NOTE: If your job failed, you may be prompted with an error message indicating a job ID that differs from the listed one. This job ID refers to the sub-job that is part of the job listed in the Job summary.
You can review a snapshot of the results of your job.
- View details: Click View details to view the generated results in the side bar. See the Output Destinations below.
This panel provides information on the progress and completion status of each stage of the job execution.
Tip: Depending on the operation, you may be able to monitor transfer rate performance for larger datasets.
- Connect: The platform is attempting to connect to the datastore hosting the asset sources for the datasets.
- Schema validation: When enabled, the schemas of a job's datasources are checked as the first step of job execution.
- Datasets with changes in them are reported at the top of the list. Click View all to see schema validation for all of the datasets used in the job in the Data sources tab.
- Optionally, the job can be halted if there are differences between the schema that is read and the schema that has been stored from the previous job run. This option can prevent data corruption. For more information, see Run Job Page.
- If no errors are detected, then the job is completed as normal.
- For more information on schema validation, see Overview of Schema Management.
- Request:The platform is requesting the set of assets to deliver.
- Ingest: Depending on the type of source data, some jobs ingest data to the base storage layer in a converted format before processing begins. This ingested data is purged after job completion.
- Prepare: (Publishing only) Depending on the destination, the Prepare phase includes the creation of temporary tables, generation of manifest files, and the fetching of extra connections for parallel data transfer.
- Transfer: Assets are transferred to the target, which can be the platform or to the output datastore.
Transform: This stage covers the execution of your recipe steps in order to transform the source data.
Profile: If you chose to profile your output data, this stage is completed after transformation is complete. Results are available in the Profile tab.
NOTE: If you chose to generate a profile of your job results, the transformation and profiling tasks may be combined into a single task, depending on your environment. If they are combined and profiling fails, any publishing tasks defined in the job are not launched. You may be able to ad-hoc
publish the generated results. See below.
Publish: This stage covers the writing of the outputs of the transformed data. These outputs are available through the Output destinations tab.
- Process: Cleanup after data transfer, including the dropping of temporary tables or copying data within the instance.
For more information, see \Overview of Job Monitoring.
If present, you can click the Show Warnings link to see any warnings pertaining to recipe errors, including the relevant step number.
- To view the job that was executed, click View dataflow job.
To review the recipe and dependencies in your job, click View steps and dependencies. See the Dependencies tab below.
- If you chose to profile results of your job, click View profile to review. See Profile tab below.
Job ID: Unique identifier for the job
Tip: If you are using the REST APIs, this value can be used to retrieve and modify specifics related to this job. For more information, see API Reference.
- Job status: Current status of the job:
Queued:Job has been queued for execution.
Running:Job is in progress.
Completed: Job has successfully executed.
NOTE: Invalid steps in a recipe are skipped, and it's still possible for the job to be executed successfully.
Failed:Job failed to complete.
NOTE: You can re-run a failed job from the Transformer page. If you have since modified the recipe, those changes are applied during the second run. See Transformer Page.
- Job type: The method by which the job was executed:
Manual- Job was executed through the application interface.
Scheduled- Job was executed according to a predefined schedule. See Add Schedule Dialog.
User: The user who launched the job
Environment: Where applicable, the running environment where the job was executed is displayed.
Start time: Timestamp for when processing began on the job. This value may not correspond to when the job was queued for execution.
Finish time: Timestamp for when processing ended on the job, successful or not
Last update: Timestamp for when the job was last updated
Duration: Elapsed time of job execution
vCPU usage: Total vCPU hours used to run the job. For more information, see Usage Metrics.
For jobs sourced from relational datasets, you can optionally enable SQL-based optimizations, which apply some of the steps specified in your recipe back in the datasource, where they can be executed before the data is transferred to the running environment for execution. Using these optimizations means faster performance based on a lower volume of data transfer.
Project owners must enable the optimization feature for the project. For more information, see Dataprep Project Settings Page.
When the feature is enabled, optimizations must be enabled for each flow. You can also select the optimizations to apply. For more information, see Flow Optimization Settings Dialog.
When optimizations have been applied to your flow, they are listed on the Overview tab:
Optimization: This setting is displayed if flow optimizations have been enabled for this flow.
Columns pruned: If one or more unused columns have been pruned in the datasource via SQL, the count of columns is listed here.
Filters pushed down: If one or more row filters has been applied in the datasource via SQL, the count of filters is listed here.
If an optimization is disabled or was not applied to the job run, it is not listed.
For each output, you can do the following:
View details: View details about the generated output in the side bar.
: View the results within
D s storage
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Download result: Download the generated output to your local desktop.
NOTE: Some file formats may not be downloadable to your desktop. See below.
Create imported dataset: Use the generated output to create a new imported dataset for use in your flows. See below.
NOTE: This option is not available for all file formats.
Direct file download
Click one of the provided links to download the file through your browser to your local desktop.
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Your version of the product supports publishing.
Your connection to the storage system includes write permissions.
Your results are generated in a format that the target system supports for writing.
All sub-jobs, including profiling, successfully completed.
For more information, see Publishing Dialog.
SQL scripts tab
Connection: Name of the connection through which the script was executed.
SQL statement: The first part of the SQL script that was executed.
Run before data ingest- script was executed pre-job.
Run after data publish- script was executed post-job, after the job results had been written.
Status: Current status and execution duration of the SQL script.
NOTE: If you have multiple SQL scripts for each settings, they may execute in parallel. For example, if you created three pre-job SQL scripts, there is no guarantee that they executed in the order in which they are listed.
Hover over a SQL script entry and click View details.
In the SQL script details window, you can review:
Connection and SQL of the executed script.
Any error messages that occurred during execution.
Tip: To review log information for any error messages, click Download logs.
For more information on these types of SQL scripts, see Create Output SQL Scripts.
Review the visual profile of your generated results in the Profile tab. Visual profiling can assist in identifying issues in your dataset that require further attention, including outlier values.
NOTE: This tab appears only if you selected to profile results in your job definition. See Run Job Page.
Download as PDF: Download your visual profile and results of your data quality rules on the entire dataset as a PDF file. For more information, see Overview of Data Quality.
Download as JSON: Download your visual profile as a JSON file.
In particular, you should pay attention to the mismatched values and missing values counts, which identify the approximate percentage of affected values across the entire dataset. For more information, see Overview of Visual Profiling.
Tip: You should review the type information for each column, which is indicated by the icon to the left of the column.
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If you have defined data quality rules for your recipe, those rules are applied to the generated results. In the Rules tab, you can review the application of the rules across your entire dataset.
Results: Hover over the data quality bar to see the number of rows and percentage of rows that passed (green) or failed (red) the rule.
Rule Description: The test applied to the values.
Type: The type of rule. For more information, see Data Quality Rules Reference.
Rule Updated At: Timestamp for when the rule was last modified.
Tip: When you download your profile as JSON, the rule definitions are included.
Tip: To open the full flow, you can click its name in the upper-left corner.
Dependency graph tab
Tip: You can use the keyboard shortcuts listed in the zoom options menu to make quick adjustments to the zoom level.
Zoom in: Zoom in 10% on the canvas to focus on greater detail.
Zoom out: Zoom out 10% from the canvas to see more of it.
Zoom to fit: Change the zoom level to fit all of the objects of your flow onto the screen.
25%, 50%, or 100%: Change the zoom level to one of the preset levels.
Download recipe: Download the text of the recipe in
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/natural language: Toggle display of the recipe in raw language or in readable language.
D s lang
You can select only recipes in the flow graph.
Context controls and menus are not available.
Data sources Tab
In the Data sources tab, you can review all of the sources of data for the executing recipe.
Data sources tab
NOTE: If a flow is unshared with you, you cannot see or access the datasources for any jobs that you have already run on the flow, including any PDF profiles that you generated. You can still access the job results. This is a known issueissue.
If schema validation has been enabled, you can review validation errors for individual datasets. For more information, see Schema Changes Dialog.
Datasets with parameters:
If your source is a dataset with parameters, you can review and count the individual files that were matched and imported.
For the imported dataset, click View details. Then, click the Files tab in the context panel.
This tab can be a good check to ensure that you have specified your dataset parameters correctly.
If your flow references parameters, you can review the state of the parameters at the time of job execution.
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When a webhook task has been triggered for this job, you can review the status of its delivery to the target system.
Webhooks are defined on a per-flow basis. For more information, see Create Flow Webhook Task.
NOTE: Webhook notifications may need to be enabled in your environment. See Dataprep Project Settings Page.
Name: Display name for the webhook task.
URL: Target URL where the webhook notification is delivered.
Status: HTTP status code returned from the delivery of the message.
200- message was delivered successfully.
Delivered: Timestamp for when the webhook was delivered.
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