Introduction

Analytics Middle Tier 2.0 is becoming a full citizen in the Ed-Fi platform in 2020, rather than just a proof-of-concept on the Ed-Fi Exchange. As it grows up, it needs to correct some architectural concerns that came up as feedback from the field. It also needs to be up to par with the latest release of the ODS/API, version 3.3. This document aims to inform about the challenges and elicit feedback on the real-world usefulness of the proposed solutions.

Naming Convention

Requirement

Hold names to under 63 characters for PostgreSQL compatibility.

Design

Currently, none of the objects have a name that violates this constraint. To help avoid problems with future views, it is proposed to either truncate "Dimension" to "Dim" or drop the word altogether. Generally, clarity should be preferred over length when naming objects, hence the question is: how much clarity is lost if we move away from the "Dimension" suffix?

Multi Data Standard Support

Requirement

Multi Data Standard Support

Requirement

Support installing the views on ODS databases supporting multiple data standards (2.2, 3.1, 3.2).

Design

Design

Version 1.3.0 added support for Data Standard 3.1, which was used by ODS/API 3.1.1 and 3.2, through the use of the –dataStandard <Ds2 | Ds31>  argument. 

Forcing the user to remember which data standard is installed is sub-optimal. We should be able to detect this implicitly and install the correct version without user input.

1. If table AddressType exists, then install Data Standard 2.  if needed
2. Else if table VersionLevel exists, then install Data Standard 3.1.
3. Else if table DeployJournal exists, then install Data Standard 3.2.
4. Else throw an error: "Unable to determine the ODS database version".

Student, Parent, and Staff Keys

Requirement

The views should expose "Key" fields based on the natural key of the underlying table. 

Design

In the case of StudentDimension , ContactPersonDimension , and UserDimension , the original release used StudentUSI , ParentUSI , and StaffUSI  respectively. The "USI" columns are primary keys and were used by mistake. The "UniqueId" columns are the correct natural keys.

Change all instances of StudentKey , ContactPersonKey , and UserKey  to use the corresponding "UniqueId" column from the source table.

Descriptor and Type Mapping

Requirement

Decouple the views from hard-coded Descriptor and Type values. 

Context

many of the views need to lookup records by Descriptor value - for instance, looking up the Attendance records where a student has an "Excused Absence" or "Unexcused Absence." Because the original developer had access to only a limited dataset, it was not realized that the Descriptor values will vary widely from one implementation to the next. Thus the hard-coding needs to be decoupled, allowing the implementation to provide a mapping from their Descriptor value to the concept used by the Analytics Middle Tier.

In theory, the various "Types" values in Data Standard 2 should provide a more universal constant than the Descriptors. However, some community members report that these too are mutable. Therefore, (a) using Types is not a solution for Data Standard 2, and (b) even those views with hard-coding to Types instead of Descriptors must be modified for greater independence. Note: Type tables were removed in Data Standard 3 precisely because they were not being used in the originally-designed manner. 

Design

Summary

Student, Parent, and Staff Keys

Requirement

The views should expose "Key" fields based on the natural key of the underlying table. 

Design

In the case of StudentDimension , ContactPersonDimension , and UserDimension , the original release used StudentUSI , ParentUSI , and StaffUSI  respectively. The "USI" columns are primary keys and were used by mistake. The "UniqueId" columns are the correct natural keys.

Change all instances of StudentKey , ContactPersonKey , and UserKey  to use the corresponding "UniqueId" column from the source table.

Descriptor and Type Mapping

Requirement

Decouple the views from hard-coded Descriptor and Type values. 

Context

many of the views need to lookup records by Descriptor value - for instance, looking up the Attendance records where a student has an "Excused Absence" or "Unexcused Absence." Because the original developer had access to only a limited dataset, it was not realized that the Descriptor values will vary widely from one implementation to the next. Thus the hard-coding needs to be decoupled, allowing the implementation to provide a mapping from their Descriptor value to the concept used by the Analytics Middle Tier.

In theory, the various "Types" values in Data Standard 2 should provide a more universal constant than the Descriptors. However, some community members report that these too are mutable. Therefore, (a) using Types is not a solution for Data Standard 2, and (b) even those views with hard-coding to Types instead of Descriptors must be modified for greater independence. Note: Type tables were removed in Data Standard 3 precisely because they were not being used in the originally-designed manner. 

Design

Summary

1. Move hard-coded values to a "Constants" table.
2. Create mapping tables that link Descriptors or Types to Constants.
3. Modify all views as needed to join to the Constants and new mapping tables.

Example

In Version 1.x, the StudentEarlyWarningFact view reports on excused and unexcused absences, looking for StudentSchoolAttendanceEvent  records with attendance descriptor values of either "Excused Absence" or "Unexcused Absence".

In version 2, the view would now search for all StudentSchoolAttendanceEvent records whose descriptor maps to the relevant constant. Thus there would be two DescriptorMap  values, one each for "Excused Absence" and "Unexcused Absence." Any school who uses a different term than these two would create a DescriptorMap  record mapping that term to the DescriptorConstant  value of "Absent".

Implications

Those who install the Analytics Middle Tier will need to carefully assess their Descriptors and Types, and then manage the DescriptorMap table (and TypeMap , for Data Standard 2) accordingly. 

Default Mappings

A new command-line Option will be provided to run a script that loads the default Descriptor mapping for the default Ed-Fi descriptors (minimal/populated template descriptors).

.\EdFi.AnalyticsMiddleTier.exe --connectionString "..." --options DefaultMap

Student Dimension Uniqueness

Changes to the Student Dimension

Requirements

1. Create a "Student" dimension with a single unique key.
2. Provide intuitive access to student demographics.

Context

Student Dimension Uniqueness

The Early Warning System fact views both assumed that the StudentDimension  would only have a single record for a student, as shown in these diagrams:

Image RemovedImage Removed

However, a student . However, a student could be enrolled in multiple schools at the same time, resulting in two records in the StudentDimension for the same StudentKey. This is problematic for the PowerBI Starter Kit, which has a hard requirement for unique StudentKeys.

At the same time, the Program-related fact and event views are looking at a Student in a given Local Education Agency, whereas the facts above are looking at a Student at a School. Although the ODS database supports a Student enrolled in a Program associated with a School, our input from the field tells us that Program association is on the LEA not the school. Thus we need to balance these competing tensions in any change to the data model.

Design

Data Standard 3 moved the student demographics into the StudentEducationOrganizationAssociation  table, away from the Student table. The current StudentDimension  view provides student name, primary contact, and demographics. The proposal is to split this into two different views, changing the Early Warning System star schema to a snowflake.

The demographics on the view are secondary to the student-school enrollment relationship. Therefore we will have a new view called StudentEnrollment to show active enrollment/school association information, with a StudentEnrollmentKey  that combines the StudentUniqueId  and SchoolId (maintaining the principal of needing only one column for a join). The early warning views would then change to use the single StudentEnrollmentKey  in addition to the two separate StudentKey  and SchoolKey  columns. The original keys are retained to facilitate aggregating by student or school.

The StudentDimension  would continue to exist in a reduced form, providing name and primary contact information. The Program views would continue to have the StudentKey in them (no changes).

Impact on Security

By leaving StudentKey  and SchoolKey in the views above, the current row-level security views will not require any changes.

Problem

What if someone wants to build queries that analyze program participation by demographic data?

Suppose we have the following data (partial representation):

ProgramTypeDimension

StudentProgramEvent

StudentEnrollmentDimension

Here a student is enrolled at two schools, presumably in the same district. Perhaps the program enrollment is due to a specialized class in the student's home language, at a different school than the one attended daily. Since data entry occurred twice - once for each school - there is an opportunity for inconsistency. In this case, one of the school enrollment entries failed to note that the student has limited English Proficiency.

We can write the following query to analyze this program proficiency by LimitedEnglishProficiency.

select
	ProgramTypeDimension.ProgramType,
    count(1) as EnrolledCount,
    sum(case when StudentEnrollmentDimension.LimitedEnglishProficiency <> 'Not applicable' then 1 else 0 end) as LimitedEnglishProficiencyCount
from
	analytics.ProgramTypeDimension
inner join
	analytics.StudentProgramEvent on ProgramTypeDimension.ProgramTypeKey = StudentProgramEvent.ProgramTypeKey
inner join
	analytics.StudentEnrollmentDimension on StudentProgramEvent.StudentKey = StudentEnrollmentDimension.StudentKey
where
	StudentProgramEvent.ProgramEventType = 'Enter'
group by
	ProgramTypeDimension.ProgramType

The result will be

In reality there is only one student, yet this naive query counted two students. And it will have the odd result of showing that 50% of the students have limited English proficiency!

Arguably, there is a data quality problem that the Analytics Middle Tier cannot solve. But there is also a training issue: the data analyst needs a deeper understanding of the nuances of Ed-Fi data storage and of the Analytics Middle Tier views. Ignoring the data inconsistency problem, the following query would resolve the double-count problem:

select
	ProgramTypeDimension.ProgramType,
    count(1) as EnrolledCount,
    sum(case when StudentDimensionEnrollment.LimitedEnglishProficiency <> 'Not applicable' then 1 else 0 end) as LimitedEnglishProficiencyCount
from
	analytics.ProgramTypeDimension
inner join
	analytics.StudentProgramEvent on ProgramTypeDimension.ProgramTypeKey = StudentProgramEvent.ProgramTypeKey
outer apply (
	select distinct
		LimitedEnglishProficiency
	from
		analytics.StudentEnrollmentDimension 
	where
		StudentKey = StudentProgramEvent.StudentKey
) as StudentDimensionEnrollment
where
	StudentProgramEvent.ProgramEventType = 'Enter'
group by
	ProgramTypeDimension.ProgramType

These problems are rather ugly, and would be difficult to detect unless you're actively looking for them. Furthermore, while writing an updated SQL query was easy, how would one represent this in a business intelligence tool like Tableau or PowerBI?

Status

School Year

Requirement

Add SchoolYear to help support longitudinal data.

Design

Data Standard 2's support for School Year is limited compared to Data Standard 3. The following dimension views could have a SchoolYear  column in them:

The DateDimension  view does not include a SchoolKey as it was intended as a generic date table. Although it strikes the writer as a nonsensical, there could be two different school years for the same date at different schools. Therefore it does not seem appropriate to add this column to the DateDimension. 

There might be value in having the SchoolYear  in the GradingPeriod  and making the decision that Data Standard 2 would always return null for this column (so that the interfaces stay consistent): one could use this to create a filter hierarchy School Year > Grading Period. However, other than interpolating the begin and end dates on the Grading Period, there is currently no way to build out the hierarchy beyond this. The PeriodSequence is insufficient for determining a parent-child relationship between grading periods.

When trying to filter the StudentEarlyWarningFact  or StudentSectionGradeFact views, the available school years could be extracted from either the Student or Student Section dimensions to create a slicer. However, this would be much simpler if the SchoolYear  were simply included in those fact views.

Status

Requesting additional input before committing. Respond to Stephen Fuqua, #dev-analytics-middle-tier on Slack, or with a comment on this page.

Questions to address. Should we add a SchoolYear  column to...

Separation Between Core and Use-Case Views

Requirement

Manage a collection of "core" views and separate collections of use-case specific views.

Design

The application already has a concept for installing optional components, which was first created for optional install of additional indexes in the ODS. Proposal:

1. analytics_rls  for Row-level Security
2. analytics_ews  for Early Warning System
3. analytics_quicksight  for QuickSight
4. analytics_program  for Program Analysis

Thus to install the Early Warning System and Row-level security collections used by the Power BI Starter Kit v2, the admin user would run this command:

.\EdFi.AnalyticsMiddleTier.exe --connectionString "..." --options EWS RLS

For deployment purposes, it would be ideal to separate these by "namespace" - or in SQL terms, by schema.

Additional Views

Will not add any new views in the 2.0 release. New views can be added with 2.1, 2.2 etc. This 2.0 release is all about fixing architectural problems and setting the stage for broader adoption.

Demographics in Ed-Fi UDM v2.2

Sources for student demographics:

• edfi.Student  contains sex, Hispanic/Latino ethnicity, economic disadvantaged (Bool), school foodservice eligibility, limited English proficiency.
• One-to-many tables:
  • edfi.StudentCohortYear 
  • edfi.StudentDisability
  • edfi.StudentLanguage
  • edfi.StudentLanguageUse
  • edfi.StudentProgramAssociation
  • edfi.StudentCharacteristic is a generic table, and contains begin/end date
  • edfi.StudentRace 

Demographics in Ed-Fi UDM v3.x

Sources for student demographics:

• edfi.StudentSchoolAssociation  contains School Year, Enrollment Date and Grade Level
• edfi.StudentEducationOrganizationAssociation  contains Sex, Hispanic/Latino ethnicity, and Limited English Proficiency
• There are a series of many-to-many tables to store specific types of multi-value demographic characteristics -  note these can be saved for either the school or the district (or charter, state, ESC, etc.)
  • edfi.StudentEducationOrganizationAssociationCohortYear 
  • edfi.StudentEducationOrganizationAssociationDisability 
  • edfi.StudentEducationOrganizationAssociationLanguage 
  • edfi.StudentEducationOrganizationAssociationLanguageUse 
  • edfi.StudentEducationOrganizationAssociationRace 
  • edfi.StudentEducationOrganizationAssociationTribalAffiliation 
• And there is the generic edfi.StudentEducationOrganizationAssociationStudentCharacteristic table, which has a time Period associated with it.
  • Includes food service eligibility, which was present on Student  as a Boolean in version 1.

Dimension or Fact?

While gender, race, and ethnicity all have strings associated with them, some elements of the demographic and enrollment data are more fact-oriented than dimension-oriented:

• IsHispanic
• IsEconomicallyDisadvantaged
• LimitedEnglishProficiency
• SchoolEnrollmentDate

The ODS does not support slowly-changing dimensions, so there is only ever one current snapshot of these data - one cannot tie them to a date unless referring to the enrollment date... except in the case of food service eligibility because there is a time period.

Foodservice Eligibility

Foodservice eligibility is tracked via a Program Association, which not a demographic in the Ed-Fi Unified Data Model. Therefore it should be removed from demographics and placed in the program association views. For supporting the Power Bi Starter Kit, a new Early Warning System view might be needed that tries to preserve the old StudentDimension in some ways, including flattening the foodservice eligibility into a single Boolean value.

Design Proposal

Summary:

1. Eliminate the idea of a separate "Student Dimension" in the core data collection.
2. Create two new, very similar, dimensions to replace the old StudentDimension:
   1. StudentSchoolDim 
   2. StudentLocalEducationAgencyDim 
3. Combine the data from various edfi.StudentEducationOrganizationAssociationXYZ  tables into a single view, DemographicDim.
4. Create two bridge tables to link student information to the characteristics
   1. StudentSchoolDemographicBridge 
   2. StudentLocalEducationAgencyDemographicBridge 

Rationale for the Student Dimension replacement:

1. Students don't (or shouldn't) exist in isolation from an organization - hence no need for a StudentDim.
2. Across the Ed-Fi data model, there are two different student relationships:
   1. With the school (e.g. StudentSchoolAssociation, StudentDisciplineIncident, StudentGradebookEntry, etc.).

   2. And with the more generic Education Organization - which in the current context of the Analytics Middle Tier, generally means Local Education Agency.

      Note
      Exception: StudentAssessment is only connected directly to a Student! From analytics viewpoint, we will define the Analytics Middle Tier as assuming that analytics on assessment data will always be in the context of a School or Local Education Agency.

      
      

3. In defining meaningful StudentSchool  and StudentLocalEducationAgency  entities, there will be some overlap of fields - but the data could be different. This is an inherently dangerous area of the Ed-Fi data model. If we were to combine the data into a single perspective, then we would be hiding the danger. The data analyst will need to read and understand why there are two "root entities" for data reporting, and then choose which one to use based on their implementation.
4. The Analytics Middle Tier is intended for Local Education Agency use cases. Other use cases can be added in the future as needed to support other types of Education Organizations (e.g. a future view StudentStateEducationAgencyDim ).

Rationale for combining the various characteristic tables into a single point in time view:

1. There are seven (in Data Standard 3+) similar characteristics tables that do not have time periods associated with them
   1. CohortYear
   2. Disability
   3. DisabilityDesignation
   4. Language
   5. LanguageUse
   6. Race
   7. TribalAffiliation
2. And there is one with a time period: StudentCharacteristic
3. Those without a time period can be combined into a single view for "demographics"
4. Those  without a time period can also be included in that view, so long as the data analyst understands that the "Bridge" between the student and the demographics represents "data as of right now".
5. The two with a time period can, in the future, be used to create new Fact views that link to the date range. See Program Views below.

DemographicDim

Ultimately these values come from the edfi.Descriptor  table, although not all descriptors will be here. String values will be used for keys instead of DescriptorId in order to allow combining data from multiple year-specific ODS databases into a single data mart - this would not be possible with the auto-incremented DescriptorId  since that value will differ between ODS database instances.

Structure

Data Standard 2.2 Source Tables

Descriptors for the following tables:

1. StudentCohortYear
2. StudentDisability
3. StudentLanguage
4. StudentLanguageUse
5. StudentRace
6. StudentCharacteristic (where time dates encompass "now")

Student contains "IsEconomicDisadvantaged" in DS 2, whereas this is now one of the "StudentCharacteristics" in DS 3. In order to have parity between the two data standards, the DemographicDim  view therefor needs a hard-coded row that does not come from a table:

Otherwise the sample records will be as with the Data Standard 3+ samples below.

Data Standard 3+ Source Tables

Descriptors for the following tables:

1. StudentEducationAgencyCohortYear
2. StudentEducationAgencyDisability
3. StudentEducationAgencyDisabilityDesignation
4. StudentEducationAgencyLanguage
5. StudentEducationAgencyLanguageUse
6. StudentEducationAgencyRace
7. StudentEducationAgencyTribalAffiliation
8. StudentEducationAgencyStudentCharacteristic (where time StudentEducationAgencyStudentCharacteristicPeriod encompasses "now")

Sample Records

StudentSchoolDim

Data Standard 2.2

Data Standard 3+

StudentLocalEducationAgencyDim

Data Standard 2.2

Data Standard 3+

StudentSchoolDemographicsBridge

Data Standard 2.2

Must be composed of a series of union queries that combine records from these tables:

1. StudentCohortYear
2. StudentDisability
3. StudentLanguage
4. StudentLanguageUse
5. StudentRace
6. StudentCharacteristic (where time dates encompass "now")

As well as a record for "StudentCharacteristic#Economic Disadvantaged" if Student.IsEconomicDisadvantaged is true.

Data Standard 3+

Must be composed of a series of union queries that combine records from these tables:

1. StudentEducationOrganizationCohortYear
2. StudentEducationOrganizationDisability
3. StudentEducationOrganizationDisabilityDesignation
4. StudentEducationOrganizationLanguage
5. StudentEducationOrganizationLanguageUse
6. StudentEducationOrganizationRace
7. StudentEducationOrganizationTribalAffiliation
8. StudentEducationOrganizationStudentCharacteristic (where StudentEducationOrganizationStudentCharacteristicPeriod dates encompass "now")

The joins need to be from Student → StudentSchoolAssociation → these tables, with StudentSchoolAssociation.SchoolId serving as the EducationOrganizationId in the joins.

StudentLocalEducationAgencyDemographicsBridge

Data Standard 2.2

Must be composed of a series of union queries that combine records from these tables:

1. StudentCohortYear
2. StudentDisability
3. StudentLanguage
4. StudentLanguageUse
5. StudentRace
6. StudentCharacteristic (where time dates encompass "now")

As well as a record for "StudentCharacteristic#Economic Disadvantaged" if Student.IsEconomicDisadvantaged is true.

Data Standard 3+

Must be composed of a series of union queries that combine records from these tables:

1. StudentEducationOrganizationCohortYear
2. StudentEducationOrganizationDisability
3. StudentEducationOrganizationDisabilityDesignation
4. StudentEducationOrganizationLanguage
5. StudentEducationOrganizationLanguageUse
6. StudentEducationOrganizationRace
7. StudentEducationOrganizationTribalAffiliation
8. StudentEducationOrganizationStudentCharacteristic (where StudentEducationOrganizationStudentCharacteristicPeriod dates encompass "now")

The joins need be from Student → StudentSchoolAssociation → School, with the School.LocalEducationAgencyId serving as the EducationOrganizationId in the other joins.

Alternatives

The following alternatives were considered and rejected

Split StudentDim into StudentDim and StudentEnrollmentDim

The original StudentDimension  would be split in two: StudentDim  with no SchoolKey  in it and a StudentEnrollmentDim  (or StudentDemographicDim ) holding the Student-to-school relationship and demographics. Rejected for these reasons:

1. Trying to keep the number of views as small as possible, so that the domain model is easier to understand compared to the source Ed-Fi data model.
2. Generally need to query for that student-school relationship - not for a student in isolation.
3. Keeping only a single "enrollment" or "demographic" dimension for the student requires implementing business logic to determine which demographics take precedence - if demographics are saved for both school and local education agency, then when one should be used? Whichever choice is made, it will likely be wrong for many implementations.

Change StudentDim to StudentSchoolDim

In this version, the old StudentDimension  is essentially renamed to StudentSchoolDim - largely preserving the old structure. Compared to the proposed model, this version allows the data analyst to quickly and easily find the right student information. It also relieves the analyst from having to decide which version of truth to use - the School or the Local Education Agency. As mentioned above, it has been decided that the Analytics Middle Tier should not gloss over this difficulty: the data analyst must inspect their implementation and decide which perspective (School or Local Education Agency) is appropriate in each circumstance.

Create Separate Bridge Tables for Each Demographic

Instead of combining the demographics into a single bridge view, we could have created one for each concept: Disability, Race, Tribal Affiliation, etc. On one level, this would have simplified the data analyst's work when looking for a particular demographic field: they can just look for the view with the word "Race" in the name, for example. However, this comes at the expense of proliferating more tables, making the Analytics Middle Tier look too much like the Ed-Fi data standard.

Program Views

Requirement

Support analytics on Program Participation at the school level.

Context

A small set of Program-related views was added to Analytics Middle Tier as an experiment in supporting a second use-case: analyzing student program participation. Programs in the default Ed-Fi ODS template include "Bilingual", "Career and Technical Education", "Special Education", and a few others. These data are represented in two different fact views: analytics.StudentProgramEvent  and analytics.StudentProgramFact 

Image Added

The "Event" view represents the date on which a student entered or exited a program. The "Fact" view represents every day on which the student was in a program. Each perspective has its own utility in analytics / reporting.

Note, however, that they both join to analytics.LocalEducationAgencyDimension . There is no linkage to schools. This is because the data modeler originally heard (or thought he heard) that program enrollment is "always" at the district level. Since then, he has received feedback that many implementations do link students to programs at the school level, or even at the state level.

Design

a Remove the Views

Eliminate the problem by eliminating the views, unless and until we get a detailed real-world use case definition that would solve these problems.

b Add a SchoolKey to Both Views

This implies that SchoolKey  or LocalEducationAgencyKey  could be null, generally an undesirable situation in dimensional modeling. A few options:

1. Ignore the problem: downstream data analyst have to join the program views to SchoolDimension  or LocalEducationAgencyDimension  with an outer join.
   1.  Good for data architect.
   2.  Dangerous for data analyst.
      
      
2. Nulls can be eliminated - or at least nearly eliminated - for LocalEducationAgencyKey by loading a School's LocalEducationAgencyKey  value. 
   1.  Moderate additional complexity for data architecture.
   2.  Resolves one outer join problem but leaves the other in place.
      
      
3. Create a "fake school" for each LEA in the SchoolDimension , with SchoolName = 'n/a' . Use this as the SchoolKey  when program participation is only at the LEA level.
   1.  Ugly for the data architect, although not impossible.
   2.  Resolves the other outer join problem, at the expense of having a strange "District" entry show up in School filters. Dubious value.
      
      
4. Separate the views into copies for School and LocalEducationAgency.
   1.  Just forces the problem onto the data analyst.

The Data Standard  shows that a School can belong to 0 or 1 Local Education Agency. Side note: that Agency might be a Charter Management Organization. Thus option 2 can still lead to lost records when using an INNER JOIN. As with Option 3, null/missing records can be eliminated by creating a "n/a" LocalEducationAgency for these schools.

If these program views are to be kept, then a combination of options 2 and 3 seems like the only option that presents a useful interface to the data analyst.

School Year

Requirement

Add SchoolYear to help support longitudinal data / multi-year databases. Wherever possible, would be nice to support drill-down hierarchies by school year.

Design

The following dimension views could have a SchoolYear  column in them; Data Standard 2's support for School Year is limited compared to Data Standard 3. 

The multi-year use-case was not originally one of the goals of the Analytics MIddle Tier, so no consideration was given to adding to the two views that could support it. It will be trivial to add to these two views in common above.

For Date and Grading Period, there is real value. To support in Data Standard 2, we would need to create a mapping table or extra column on each of those two tables. This takes into account that one record could below to multiple school years in some edge cases. The additional effort required may push solving for Date and Grading Period to a future release, e.g. Analytics Middle Tier 2.1.

Separation Between Core and Use-Case Views

Requirement

Manage a collection of "core" views and separate collections of use-case specific views.

Design

The application already has a concept for installing optional components, which was first created for optional install of additional indexes in the ODS. Proposal:

1. Always install a core set of views
   1. ContactPersonDimension
   2. DateDimension
   3. GradingPeriodDimension
   4. LocalEducationAgencyDimension
   5. MostRecentGradingPeriod
   6. SchoolDimension
   7. SchoolNetworkAssociationDimension
   8. StudentDimension
   9. StudentEnrollmentDimension (if created, see above)
   10. StudentSectionDimension
       
       
2. Move some of the existing views into new optional collections:
   1. Row-level Security (RLS)
      1. StudentDataAuthorization
      2. UserAuthorization
      3. UserDimension
         
         
   2. Early Warning System (EWS)
      1. StudentEarlyWarningFact
      2. StudentSectionGradeFact
         
         
   3. QuickSight-Early Warning System (QEWS)
      1. Ews_SchoolRiskTrend
      2. Ews_StudentAttendanceTrend
      3. Ews_StudentEnrolledSectionGrade
      4. Ews_StudentEnrolledSectionGradeTrend
      5. Ews_StudentIndicators
      6. Ews_StudentIndicatorsByGradingPeriod
      7. Ews_UserSchoolAuthorization
         
         
   4. Program Analysis (PROGRAM)
      1. ProgramTypeDimension
      2. StudentProgramEvent

      3. StudentProgramFact
         
         

         Tip
         Thus to install the Early Warning System and Row-level security collections used by the Power BI Starter Kit v2, the admin user would run this command: Code Block .\EdFi.AnalyticsMiddleTier.exe --connectionString "..." --options EWS RLS

         
         

3. Avoid name overlaps

   1. Option 1: separate by "namespace" (schema).  Instead of having a single analytics  schema, we could create an analytics_core  schema and other schemas to match use cases:
      

      v1 Name	v2 Name
      analytics.​ContactPersonDimension	analytics_core.​ContactPersonDimension
      analytics.DateDimension	analytics_core.DateDimension
      analytics.Ews_SchoolRiskTrend	analytics_qews.SchoolRiskTrend
      analytics.Ews_StudentAttendanceTrend	analytics_qews.StudentAttendanceTrend
      analytics.Ews_StudentEnrolledSectionGrade	analytics_qews.StudentEnrolledSectionGrade
      analytics.Ews_StudentEnrolledSectionGradeTrend	analytics_qews.StudentEnrolledSectionGradeTrend
      analytics.Ews_StudentIndicators	analytics_qews.StudentIndicators
      analytics.Ews_StudentIndicatorsByGradingPeriod	analytics_qews.StudentIndicatorsByGradingPeriod
      analytics.Ews_UserSchoolAuthorization	analytics_qews.UserSchoolAuthorization
      analytics.GradingPeriodDimension	analytics_core.GradingPeriodDimension
      analytics.LocalEducationAgencyDimension	analytics_core.LocalEducationAgencyDimension
      analytics.MostRecentGradingPeriod	analytics_core.MostRecentGradingPeriod
      analytics.ProgramTypeDimension	analytics_program.ProgramTypeDimension
      analytics.SchoolDimension	analytics_core.SchoolDimension
      analytics.SchoolNetworkAssociationDimension	analytics_core.SchoolNetworkAssociationDimension
      analytics.StudentDataAuthorization	analytics_rls.StudentDataAuthorization
      analytics.StudentDimension	analytics_core.StudentDimension
      analytics.StudentEarlyWarningFact	analytics_ews.StudentEarlyWarningFact
      analytics.StudentProgramEvent	analytics_program.StudentProgramEvent
      analytics.StudentProgramFact	analytics_program.StudentProgramFact
      analytics.StudentSectionDimension	analytics_core.StudentSectionDimension
      analytics.StudentSectionGradeFact	analytics_ews.StudentSectionGradeFact
      analytics.UserAuthorization	analytics_rls.UserAuthorization
      analytics.UserDimension	analytics_rls.UserDimension
      analytics.UserStudentDataAuthorization	analytics_rls.UserStudentDataAuthorization

      
      

   2. Option 2: keep everything in a single schema, ensuring unique names, so that downstream data models (without namespaces/schemas) do not need to name their models differently than the views. Put use case name as object name prefix.

      v1 Name	v2 Name
      analytics.​ContactPersonDimension	analytics.​ContactPersonDim
      analytics.DateDimension	analytics.DateDim
      analytics.Ews_SchoolRiskTrend	analytics.qews_SchoolRiskTrend
      analytics.Ews_StudentAttendanceTrend	analytics.qews_StudentAttendanceTrend
      analytics.Ews_StudentEnrolledSectionGrade	analytics.qews_StudentEnrolledSectionGrade
      analytics.Ews_StudentEnrolledSectionGradeTrend	analytics.qews_StudentEnrolledSectionGradeTrend
      analytics.Ews_StudentIndicators	analytics.qews_StudentIndicators
      analytics.Ews_StudentIndicatorsByGradingPeriod	analytics.qews_StudentIndicatorsByGradingPeriod
      analytics.Ews_UserSchoolAuthorization	analytics.qews_UserSchoolAuthorization
      analytics.GradingPeriodDimension	analytics.GradingPeriodDim
      analytics.LocalEducationAgencyDimension	analytics.LocalEducationAgencyDim
      analytics.MostRecentGradingPeriod	analytics.MostRecentGradingPeriod
      analytics.ProgramTypeDimension	analytics.program_ProgramTypeDimension
      analytics.SchoolDimension	analytics.SchoolDim
      analytics.SchoolNetworkAssociationDimension	analytics.SchoolNetworkAssociationDim
      analytics.StudentDataAuthorization	analytics.rls_StudentDataAuthorization
      analytics.StudentDimension	analytics.StudentDim
      analytics.StudentEarlyWarningFact	analytics.ews_StudentEarlyWarningFact
      analytics.StudentProgramEvent	analytics.program_StudentProgramEvent
      analytics.StudentProgramFact	analytics.program_StudentProgramFact
      analytics.StudentSectionDimension	analytics.StudentSectionDim
      analytics.StudentSectionGradeFact	analytics.ews_StudentSectionGradeFact
      analytics.UserAuthorization	analytics.rls_UserAuthorization
      analytics.UserDimension	analytics.rls_UserDim
      analytics.UserStudentDataAuthorization	analytics.rls_UserStudentDataAuthorization

      
      

   3. Option 3: keep everything in single schema and don't force prefixing for use cases. Just have clear and unique names for views. Prefix on case-by-case basis.
      
      

      Note
      Leaning toward option (b). Additional benefit: helps the reader know where to look up additional information about use-case specific views, such as important usage notes.

      
      

Additional Views

Will not add any new views in the 2.0 release. New views can be added with 2.1, 2.2 etc. This 2.0 release is all about fixing architectural problems and setting the stage for broader adoption.

Documentation

End-Users

Decisions made in defining the Ed-Fi data model are allowing a great deal of flexibility in storing data, at the expense of un-intuitive complexity. Users of the Analytics Middle Tier need to know about the complexities in order to use this tool effectively. For example, if adopting option (b) to solve the Program view problem, there needs to be clear guidance to help the end-user.

Users also need to be made aware of potential data quality issues, for example with the Student Demographics. If a student is enrolled in two schools at a time, and they don't both enter the same demographic information (e.g. one accidentally clicks on the wrong gender, or one does not mark student as Hispanic/Latino), then how will the data analyst know and reconcile this? The Ed-Fi Alliance cannot prescribe an answer: it depends on the implementation.

For the (rare?) case that the console deployment tool does not work, provide guidance on directly accessing the views from the source code repository. Warn that scripts, when manually executed, need to be run in numeric order of file name, starting with the Core collection first and then installing other collections as needed.

Other issues will likely arise, so that end-user documentation will be an ongoing exercise.

Contributors

Documentation for contributors to the project will need to spell out how to contribute; how to create use-cases; naming conventions; when and how to place a new view into the Core collection.

Version 2 versus Version 3 support.