May Release Notes

Aunalytics is excited to announce the May 2021 Daybreak release to our clients. This release will provide clients with model and site enhancement information along with any fixes to existing functionality we have included.


Context Selector

Users of Daybreak have always been able to access more than one Daybreak datamart through the use of separate solution URLs communicated to clients. This month, a change to the login screen user experience now allow all users to access Daybreak through a single URL and select the Daybreak datamarts they would like to access from a context selector screen displayed after successfully authenticating with the platform.

Daybreak context selector screencap

Context selection enhances the Daybreak login experience and simplifies the way clients access Daybreak from a single, memorable URL. Moreover, context selection enables users who subscribe to more than one Daybreak instance to easily switch between instances without navigating to a separate site and authenticating again.

Natural Language Answers Model Updates

In April, Aunalytics released Daybreak™ Natural Language Answers, a natural language processing model that generates structured queries from questions in natural language. This model is developed by Aunalytics’ Innovation Lab and is updated to support changes to the underlying data model used to power our Daybreak datamarts. This month, Innovation Lab will release a new model version trained to better recognize answers that involve external relationship fields—Smart Features™ generated by reading through a customer’s transaction data to detect financial products they may have with external entities (other financial institutions, auto dealerships, etc.) After release, Natural Language Answers will be able to understand questions about customers with external relationships by answering questions like:

  • “Give me a list of customers who have an external mortgage and don’t have a HELOC.”
  • “Show customers who have external brokerage accounts”
  • “Customers born between 1950 – 1955"
  • “Customers who are millennials with active checking accounts”
  • “Number of accounts created in the last year”

These natural language phrases are supported by retraining the model with data to support the following fields:

  • Customer_HasExternalCarLease
  • Customer_HasExternalCarPayment
  • Customer_HasExternalCreditCard
  • Customer_HasExternalDealerPayment
  • Customer_HasExternalInvestment
  • Customer_HasExternalMerchantProcessing
  • Customer_HasExternalMortgage
  • Customer_DateofBirth
  • Customer_Generation

Additionally, the model now uses a dictionary of synonyms and acronyms as a training source to better understand technical terms and natural language expressions used in questions. For example, the model now understands “create” to be synonymous with “opened” in the context of financial institution accounts. Searches for "women with mortgages" now understands that "women" refers to a subset of customers (Gender == "Female"). Finally, a technical acronyms have been added so that these terms can be understood by the model. For example, "DDA" ("demand account") is understood to be a subset of accounts of a certain type (ProductCategory == "Demand"). This new technique will allow the model to easily be trained to accommodate more natural forms of expression and technical terms that could not easily be understood by the current machine learning techniques used to generate the model.

Daybreak™ for Financial Services Version 2.2 Patch

This month Daybreak for Financial Services users on version 2 of the data model will receive a patch update with several minor changes. Clients should be aware, however, that some user impact should be expected, particularly for Tableau users.

One change with no negative impact are that certain fields now support negative integer values:

  • Lend_AmountPastDue
  • Lend_BookBalance
  • Lend_FASBCost
  • Lend_NetDeferredFees

This change should not negatively impact users, but will enhance the analytical value of these fields.

One change with possible impact to users is that the data type of Lend_ParticipationStatus has been changed from integer to string.

Tableau users using this field in dashboards have likely used casting operations to display this field's values correctly. However, now that the underlying data type has changed, those casting operations will cause an error in the dashboard that can easily be resolved by simply removing the casting operation and treating the field as a string.

Finally, two spelling errors in the names of fields have been corrected:

  • Lend_CurrentAmoritizationPeriod corrected to Lend_CurrentAmortizationPeriod
  • Lend_LeinPosition corrected to Lend_LienPosition

While this correction should not confuse users, clients should be made aware that may impact field references in Tableau dashboards. Any dashboards referencing these fields will need to be updated to use the corrected field names.


Aunalytics is excited to announce also the May 2021 Aunsight platform release to our internal users. This release will provide users with enhancements, new features, as well as any fixes to existing functionality we have included.

Machine Learning Tool Improvements

This month's release includes some feature enhancements to Aunsight's machine learning tools, Data Lab and Model service.

Data Lab Git Integration

Data scientists use Data Lab to create machine learning model code within containerized Jupyter notebooks. In most cases, machine learning code is stored in repositories such as Bitbucket, Github, or Gitlab using the git version control system.

Git integration enables Data Lab users to automate the management and versioning of machine learning code by handling the authentication and transfer of code into and out of Data Lab containers and their persistent storage mounts. This month's new feature rolls out git credential validation to ensure that authentication tokens are valid for remote repository used to version container code so that users do not need to recreate a Data Lab if their credentials are incorrect.

Model Service Job Tracing

Machine learning model deployment is managed using the Model service, an object store for serialized machine learning models. When users retrieve a model from the service (for use in a pipeline to score datasets, for example), a platform copy job is performed in the Aunsight system services. This month, these copy jobs will now be traceable so that failures in model retrieval can be discovered and remedied by operations staff. This feature builds off of recent work to better manage jobs in the Aunsight platform in order to offer clients more resilient data pipelines.

NL2SQL as a Service

The Aunsight NL2SQL service developed last year was initially designed to provide a stateful API for specific language models developed for a particular client. As such, the NL2SQL service was deployed for clients using certain hard-coded contexts for the use of that service. As our client base for NL2SQL has grown, it has become evident that deploying machine learning language models in this manner presented a number of difficulties.

This month, the NL2SQL backend has been reconfigured to be deployed in a less stateful manner, meaning instances of the NL2SQL service will be initialized independent of any particular Aunsight organization/project context. State-based data such as the Aunsight context are now supplied with the request, meaning NL2SQL now runs as a platform service rather than a component within particular Aunsight projects. These changes make it easier for us to deploy, manage, and test NL2SQL models for client work.

Aunsight™ Golden Record

Aunalytics is excited to announce also the May 2021 Aunsight Golden Record data integration platform release to our clients. This release will provide users with enhancements, new features, as well as any fixes to existing functionality we have included.

Agent Notifications

Last month AuGR introduced a new notifications framework to enable better communication with system operators. This month, AuGR agent notifications have been released, enabling system operators to receive email notifications when there are failures with an AuGR agent deployment.

AuGR agent notifications will enable those who support AuGR agents to monitor the health of these deployments and take action should there be a failure.

Release Contents

Issue ID Description
ILT-136 Replace error message for erroneous input
DATAINT-547 Disable operator from setting feature flags.
DATAINT-546 API Request - Add option to trigger input job reads
DATAINT-535 Add paging component to Pending/Failed WB queues
DATAINT-523 Support CIFI 3.0 Change log Format
DATAINT-513 AuGR Microservice Consolidation: Merge Dataflow into Nucleus
DATAINT-498 Short-circuit read if bad record ratio is too high
DATAINT-487 Update UI to validate schemas in draft if connection settings is changed
DATAINT-447 Text Area version storage
AUN-14980 Martin's UI changes
AUN-14928 Getting rid of 'is not a recognized counter' logs in dataflows
AUN-14914 Datamarts: Load from DSV
AUN-14892 Add pagination for Exasol in Daybreak v2
AUN-14779 Add a system info page to webapps that show library versions
AUN-14734 Show Org/Workflow Names in Workflow Notification Emails
AUN-14733 Workflow: "validate_schema" option in copy
AUN-14715 add EAI_AGAIN to the list of connection retries
AUN-14649 Add whitelist of cases where workflow retries immediately fail
AUN-14647 "validate_schema" option for Copy
AUN-14435 Remove the gl2_message_id key from logs
AUN-14415 Create "load datamart" workflow component
AUN-14406 Update UI to accept batched queries
AUN-14402 Update query service to use lib-query v2
AUN-14307 Metric service failure handling

Bug Fixes

Issue ID Description
DATAINT-554 mdm notifications not working for tep
DATAINT-550 Dataflow Statistics API is returning 0 for the current month when it should have data.
DATAINT-548 Hide lookup shapes in Monitor UI
DATAINT-541 Test Exact Match on Hashed Property
DATAINT-538 Matching breaks on data type 'Decimal'
DATAINT-526 UI Session Expiration not being enforced
DATAINT-505 Matching failure not apparent in UI
DATAINT-503 Do not auto-discover on Input edit
DATAINT-495 Auto-provisioning should set the UI host suffix specific to the cluster.
DATAINT-494 UI logging doesn't seem to be logging errors to Graylog
DATAINT-491 Update charts with correct logging variables
DATAINT-152 Scheduled Read Jobs getting stuck / not running correctly
AUN-14953 Workflow Trace Info - Child job links in WF jobs can be overwhelming
AUN-14931 Notifications count fetch even when user is logged out
AUN-14918 Difference between row count shown in the data results page and an Insights summary card for number of records
AUN-14802 While editing details of a fixture, hitting enter refreshes the page and loses changes
AUN-14756 Unable to run stored queries through lib_aunsight_py
AUN-14714 Workflow Builder disabled components should disable required fields
AUN-14426 GraphQL WF References should point to specific job types not TrackerJob
AUN-14276 Formations Updating Roles is not working as expected.