cQube Schemas
Details out the schemas of events and dimensions for all programs in which cQube Ed V 5.0 ingests data
cQube requires 2 types of schemas for the programs that need to be enabled:
- 1.
- 2.
There are 3 state programs available in cQube v5.0 for which the event schema needs to be ingested by the cQube adopter:
The schemas for national programs (UDISE, PGI, PM POSHAN, NAS, DIKSHA, NISHTHA) can be accessed here.
Note: There could be some common errors in the files which could lead to unsuccessful data ingestion into cQube. Look at these errors here and resolve them before ingesting data into cQube.
This program entails indicators to monitor compliance and performance of student attendance. Following are the reports within this program.
- 1.Student Attendance Compliance: This report shows the compliance of student attendance being marked in the schools. A school is counted as attendance-compliant when attendance of more than 50% of the students in the school is marked on a particular date.
- 2.Student Attendance Summary: This report shows the summary of students being present in the schools (out of the ones being marked).
Following schema will be required to enable the student attendance program:
If CSV is being ingested, the file name should be -> studentsattendance-event.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | date | string | Date when the data was recorded | YYYY-MM-DD format to be used |
2 | district_id | string | Unique ID of the district as per the dimension table | NA |
3 | block_id | string | Unique ID of the block as per the dimension table | NA |
4 | cluster_id | string | Unique ID of the cluster as per the dimension table | NA |
5 | school_id | string | Unique ID of the school as per the dimension table | NA |
6 | grade | string | Grade for which the data is being entered | NA |
7 | schoolcategory_id | string | Category_id which the school ID belongs to as per the dimension table | NA |
8 | gender | string | Gender for which data is being entered | NA |
9 | total_students | string | Total number of students | NA |
10 | students_attendance_marked | string | Total number of students whose attendance was marked | NA |
11 | students_marked_present | string | Total number of students who were present in the class | NA |
This program entails indicators to monitor compliance and performance of teacher attendance. Following are the reports within this program:
- Teacher Attendance Compliance: This report shows the compliance of attendance being marked by teachers.
- Teacher Attendance Summary: This report shows the summary of teachers being present in the school (out of the ones marking their attendance).
Following schema will be required to enable the teacher attendance program:
If CSV is being ingested, the file name should be -> teachersattendance-event.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | date | string | Date when the data was recorded | YYYY-MM-DD format to be used |
2 | district_id | string | Unique ID of the district as per the dimension table | NA |
3 | block_id | string | Unique ID of the block as per the dimension table | NA |
4 | cluster_id | string | Unique ID of the cluster as per the dimension table | NA |
5 | school_id | string | Unique ID of the school as per the dimension table | NA |
6 | schoolcategory_id | string | Category_id which the school ID belongs to as per the dimension table | NA |
7 | grade | string | Grade for which the data is being entered | NA |
8 | total_teachers | string | Total number of teachers | NA |
9 | teachers_attendance_marked | string | Total number of teachers whose attendance was marked | NA |
10 | teachers_marked_present | string | Total number of teachers who were present | NA |
This program entails indicators to monitor compliance of monthly review meetings being conducted at all levels.
Following schema will be required to enable the review meetings program:
- 1.For district review meetings:
If CSV is being ingested, the file name should be -> district-event.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | date | string | Date of the meeting conducted at the district | YYYY-MM-DD format to be used |
2 | district_id | string | Unique ID of the district as per the dimension table | NA |
3 | academicyear_id | string | Unique ID of the academic year as per the dimension table | NA |
4 | meeting_conducted | string | Whether the meeting is conducted or not | 1, 0 |
- 2.For block review meetings:
If CSV is being ingested, the file name should be -> block-event.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | date | string | Date of the meeting conducted at the block | YYYY-MM-DD format to be used |
2 | block_id | string | Unique ID of the block as per the dimension table | NA |
3 | district_id | string | Unique ID of the district as per the dimension table | NA |
4 | academicyear_id | string | Unique ID of the academic year as per the dimension table | NA |
5 | meeting_conducted | string | Whether meeting is conducted or not | 1, 0 |
- 3.For cluster review meetings:
If CSV is being ingested, the file name should be -> cluster-event.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | date | string | Date of the meeting conducted at the cluster | YYYY-MM-DD format to be used |
2 | cluster_id | string | Unique ID of the cluster as per the dimension table | NA |
3 | block_id | string | Unique ID of the block as per the dimension table | NA |
4 | district_id | string | Unique ID of the district as per the dimension table | NA |
5 | academicyear_id | string | Unique ID of the academic year as per the dimension table | NA |
6 | meeting_conducted | string | Whether meeting is conducted or not | 1, 0 |
For all the state programs, some dimensions (master data) will need to be ingested by the cQube adopter as well.
There are 8 dimensions for which the data needs to be ingested by the cQube adopter in order to get the state programs:
This is the data for the particular state.
If CSV is being ingested, the file name should be -> state-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | state_id | string | Unique ID in the table | NA |
2 | state_name | string | Name of the state | NA |
3 | latitude | string | Latitude of the state | NA |
4 | longitude | string | Longitude of the state | NA |
This is the data for all districts in the state.
If CSV is being ingested, the file name should be -> district-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | state_id | string | Unique ID in the table | NA |
2 | state_name | string | Name of the state | NA |
3 | district_id | string | Unique ID in the district | NA |
4 | district_name | string | Name of the district | NA |
5 | latitude | string | Latitude of the district | NA |
6 | longitude | string | Longitude of the district | NA |
This is the data for all blocks and districts in the state.
If CSV is being ingested, the file name should be -> block-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | district_id | string | Unique ID in the district | NA |
2 | district_name | string | Name of the district | NA |
3 | block_id | string | Unique ID in the block | NA |
4 | block_name | string | Name of the block | NA |
5 | latitude | string | Latitude of the block | NA |
6 | longitude | string | Longitude of the block | NA |
This is the data for all clusters, blocks and districts in the state.
If CSV is being ingested, the file name should be -> cluster-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | district_id | string | Unique ID in the district | NA |
2 | district_name | string | Name of the district | NA |
3 | block_id | string | Unique ID in the block | NA |
4 | block_name | string | Name of the block | NA |
5 | cluster_id | string | Unique ID in the cluster | NA |
6 | cluster_name | string | Name of the cluster | NA |
7 | latitude | string | Latitude of the cluster | NA |
8 | longitude | string | Longitude of the cluster | NA |
This is the data for all schools, clusters, blocks and districts in the state.
If CSV is being ingested, the file name should be -> school-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | district_id | string | Unique ID in the district | NA |
2 | district_name | string | Name of the district | NA |
3 | block_id | string | Unique ID in the block | NA |
4 | block_name | string | Name of the block | NA |
5 | cluster_id | string | Unique ID in the cluster | NA |
6 | cluster_name | string | Name of the cluster | NA |
7 | school_id | string | Unique ID in the school | NA |
8 | school_name | string | Name of the school | NA |
9 | schoolcategory_id | string | ID of the school category for the respective school | NA |
10 | grade_id | string | ID of the grade present in the school | NA |
11 | latitude | string | Latitude of the school | NA |
12 | longitude | string | Longitude of the school | NA |
This is the data for categories of schools present in the state (Eg: Primary, Upper Primary, Secondary, Senior Secondary or any other kind of category).
If CSV is being ingested, the file name should be -> schoolcategory-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | schoolcategory_id | string | Unique ID of the school category | NA |
2 | schoolcategory_name | string | Name of the school category | NA |
3 | grades | string | Array of grades available in the respective school category | NA |
This is the data for grades present in schools in the state (Eg: 1, 2, 3, 4, 5 or any other grades present in the state).
If CSV is being ingested, the file name should be -> grade-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | grade_id | string | Unique ID of the grade | NA |
4 | grade | string | Grade as per what is followed in the state | NA |
Note: The grade column will consist of the master values from the state data, DIKSHA Data and NAS Data.
This is the master data for genders present in the state (Eg: Male, Female, Other).
If CSV is being ingested, the file name should be -> gender-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | gender_id | string | Unique ID of the gender | NA |
2 | gender | string | Name of the gender | NA |
This is the data for subjects being taught in the state (Eg: Mathematics, Science, English etc).
If CSV is being ingested, the file name should be -> subject-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | subject_id | string | Unique ID of the subject | NA |
4 | subject | string | Subject as per what is taught in the state | NA |
Note: The subject column will consist of the master values from the DIKSHA Data and NAS Data.
This is the data for mediums being taught in the state (Eg: English, Hindi etc).
If CSV is being ingested, the file name should be -> medium-dimension.data.csv
# | Column Name | Data Type | Description | Validation |
---|---|---|---|---|
1 | medium_id | string | Unique ID of the medium | NA |
4 | medium | string | Medium as per what is taught in the state | NA |
Note: The medium column will consist of the master values from the DIKSHA Data.