Release notes 2.6.0

2023-04-17

Metamodel changes

  1. A new property violatedConstraintMaxSampleSize is added to Delivery.

    If no value is set when creating a delivery the property will default to a value of 1.000.

    This value will limit the number of records that will be registered in ViolatedRecord and ViolatedConstraint.

    For each delivery a count of the number of violations for each validated constraint is registered in DeliveryViolatedConstraintCount.

    Delivery Violated Constraints
    Delivery Violated Constraints

  2. The entity RelationshipConstraint is removed.

    A relationship constraint is from now on available on both directions of the relationship (if applicable). The entity ParentRelationshipExistsConstraint registers all relationship constraints from a child entity key to a parent entity key. This is the traditional parent-child relationship.

    The entity ChildRelationshipExistsConstraint registers all relationship constraints from a parent entity key to a child entity key where at least a child entity instance should exists for a given parent entity instance.

    An example: An order should at least have one line item.

    Relationship Constraint
    Relationship Constraint

New features

Limit the number of records to be registered in ViolatedRecord and ViolatedConstraint

When creating a Delivery a maxSampleSize can be specified (the default value is set to 1.000). Instead of registering each and every violation in ViolatedRecord and ViolatedConstraint the number of records registered can be limited to the value specified in violatedConstraintMaxSampleSize. This will significantly improve the throughput of the validation process and lowers the storage cost in case of a high volume of constraint violations. This sample size value is per constraint. So for each constraint a maximum of sample size records will be registered, including the record on which the constraint violated.

The set of violated records is first ordered so we will always register the records with the most violated constraints.

The validated constraints and the functionality of the validation process is not affected. Still each and every constraint to be validated will be executed. The total count of violations for each and every constraint will now be registered in DeliveryViolatedConstraintCount.

If a full log of each and every record with violations is still required you should set a high value for maxSampleSize when creating a Delivery. This affects the storage and throughput as registering high volumes of violatedRecords and violatedConstraints can be costly.

Default value for snapshot datetime for a deliveredEntity

When creating a Delivery with a list of DeliveredEntities up till now you should specify a value for the snapshotDatetime for each DeliveredEntity. In this release if a value is not set for the property snapshotDatetime it will default to system datetime.

With this change issues with different clock times on different servers can be circumvented by letting the i-refactory server decide regarding system datetime.

Mandatory child relationship

When a relationship in the logical validation model is set to a cardinality 1..* at least one child instance should exist for a parent instance (see the image below how you can set this property in Powerdesigner). This mandatory child existence constraints is now automatically checked for each relationship where the cardinality is set to 1..*. This eliminates the need to create a custom entity set constraint for mandatory child existence constraints.

This constraint type is registered in ChildRelationshipExistsConstraint. The regular relationship from child to parent is registered in ParentRelationshipExistsConstraint.

Cardinality Setting
Cardinality Setting

Composite independent foreign keys

If an entity in a logical validation model has an attribute that plays a role in more than one relationship where one of them plays a role in a dependent relationship and another one plays a role in an independent relationship you had to create a computed column. From now on you do not have to create a computed column in other to be able to properly map this information to a central facts context entity.

Given the example logical validation model a Nation has a dependent reference with Snapshot Date and an independent reference with Region. The SNAPSHOT DATE attribute plays 2 roles: a reference to Snapshot Date and a reference to Region.

In the corresponding fact model Nation is dependent on Snapshot Date and the relationship between Nation and Region is stored in a context entity Nation Region.

Composite Foreign Key
Composite Foreign Key

We can now map the attribute SNAPSHOT DATE to the context entity Nation Region to both the ID column and the Region ID column. In prior releases this was not possible.

Composite Foreign Key Mapping
Composite Foreign Key Mapping

Allow for executing attribute value constraint on null values

Attribute value constraints were only executed if the attribute contains a value after having executed the attribute datatype constraint.

We will now execute the attribute value constraints if the attribute has a value in the technical staging table or the attribute still has a value after having executed the attribute datatype constraint.

In order to be able to implement this change we had to create a distinct column name for the technical staging column name versus the logical staging column name. We have implemented this by prefixing and postfixing each technical staging attribute with a tilde. We assume that no entities exists where this might result in a name conflict.

For example creating an entity with the following column names is not allowed:

CREATE TABLE wrong_column_naming
(
 "name" varchar(15)
,"~name~" varchar(10)
)

Why? It results in the following intermediate table (which is generated runtime) with a conflict on name:

CREATE TABLE #intermediate_table
(
 "name" varchar(15)
,"~name~" varchar(10)
,"~name~" varchar(15) -- Conflicting name. Already exists.
,"~~name~~" varchar(10)
)

Performance related enhancements in validating constraints

  1. Indexes are now automatically created on the intermediate storage tables for the basic and set related constraints also taking into account computed columns.
  2. Casting from a source to target datatype is skipped if casting is not required.
  3. Some parts of the generated SQL code is optimised.

Reduced lock contention

Lock timeout errors on restful request should occur less often and the concurrency level of handling restful request is strongly improved. We have implemented a more fine grained locking principle when handling requests.

{note} Deadlocks or database contention however might still occur.

Changes

Validation of relationship

From this release on we will validate a relationship only if all the attributes involved in the relationship have a value. This in contrast to previous releases where we'd validate a relationship if one of the relationship attributes has a value.

The reason for this change is to have a uniform approach in validating relationships with overlapping roles.

As a side effect of this new approach you should create a record constraint for each independent relationship that is optional and involves more than one non primary key attribute. This record constraint should validate on either all columns having a value or all columns not having a value. If this constraint is violated you should either skip the row or set the threshold to zero.

Determination of consistent transaction time

If GREATEST is chosen as consistent time for a business rule helper from now on the calculation of the consistent transaction time will return the highest transaction time value even if one of the input entities has an undefined value for the transaction time.

Businesskeys in a Rest get request

A Rest get request will now consistently return all the business key attributes. In previous releases the business key attributes were only returned if the query could be executed on the cache. From now on we will return the business key attributes in Rest get request for queries executed on the database as well.

Cached queries now correctly return the specified result

For example:

When a get request was issued on Entity with a join on Attribute where attribute.code = statusCode only entities with an attribute statusCode were returned. But instead of only returning the attribute statusCode all attributes of the entity were returned. From now on you will still get the properly filtered entities but the attribute filter is now correctly applied as well.

{note} This was a known and open issue and is now changed.

Query pushdown from Node server cache to database

Get requests are pushed down to the database server if the cost of executing them on the Node server cache exceeds a given threshold. This will not reduce the overall elapsed time in returning the result but will prevent the node server from becoming not responsive as returning the result from the node server cache will block the server from accepting other requests for a while.

Execution of CFPL Business Rule

From now on the entity update to a CFPL target entity with a CFPL Business rule as the source entity will be executed even if a GDAL Crud is active on the target entity.

This enables initialising/loading facts to an anchor/context while in the meantime have the ability to update this derived context. However, no guarantees are given that conflicts may arise due to transaction time problems which might be caused by a CRUD transaction operating on current time while the business rule helper tries to update context on a transaction time less than the current time. To circumvent conflicts the business rule helper should only insert and not update or delete.

Blocking of GDAL deliveries

A GDAL delivery will be blocked (entirely) if at least one it's entities will be updated by a logical validation model delivery. If however a GDAL model only reads and writes to an entity which is updated by a business rule helper the GDAL will not be blocked.

Bug Fixes

Issue Summary
[IREFACTORY-1289] An invalid state transition error could sometimes occur. This was caused by removing a cached record while a pending flush transaction was still open.
[IREFACTORY-1318] Import of metadata went wrong when entities of a fact model were completed removed and a generic data access interface was imported as well. The error was caused by an not properly removing the cached relationship between entities.
[IREFACTORY-1301] An arithmetic overflow error was sometimes returned in the web app showing the delivery statistics. This is caused by SQL Server summing before filtering. The summed result did not fit in the integer datatype. Fixed by first casting the sum to a bigint datatype.
[IREFACTORY-1279] To be able to properly validate all set constraints on a logical validation model we constructed the set of rows from the rows delivered and the rows already available in the logical validation model from previous deliveries. In case the newly provided set of rows was a delta set the addition of rows from the existing logical validation model was not filtered correctly.
[IREFACTORY-1276] During the execution of the SQLServer installation script an error might occur indicating a primary key violation. This is fixed.
[IREFACTORY-1319] An invalid query path resulted on a Rest get request resulted in a 500 error instead of 409 error.

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.

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.

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 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).

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

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.

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.

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.

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.

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.