Developer Tools
DataBlend
Connect your AI to DataBlend
Integration developers, data engineers, and IT teams build DataBlend datasources, collectors, queries, and workflows to move data between systems, but the state of those pipelines is buried across screens no business user can follow. AI can surface what is running, what failed, and what a workflow does, but only within the credential boundaries and workflow permissions each DataBlend account enforces.
Why it matters
Capabilities that matter most for DataBlend
01
Pipeline health in one list
DataBlend datasources, collectors, queries and workflows become lists, so anyone can see what ran, what failed and when.
02
Integrations beside their systems
Join DataBlend runs with the ERP or CRM lists they feed to see where a missing record went.
03
Governed AI for integrations
AI tools answer within each DataBlend account's credential boundaries and per-user security.
Use cases
Example AI Use Cases for DataBlend
scenario
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You want to know whether anything in DataBlend has been quietly failing, without opening every workflow and query to check its run history one at a time.
What popdock provides
--
Ask for a health check and you get one report covering every workflow and query run in the last week: how many succeeded, how many failed, which failures are one-offs and which have been repeating, and the three things most worth fixing first. Where a failure looks like an expired credential it tells you which credential to check. Nothing is changed, so it is safe to run any time, including on a schedule.
Business outcome
---
One weekly report shows exactly what has been quietly failing and the three things most worth fixing first.
scenario
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Credentials expire and get revoked without warning, and you find out when an overnight integration fails rather than when the credential stopped working.
What popdock provides
--
Every saved credential is tested against its target system, and you get a pass or fail table plus, for each failure, exactly which collectors, data targets and workflow steps it blocks. Failures are ordered by how much they take down, so you fix the one that stops four integrations before the one that stops a report nobody reads. The test only verifies authentication, so nothing is changed.
Business outcome
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Expiring credentials are caught before an overnight run fails, ranked by how many integrations each one would take down.
scenario
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You want to be told when the data going through an integration is wrong, rather than finding out from the person downstream who received it.
What popdock provides
--
Describe the condition that means something is wrong and a data quality report is created over the query, its parameters set, and then run once to prove it fires. A first run that flags nothing is not treated as proof: you are told whether the query data would have tripped the check, and if it would not, that the check is still unproven. Name a workflow and the report is attached as a step after the ones that produce the data it checks.
Business outcome
---
Bad data trips an alert on the way through, so you hear it before the person downstream does.
scenario
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A workflow has started failing and the run history tells you it broke without telling you why. You need the cause before you can fix it.
What popdock provides
--
Name the workflow and you get a diagnosis rather than a log dump. It finds the last successful run, the step that actually failed, and then checks the causes that are usually behind it: whether the credential still authenticates, whether the stream the step reads was ever closed, whether the collector feeding it returned any rows, and whether a step input points at something that has been deleted. You get the likely cause, the evidence for it, and the exact fix. It stops there and leaves the change to you.
Business outcome
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A failing workflow comes back with its actual cause, the evidence, and the exact fix, instead of a log to wade through.
scenario
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You have a file to get into DataBlend and the load has to end with the stream closed, because data sitting in an open stream is invisible to every query that reads it.
What popdock provides
--
Give it the file, the datasource and the schema. The datasource and schema are created if they do not exist, with columns derived from the file's header row when you have not specified them, the file is loaded, and the stream is closed so queries can see the data. If the schema already exists and its columns do not match the file, it stops and shows you the difference rather than forcing the load. Rejected rows and a zero row load are reported as problems, not quietly passed off as success.
Business outcome
---
A file lands in DataBlend visible to every query, with mismatched columns and rejected rows flagged instead of silently passed.
scenario
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A source system has added or renamed a field and the schema has to follow, but removing a column drops the values already stored under it and the queries that read those columns will not update themselves.
What popdock provides
--
The change is spelled out column by column before anything is applied, including an explicit warning about the stored values a removal will drop. Incoherent changes, such as adding a column that already exists or renaming one that does not, stop the run rather than half applying. Afterwards you get the list of queries, collectors, data targets and workflow steps that now reference a column that has moved, so the follow-up work is visible instead of surfacing as a failed run next week.
Business outcome
---
The schema follows the source cleanly, with dropped values and every affected query surfaced before they become next week's failed run.
scenario
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A workflow that matters is only understood by the person who built it, and nobody else could pick it up or support it.
What popdock provides
--
Name the workflow and you get a written runbook: what it moves and to where, how it is triggered, every step in order with what it reads and writes, the systems and credentials involved, and what breaks when each one fails. If a step points at a query or data target that no longer exists, that is reported rather than glossed over. Useful for handover, for support, and for a review of an integration nobody has looked at in a year.
Business outcome
---
Any workflow becomes a runbook someone else can support, instead of living only in the head of whoever built it.
scenario
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The data in a schema is wrong or stale and the fix is to clear it out and collect it again from source. That deletes rows, so it needs to be deliberate.
What popdock provides
--
The purge only happens once you have typed the schema name back as confirmation, so it cannot be triggered by a loosely worded request. Row counts are recorded and reported before anything is deleted, the collector is re-run, and the stream is closed so queries see the reloaded data. You get before and after row counts, and if the collection fails after the purge you are told plainly that the schema is now empty and what it takes to restore it.
Business outcome
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A stale schema is reloaded from source on a deliberate, confirmed action, with row counts proven before and after.
scenario
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The answer you need is already in a DataBlend query, but getting it means running the query, exporting the rows and working it out yourself.
What popdock provides
--
Name the query and ask your question in plain language. The query runs and the answer comes back with the figures behind it and the row count it was based on. It answers only from the returned rows: if the query came back empty, if the data is related but does not settle the question, or if the results were cut short by a row limit, it says so instead of filling the gap with something plausible.
Business outcome
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The answer comes straight from the query's own rows in seconds, with nothing invented to fill a gap.
scenario
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You want a workflow to run on its own, and a cron expression is easy to write, easy to misread, and expensive to get wrong when it fires four times an hour against a system that cannot take it.
What popdock provides
--
The current schedule is shown first, then your new one is read back to you in plain language with the time zone spelled out, so an accidental hourly run is obvious before it is saved. It also checks the interval against how long recent runs actually took and flags a schedule that would start a run before the last one finished. If the workflow is paused, you are told the schedule will not fire until it is resumed, and if the workflow has never run successfully you are told that a schedule will simply repeat the failure automatically.
Business outcome
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Workflows run themselves on a schedule you can trust, with an accidental hourly run caught before it fires.
scenario
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Standing up a new integration means creating a datasource, its schemas, a collector, a query, a data target and a workflow, then wiring the steps together in the right order. Getting the order wrong produces a workflow that runs and moves nothing.
What popdock provides
--
Describe the source, the target and what should move, and the whole set is built for you in the order DataBlend requires, with the credential test attached first so a bad login fails the run immediately rather than part way through. Anything that already exists is reused rather than duplicated. The workflow is created paused and nothing is run, so you review the finished pipeline before any data moves. Where a required detail cannot be worked out from your description it stops and asks instead of inventing a table or an endpoint.
Business outcome
---
A complete, correctly ordered pipeline is ready to review before a single row moves, with bad logins caught up front.
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