What's New in Release 3.2.0

2023-04-17

New features summary

Feature	Summary
Skipped Row Option	With this option the user can choose to use existing data in the set validation for rows that are rejected on basic constraint checks.

Application Changes

Skipped Row Option

In the situation that rows of a delivery are rejected for basic constraints, by default the i-refactory tries to fetch the known data of these rejected rows from previous deliveries. This data is then used to perform set validations on rows in the current delivery that did not get rejected.

With the option includeExistingDataForSkippedRowsInSetValidation this default behavior can be changed for delivies on this interface in the Logical Validation Layer (HSTGIN). If this flag is set to 'NO' the i-refactory will not fetch the data of skipped rows for set validation.

This option can be manipulated on Interface, Delivery Agreement and Delivery. On Interface level this option can be set to 'YES' or 'NO', by default the option is set to 'YES'. This setting will be inherited from Interface to Delivery Agreement and from Delivery Agreement to Delivery. However, on both levels the setting can be overridden.

Delivery Agreements and Delivery's have a computed option, includeExistingDataForSkippedRowsInSetValidationConclusion, that by default will be the same as includeExistingDataForSkippedRowsInSetValidation's value of the corresponding Interface. This option can be overridden per Delivery Agreement by setting includeExistingDataForSkippedRowsInSetValidation on a Delivery Agreement to 'YES' or 'NO' (see table below). For Delivery's it works the same, includeExistingDataForSkippedRowsInSetValidation on the Delivery overrides the Delivery Agreement's setting for that particular Delivery.

The table below shows how includeExistingDataForSkippedRowsInSetValidationConclusion is computed for a Delivery Agreement.

Delivery Agreement value	Interface value	Delivery Agreement Conclusion
DEFAULT	YES	YES
DEFAULT	NO	NO
YES	NO	YES
NO	YES	NO

The table below shows how includeExistingDataForSkippedRowsInSetValidationConclusion is computed for a Delivery.

Delivery value	Delivery Agreement value	Delivery Conclusion
DEFAULT	YES	YES
DEFAULT	NO	NO
YES	NO	YES
NO	YES	NO

CRUD merge statement support

The transactional write interface on a Generic Data Access Model assumed only simple set based insert, update and delete (throug updates) statements were executed on the writable views. Some unit tests however failed because a SQLServer merge statement in some cases was executed on this API with mixed updates and deletes. Due to the complexity of the generated trigger code we prevented mixed updates and deletes.

In this release we fixed this limitation. You now have the ability to issue updates and (logical/physical) deletes in a single statement.

Rest API

POST Changed

/acmDatadel/deliveryAgreement and /acmDatadel/Delivery

New properties:
- includeExistingDataForSkippedRowsInSetValidation: Determines if existing rows (from previous deliveries) should be merged into the dataset for set validation in case a row delivered in the current delivery is skipped due to basic constraint violation. Only relevant for logical implementation models. The value by default will be set to YES if no specific value is provided.

PATCH Changed

/acmDatadef/interface, /acmDatadel/deliveryAgreement and /acmDatadel/Delivery

New properties:
- includeExistingDataForSkippedRowsInSetValidation: Determines if existing rows (from previous deliveries) should be merged into the dataset for set validation in case a row delivered in the current delivery is skipped due to basic constraint violation. Only relevant for logical implementation models. The value by default will be set to YES if no specific value is provided.

GET Changed

/acmDatadef/interface and /acmDatadef/deliverableInterface

New properties:
- includeExistingDataForSkippedRowsInSetValidation with default value YES. Can be set to YES or NO.
/acmDatadel/deliveryAgreement

New properties:
- includeExistingDataForSkippedRowsInSetValidation with default value DEFAULT.
- includeExistingDataForSkippedRowsInSetValidationConclusion: the final calculated conclusion based on the setting on interface and agreement.
/acmDatadel/Delivery

New properties:
- includeExistingDataForSkippedRowsInSetValidation with default value DEFAULT.
- includeExistingDataForSkippedRowsInSetValidationConclusion: the final calculated conclusion based on the setting on agreement and delivery.

NodeJS

The i-refactory runtime engine runs on NodeJS LTS version 10, 12 and 14.

Upgrade

Upgrade Notes

Bug Fixes

None.

Constraint violation actions are applicable to certain constraint categories. Not all combinations of constraint categories and violation actions are allowed.

An attribute must have a value, whatever that value may be. It must not be NULL.

A data type of an attribute defines what value an attribute can hold. The data type specifies what type of mathematical, relational, or logical operations can be applied to it without causing an error.

An attribute datatype constraint is the most basic constraint type. It checks for the datatypes we support and have implemented.

For example, we check for string, length of string, integer, date, etc. In the following figure you can see the supported data types by PowerDesigner.

Image is omitted: Supported data types

Constraints can be violated and there are some actions that can be performed when a violation occurs. The possible actions are: EMPTY COLUMN, NO ACTION and SKIP ROW.

An attribute value constraint is an expression that is evaluated. The person who writes the expression is responsible for the correctness of it. The expression should be formulated in a positive way and lead to a Boolean answer. If the expression validates to True, than the value is correct.

Examples

The values in attribute X has to be bigger than 10: X > 10
The email address has to be in a certain pattern: email address LIKE '%_@_%.__%'

A Concept Integration Model is also a central facts model on which you place integration patterns. It is not required to create a concept integration model, but it can be very useful.

Every constraint is assigned to a constraint classification.

The main purposes of the Generic Data Access Layer (GDAL) are to provide logical perspectives for data consumption and to manage CRUD actions.

A generic data access model is a virtual data model that acts as an interface bridge between consumer applications and the central fact storage.

Every attribute is assigned to an attribute classification.

An entity record constraint checks whether an attribute meets the requirements set by another attribute belonging to the same entity.

The main purpose of the Logical Validation Layer (LVL) is to transform the data received from external data sources to fit into the logical data model structure. It is also responsible for validating deliveries. The Logical Validation Layer is also known as the Historical Staging In (HSTGIN) Layer.

The logical validation model is the representation of a single external data source in a logical format. It represent how data delivered by a specific tenant should be transformed, temporalized and validated in the {popup}logical validation layer. The logical validation model is also known as Historical Staging model (HSTGIN).

Multi-active attributes are attributes that contain a business key to provide multiple context records at the same time. For example: a customer has multiple types of phone numbers. “Home”, “Work” and “Mobile”. In that case we add a dependent entity on customer with key “Phone Nbr Type”. This is to prepare for the CFPL multi-active key on customer.

The main purpose of the Technical Staging Layer (TSL) is to create a common starting point for further data processing. It receives data delivered from external data sources and temporally stores them in a database. The input data should be in a tabular format (rows and columns).

Bi-temporal attribute is an attribute that changes over time: they follow a valid timeline. For example, a Part may have a price valid for December and a price valid for January.

Every entity is assigned to an entity classification and to a parent entity classification. The possible values for entity classification are: ALTERNATE KEY CONTEXT, ATTRIBUTE CONTEXT, GENERALIZATION,HELPER, REFERENCE CONTEXT, STABLE, STABLE DEPENDENT and STABLE INDEPENDENT

Entity Set Constraint An entity set constraint can be used to perform a check concerning values of two or more attributes that belong to different entities or to perform a check concerning the value of an attribute with respect to a set of values.

A Set Constraint Helper is a helper in the logical validation model. It is the implementation of a set constraint. The helper returns the records of an entity for a given set constraint, where the instances of this entity do not meet the definition of this set constraint.

The business requirements describe how data should be delivered for the data consumers (end users or applications) in terms of concepts, relationships between concepts and constraints to validate the data. These requirements can be described in a logical data model, for example.

A Business Rule Helper is a helper in the central facts model. It is a set-based calculation of derived facts. You need to use a Business Rule Helper if you want to make a calculation and want to keep a transaction history of the results of this calculation. You use the existing entities from the central facts model as input. The results of the helper must be materialized in 'regular' fact entities, such as Anchors and Contexts, to make them accessible in the Generic Data Access Layer.

Closed Open means that the timeline is valid from (vanaf in Dutch) the supplied valid start date until - but not including - (tot in Dutch) the supplied valid end date. In practice, this means that the start date of a valid time record is equal to the end date of the previous valid time record.

You need to create context-based entities when a set of data may be delivered within the boundaries of a parent context. A context-based entity applies when:

At least 2 entities are delivered.
A context relationship exists between these 2 entities. One entity is the parent context of the other entity.
The parent context entity is delivered as a delta and the child entity is delivered as a full set.

You need to create context-based entities when a set of data may be delivered within the boundaries of a parent context. A context-based entity applies when:

At least 2 entities are delivered.
A context relationship exists between these 2 entities. One entity is the parent context of the other entity.
The parent context entity is delivered as a delta and the child entity is delivered as a full set.

The Management Model contains the PowerDesigner objects for the Unified Anchor Modelling (UAM). When a UAM object is created, a so-called PowerDesigner replica of the corresponding Management Model object is created. This means that certain properties such as metadata columns and column stereotypes are configured in the Management Model and cannot be changed. The replication settings specify which elements of an object can be changed after creating a replica from the template object. It is possible to override the replication settings of an UAM object and change a specific property.

The temporal atomic type describes the datatype of the temporal attributes|

The main purposes of the Central Facts Layer (CFL) is to store data historically. It can also integrate data from different sources. The Central Facts Layer is also known as Central Facts Persistency Layer (CFPL)

The central facts persistence implementation model is the representation of facts in an anchorized data model with the ability to integrate multiple logical models.

In the context of i-refactory, data transformation refers to operations involved in turning raw data readily useful and closer to the business requirements.

Integration patterns are used to integrate entities from different data models. If two or more entities from different data models share the same business key, you can use the Integration Pattern named Key Root. It is a good practice to capture integration patterns in a separate model, named Concept Integration Model.

An attribute is mandatory when its value can not be empty (NULL).

A Physical Data Model (PDM) represents how data will be implemented in a specific database.

{note} The i-refactory uses four PDMs: technical staging model, logical validation model, central facts model and generic access model. Each one of these models is implemented as an additional database, which is used to store data from external and internal data sources.

Reverse engineering is the process of reconstructing a physical and/or Entity Relationship (ER) model from an existing data source. The purpose of reverse engineering is to avoid manual work as much as possible.

Architecture layer

The core of the i-refactory architecture has four layers: TSTGIN, LVL, CFL and GDAL. There are also two auxiliary layers: UCLVL and EXT.

If an entity has one or more attributes that changes over time and you want to keep track of when a attribute is valid at a certain transaction time, then you have a special case of a regular dependent entity, called bi-temporal entity. The bi-temporal entity stores historical data with two timelines. The primary key of the bi-temporal entity is composed by the primary key of the parent entity and the valid start date attribute. The attribute that changes over the valid time is called a bi-temporal attribute.

A delivery agreement is a contract between a Tenant and a Logical Implementation Model or Generic Data Access model. An agreement has a duration. The delivery agreement set the architecture layer (interface) where the data should be ingested as well as the default settings to be applied to the deliveries.

A dependency mapping is a mapping between a helper (or BR helper) and a source entity used in the query of the helper. The helper and the source entity must belong to the same model.

Default dependency is set on entity level (source entity to helper entity)
To allow lineage on attribute level, via the Mapping editor, you could manually add the dependency on attribute level.

An Independent Entity is an entity that implements an Anchor for a Business Key that ‘stands alone’ e.g. that does not contain a reference to another Entity.

A Logical Data Model (LDM) matches the language, structure and quality of the business, regardless of the physical data implementation. The Logical Data Model reflects the business requirements.

A delivery may be considered as "untrusted" if deletes of data in the Logical Validation Layer have taken place and the processing of new deliveries cannot 100% rely (trust) on having enough statistics and data available to detect logical deletes, to determine the exact delta and to execute set based validations.

A Dependent Entity is an entity that implements an Anchor for a Business Key that ‘depends’ in its existence on another Entity. A Dependent Entity contains Business Key fields of which at least one is a foreign key (FK).

The transaction time in i-refactory is different from what is commonly understood by transaction time. Transaction time is usually seen as the moment when a fact was stored in the database. In the i-refactory, the transaction time is the time, as dictated by the source system, not by the i-refactory database.

The Attribute type links the attribute to one of the existing interfaces.

Computed columns are columns whose content is computed from values in other columns in the table.

Functional date A functional date or time is a point in time and is defined by a user. An example is an order date or date of birth.

The technical model (also known as Technical Staging In model: TSTGIN) is a representation of how exactly one delivery from a specific data source will be processed in the technical staging layer.

Generalization is the process of extracting shared characteristics from two or more classes (hyponyms), and combining them into a generalized superclass (hypernym). For example: an 'employee' and a 'customer' are both 'persons'.

The Mapping Editor provides a graphical interface for creating and viewing mappings between models. It provides a global view of all the mappings related to the entities of a given model, allowing you to quickly identify those which are mapped and not mapped.

When a certain fact can change over time and you need to capture when that fact is valid in the real world, you can add a valid start date and a valid end date to the entity.

A valid time tells us in which period a record is valid. While a functional date represents just one point in time, the valid time has a begin and an end date, for example:

For Order item 123, a Retail price of 10.00 was valid from 2019-01-01 to 2019-06-01.
For Order item 123, a Retail price of 12.00 was valid from 2019-06-01 to 2020-01-01.

Alternate key is an attribute or a group of attributes whose values uniquely identify every record in an entity, but which is not the primary key

Candidate key

A candidate key consists of one or more attributes and meets the following requirements:

Unique: The value of the key defines uniquely one instance of a concepts. There are no double values.
Non-volatile: (Almost) doesn't change.
Minimal: Contains only the elements needed.

There are two kinds of candidate keys:

primary key
alternative key

A Key Root Link is an integration concept that must be used when the exact same business concept or dependent business key occurs in different models. The Links for this dependent business key in the different UAM models are all subtypes of the Keyroot Link.

Normalization is the process of decomposing tables in a database in order to reduce data redundancy and improve data integrity.

A strongly typed model is a model in which each all attributes have a predefined data type, for example: integers, doubles, date.

Surrogate Key A surrogate key is a system generated unique identifier that does not have any contextual or business meaning.

Business Key

A business key is an unique identifier that has business meaning and exists in the real world outside of the database. It consists of a column or a set of columns that already exists in a table. A business key is also known as a natural key

A Key Root Hub is an integration concept that must be used when the exact same business concept or independent business key occurs in different models. The Hubs for this independent business key in the different UAM models are all subtypes of the Keyroot Hub.

A relationship shows how two entities are related to one another. For example, a customer can place an order, and a order can have a customer.

Every Attribute has an atomic type (data type) which is linked to the attribute type of that attribute.

The cardinality shows how many instances of an entity can take place in a relationship.

An enumeration consists of the list of values that a given attribute should adhere to.

{example} An order can have different statuses, such as shipped,packing,created anddone. Other statuses are not allowed.

Foreign Key

A foreign key is an attribute or a set of attributes that refers to the primary key of another entity. The original entity containing the primary key is called the 'parent' entity and the entity containing the foreign key is called the 'child' entity.

A natural key is an unique identifier that has business meaning and exists in the real world outside of the database. It consists of an column or a set of columns that already exists in a table. A natural key is also known as a business key

The primary key is an assigned key that consists of a minimal set of attributes to uniquely specify an instance of a record. The attribute or a combination of attributes should meet the following characteristics:

Unique: The attribute values of the key uniquely identify one instance of a concept. There are no duplicate instances.
Non-volatile: The key does not change.
Mandatory: All values are filled; there are no NULL values.

It is good practice to choose a primary key that also meet the following characteristic:

Safe: Doesn't contain private or sensitive information, such as a social security number.

Constraints are related to the other elements depending of the type of the constraint. Certain constraints are associated to attributes, entities, helper entities, unique keys or relationships between entities.

An attribute may be assigned to one or more entities (ex: acm_exists_ind) and an entity may have several attributes

Each layer may have one or more interfaces. The amount of interfaces depend on how many tenants and delivery agreements have been configured.

Namespace is what in the terminology of SQL Server is called database schema.|

A Delivery is a container that holds the specification of what is actually pushed to the i-refactory platform. This specification consists of a list of entities.

Key Root A Key Root is a central repository for Business Keys. A Key Root ensures that similar records out of different data sources are identified by both the same Business Key as the Surrogated Key.

Context

A Context is a temporal table with a transaction start and end date. The Context tracks all changes of the context attributes related to a business key in the transaction time. This means that every change of an attribute value in a source system leads to a new record in the Context. The old record is end dated with the load date and the new record is start dated with the load date.

Hyponym is a term that denotes a subcategory of a more general class. For example: 'cat' and 'dog' are a hyponyms of 'animal'.

A mapping establishes relationships between concepts of separate data models. It creates a link between entities and attributes from a source model to related entities and attributes in the target model. A source model should precede the target model in the i-refactory architecture.

oasi_bk is an abbreviation for One Attribute Set Interface (OASI) with business keys. A normal view in the generic data access layer (GDAL) consists of the surrogate key, foreign key and attributes. The oasi_bk-view in the GDAL is a view where the business key(s) are also shown.

A subtype is a subgroup of an entity. You can create a subtype if a group of instances share some attributes and relationships that only exist for that group. For example, entity Customer can have a subtype Company and a subtype Person. They share the common attribute customer number, and can have some attributes of their own. Such as birth date for a Person. The entity Customer is called a supertype.

A subtype:

inherits all attributes of the supertype
inherits all relationships of the supertype
usually has one or more own attributes
can have subtypes of its own

Anchor: Independent Entity

An Independent Entity is an entity that implements an Anchor for a Business Key that ‘stands alone’ e.g. that does not contain a reference to another Entity.

Anchor: Dependent Entity

A Dependent Entity is an entity that implements an Anchor for a Business Key that ‘depends’ in its existence on another Entity.

A domain will help you to identify the types of information in your model. It defines the set of values for which a column is valid. A domain can specify a data type, length, precision, mandatoriness, check parameters, and business rules. It can be applied to multiple columns, which makes it easier to standardize data characteristics for columns in different tables.

Each interface may have one or more entities and one entity belongs to only one interface. An entity belongs to an i-refactory data model.

A helper entity creates a derived entity and can be used when you need to transform, filter, or calculate data. The purpose of a helper differs per model:

Technical model: a helper is used to transform data.
Logical validation model: a helper is an implementation of a set constraint (Set Constraint Helper).
Central facts model: a helper is used for a set-based calculation of derived facts (Business Rule Helper).

HSTGIN is the abbreviation of Historical STaging IN. It is an older term to indicate the Logical Validation Model or Logical Validation Layer.

A schema is a set of database objects, such as tables, views, triggers, stored procedures, etc. In some databases a schema is called a namespace. A schema always belongs to one database. However, a database may have one or multiple schema's. A database administrator (DBA) can set different user permissions for each schema.

Each database represents tables internally as <schema_name>.<table_name>, for example tpc_h.customer. A schema helps to distinguish between tables belonging to different data sources. For example, two tables in two schema's can share the same name: tpc_h.customer and complaints.customer.

A Tenant is a delivering party for a dataset or datarecord as agreed in the Delivery Agreement.

TSTGIN is the abbreviation of Technical STaging IN. It is an older term to indicate the Technical Model or Technical Staging Layer.

An index organizes data in a way that improves the speed of data retrieval from a database. To maintain the index data structure, there is a cost of additional writes and storage space.

The acronym CRUD stands for create, read, update, and delete. These are the four basic functions of persistent storage.

OLAP is a acronym for Online Analytical Processing. OLAP is category of software tools which provide analysis of data for business decisions. It uses complex queries to analyze aggregated historical data from OLTP systems.The primary objective is data analysis and not data processing.

OLTP is a acronym for Online transaction processing. OLTP captures, stores, and processes data from transactions in real time. Its primary objective is data processing and not data analysis.

A hub or independent entity is an entity that implements an Anchor for a business key that ‘stands alone’ e.g. that does not contain a reference to another entity. An independent entity contains business key fields, that show up as alternate key (AK), and the primary key (PK) is its surrogate key (ID).

A key is a combination of one or more attributes of an entity that uniquely defines one instance of that entity.

What's New in Release 3.7.0

Automated loading of CSV files into technical staging

When creating a delivery you now can specify to load CSV files automatically into the technical staging tables. You can set values in the property csvLoaderSpecification. When this property is set the i-refactory engine will automatically load supplied CSV files from the location specified in uncPath into the appropriate technical staging tables for this specific delivery.

The property csvLoaderSpecification has the following schema:

csvLoaderSpecificationschema = {
    $id: '/acmDatadel/delivery/csvLoaderSpecification',
    type: 'object',
    properties: {
        uncPath: { type: 'string' },
        rowTerminator: { type: 'string', default: '0x0A' },
        fieldTerminator: { type: 'string', default: ',' },
        codePage: { type: 'string', default: '65001', nullable: false },
        firstRowIsHeader: { type: 'boolean', default: true },
        datafileType: { enum: ['char', 'widechar'], default: 'char' },
        fileExtension: { type: 'string', default: 'csv' },
    },
    additionalProperties: false,
    required: ['uncPath'],
}

For a detailed description of these properties please check the OpenAPI schema specification. If you set a value (the only required value is uncPath) the i-refactory engine will automatically load the data, otherwise you need to load the data into the appropriate tables with your own tooling.

The i-refactory engine will immedatiately start loading the relevant files in parallel when auto-load is applicable. If a required file cannot be read or if a file cannot be loaded with BULK INSERT the task will be set to FAILED (which you can monitor in the web app). The i-refactory engine does not move, remove or otherwise write to the specified uncPath.

You have the ability to REJECT the delivery while still in the loading phase. In this case already running technical load tasks will be completed but other technical load tasks will not be scheduled or processed. The delivery will be set in a REJECTING state and eventually to REJECTED.

This is an example of a delivery with csvLoaderSpecification properties set:

{
    "architectureLayerCode": "HSTGIN",
    "interfaceCode": "TPC_H_HSTGIN",
    "tenantCode": "TPCH",
    "receivedDt": "2018-01-01T00:00:01",
    "sentDt": "2018-01-01T00:00:00",
    "snapshotDt": "2018-01-01T00:00:00",
    "isComplete": true,
    "constraintsToValidate": "ALL",
    "violatedConstraintMaxSampleSize": 50,
    "logicalValidationDeliveryDeleteType": "LOGICAL",
    "deliveredEntity": [],
    "csvLoaderSpecification": {
        "uncPath": "//Mac/home/i-refact-demo-tpch/deliveredData/orderSystem/delivery-report-load",
        "codePage": "raw",
        "fieldTerminator": "|",
        "firstRowIsHeader": false,
        "fileExtension": "tbl"
    }
}

After succesfull acceptance of the delivery the monitor shows the generated load tasks to the technical staging tables. In the example monitor you can see some have them are succeeded, processing and scheduled. The number of parallel load tasks dependents on the concurrency settings.

You can look into the load task and check the generated SQLServer code and csvLoaderSpecification settings:

/**
 * i-refactory version                           : 3.7.0
 * Generated for entityUpdate(auditId)           : 16
 * SourceArchitetureLayer                        : EXT
 * SourceInterface                               : TPC_H_TSTGIN
 * SourceEntity                                  : IR_TSTGIN.tpc_h.LINEITEM
 * SourceEntityClassification                    : STABLE DEPENDENT
 * TargetArchitetureLayer                        : TSTGIN
 * TargetInterface                               : TPC_H_TSTGIN
 * TargetEntity                                  : IR_TSTGIN.tpc_h.LINEITEM
 * TargetEntityClassification                    : STABLE DEPENDENT
 * CsvLoaderSpecification.uncPath                : /var/opt/home/i-refact-demo-tpch/deliveredData/orderSystem/delivery-report-load
 * CsvLoaderSpecification.rowTerminator          : 0x0A
 * CsvLoaderSpecification.fieldTerminator        : |
 * CsvLoaderSpecification.codePage               : raw
 * CsvLoaderSpecification.firstRowIsHeader       : false
 * CsvLoaderSpecification.datafileType           : char
 * CsvLoaderSpecification.fileExtension          : tbl
**/

TRUNCATE TABLE "IR_TSTGIN"."tpc_h"."LINEITEM";

BULK INSERT "IR_TSTGIN"."tpc_h"."LINEITEM"
FROM '/var/opt/home/i-refact-demo-tpch/deliveredData/orderSystem/delivery-report-load/LINEITEM.tbl'
WITH (TABLOCK, DATAFILETYPE='char', FORMAT='CSV', FIRSTROW=1, FIELDTERMINATOR='|', ROWTERMINATOR='0x0A', CODEPAGE='raw')
;

Prerequisites

Making use of the autoload option has some restriction:

It is only supported on SQLServer 2019 or higher. If you try to create a delivery with a value set for csvLoaderSpecification on SQLServer version 2017 or 2016 a functional error is returned.
Tables specified in the technical model cannot contain computed columns.
The CSV file should supply all columns specified in the technical table and in the same order as the column order of the table.
For each table for wich is load task is created a file should exists in uncPath with a name matching the code-property of the technical table. Proper casing of filenames is required. If other files exists in uncPath, they are simply ignored.
The SQLServer application user which is used in the i-refactory config should have read access to the uncPath.
The CSV files should be compliant to the RFC 4180 standard.

Minimal specification

You only need to specify uncPath. The other properties will be initialized with their default values:

By default the file contains a header. In this case the first line of the file will be skipped.
By default we expect a comma as field seperator.
By default we expect a linefeed a row seperator.
By default we expect files are encoded as UTF-8 (codepage: 65001).
By default we expect files do not contain multi-byte characters (UTF-16).
By default we expect files have an extension "csv".

If your CSV files are encoded in another codepage, have a different field or row separator you need to set the appropriate values when creating the delivery.

UTF-16

If you your files are UTF-16 encoded files they should have a byte order mark. You also need to set datafileType to widechar and change the setting of rowTerminator to 0x0A00 or another rowTerminator setting.

Although the SQLServer documentation specified it complies to the RFC 4180 standard there is an issue when UTF-16 files are provided. The last line of a file needs to have a rowTerminator (end of file only does not work). If the last line does not have a rowTerminator this last line will not be loaded.

SQLServer on Linux

Loading CSV files on SQLServer running on Linux has additional limitations:

You cannot set the codePage to a value other than raw.
So in case you have UTF-8 encoded files you need to convert them to UTF-16 (with a byte order mark).

Read access to files

The Tedious driver which we use to connect to SQLServer supports the following connection types:

default => connect with a SQLServer account/password
ntlm => connect with a Windows account and the NTLM protocol
Azure connections types (for further explanation see the Tedious driver documentation): azure-active-directory-default, azure-active-directory-password, azure-active-directory-access-token, azure-active-directory-msi-vm, azure-active-directory-msi-app-service, or azure-active-directory-service-principal-secret.

If files needs to be accessed on for instance a fileshare there are limitations in how to access these files.

If a connection is made with a SQLServer account the SQLServer instance should be started under a specific domain account. And this specific domain account should be granted access to the fileshares. SQLServer will delegate the bulk insert statement to the domain account under which SQLServer has been started.
If a connection is made through NTLM (with a Windows account) you cannot access files on a remote fileshare. This is caused by the fact that SQLServer needs to access another server and needs to pass the original credentials of the user requesting this access. When an NTLM connection has been established there is no way of passing the password to the other server. SQLServer in this case will attempt an anonymous logon. The server will reject file access for anonymous logins resulting in a BULK INSERT statement error. Loading of files in this case is only possible if the files can be accessed without authentication (store locally, ...).

To allow for delegated access to another server a connection based on Kerberos is required. Unfortunately the Tedious driver does not support Kerberos based authentication. We are currently investigating the ability to connect through ODBC with Kerberos authentication support. For bulk loading data we will then use the ODBC based connection while still relying on the Tedious driver for the rest of the application.

Performance improvement Generic Data Access views

The i-refactory engine stores data over time. For the GDAL current views (CRUD) and GDAL business key views which read the data in the fact store we need to apply a filter to only retrieve the latest version which is not logically removed.

The generated query in the view made use of a self join to determine which versioned record is the actual (or latest version). This query is quite expensive (due to the fact it needed a join).

In this release we changed the query such that it uses the value of acm_end_dt to get the latest version. Queries (especially when retrieving large sets) now on average consume 75% less CPU, the amount of logical reads is reduced with 90% and the elapsed time is reduced with 78%.

The read performance improvements do come at a small cost of additional storage and write throughput. An additional index is automatically created on each context table which tracks changes over time. This index is automatically created upon importing a fact based model in case this index does not yet exist.

Bug Fixes

Issue	Summary
datatype truncation	Applying where conditions on datetime columns in the Rest API resulted in truncation to scale 1 when a more precise scale value was supplied (ie milliseconds).
flush transaction errors	In case SQLServer reported a primary key violation error it also reports the record that caused this violation. When this reported record contains multibyte characters the i-refactory engine was not capable of storing the error message resulting in a flush transaction error. The error message property in the database was of datatype varchar and is now changed to nvarchar where applicable.

Upgrade

Upgrade Notes

Release notes 3.1.1

Bug fixes

Issue	Summary
Auxiliary key in Generic Data Access Model	Attributes for independent auxiliary relations were added to the auxiliary key when a dependent auxiliary reference was added afterwards. This issue is now fixed.
Name to code conversion	The name to code conversion was applied for key columns in a dependent anchor which were not a reference to another anchor. This results in unwanted overrules of the code convention of a column.
Export issues	Export to a temporary directory did not work as expected regarding the timestamp added and used in scripts.

Index

What's New in Release 3.2.0

New features summary

Application Changes

Skipped Row Option

CRUD merge statement support

Rest API

POST Changed

PATCH Changed

GET Changed

NodeJS

Upgrade

Bug Fixes

Candidate key

Business Key

Foreign Key

Context

Anchor: Independent Entity

Anchor: Dependent Entity

What's New in Release 3.7.0

Automated loading of CSV files into technical staging

Prerequisites

Minimal specification

UTF-16

SQLServer on Linux

Read access to files

Performance improvement Generic Data Access views

Bug Fixes

Upgrade

Release notes 3.1.1

Bug fixes