off on 503 responses.
How do I set an expiry on a Data Lake Store file through Jentic?
Search Jentic for 'set file expiry data lake store', load the schema for PUT /WebHdfsExt/{path} with op=SETEXPIRY, then execute with expiryOption=Absolute or RelativeToNow and the desired expireTime. The pip install jentic Python SDK uses the async search, load, execute pattern.
Is the Data Lake Store Filesystem API free?
There is no per-request charge for the filesystem extension calls themselves; you pay for storage and transactions in Data Lake Store Gen1 according to the standard Azure pricing for the service. Note that Gen1 has been retired in favour of ADLS Gen2 — new workloads should target Gen2 with its blob-compatible API.
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