We handle the implementation
We review the selected workflow, build it on Mage, and check its required output.
Modernize a Dataflow batch feed. Keep your rollout moving.
Our operational rollout needs a daily batch feed. Our small data team is busy investigating slow Dataflow stages. Can you check whether this job fits, rebuild the feed, and take on its ongoing operation?
Mage
We’ll first assess the batch job’s fit, volume and timing. For an agreed scope, we’ll build it on Mage, verify output, and take on ongoing operation.
- Assess the batch transforms, volume and timing first
- Build the suitable feed and verify complete output
- Operate the agreed batch workflow and recovery steps
- Explore operational data with questions, charts and AI
Start with one workflow and the rollout it needs to support.
Overview
We do the work. You get on with yours.
Get a suitable daily feed delivered while your data team stays focused on the wider operational rollout.
We take on the operation
We maintain the agreed workflow and bring your team decisions that need approval.
Your team uses the results
Inspect the work, explore its data, and make approved changes with access you control.
From batch stages to a checked operational feed.
Dataflow is a distributed batch and streaming engine. We assess one recurring batch feed for fit, including transforms, volume and timing. This is a scoped implementation, not a promise to replace every Beam job or streaming workload.
Dataflow batchFrom daily files
Transform
Operations table
Daily window
Check the job fitWe review batch transforms. We check volume and timing.
Build the daily batch on Mage
Selected workflowOperations feedDaily files → operations table
Transform · check batch totals
Build the batch feedWe rebuild the agreed feed. We keep its required output.
| Field | Dataflow | Mage |
|---|---|---|
| Keys | Match | Match |
| Totals | Match | Match |
| Period | Checked | Checked |
Values agree
Check a full batchWe compare the full input. We check output and timing.
Then runs on MageWe operate the daily batch
Approve the cutoverWe plan for repeat runs. Your team approves the move.
We keep it running after the move.
We operate the agreed workflow on Mage. Safe recovery steps are checked; unclear results and changes outside the agreed rules go to your team.
Daily batch feedAgreed response
Prepare batch
Write timeoutRecovery checked
Agreed retry · output checked
Recover a failed batchCheck the failed processing step. Retry safely and verify output.
Check the daily batch
records ready
records held
Batch checked
Check complete outputCheck daily periods. Hold invalid records.
Check the workload fitAgree the data volume. Check its delivery window.
Put your data to work.
Ask which daily batches are complete, chart held records, and use the checked operational data for approved AI work.
Ask a question and explore the data behind the answer.
Fits your systems. Keeps you in control.
Choose where your data work runs and who can see or change it.
Runs where you need it
Team spaces
Access you control
Draft
Review
Live
Your systems
Keep your systems
Use agreed sources. Choose where it runs.
Your team
Control team access
Set the team’s access. Review each change.
Our support
Get ongoing help
Get help with issues. Plan agreed changes.
Start with one recurring Dataflow batch job.
What does the job deliver, and what volume and timing must it handle? Share its transforms and destination. We’ll assess fit before scoping implementation and ongoing operation.