{ "_about": "Shape map for every domain in mcps_consolidated.parquet (the full-fidelity EAV warehouse \u2014 audit layer). 'eav' domains melt one source record into sibling rows sharing record_id and need the pivot in example_sql; 'flat' domains are already one fact per row. dimension_metrics carry labels in value_text (value is null); fact_metrics map to their share of rows with a numeric value (the rest are suppressed or text). Classification is mechanical, so a numeric-valued dimension (e.g. mcap's 'Grade') lands under fact_metrics \u2014 pivot it like any other. dimension_year_gaps is domain-wide; gaps scoped to one level/source (e.g. mcap 'Student Group' missing from 2019 DISTRICT records only \u2014 QUERYING.md recipe 1) do not appear here. Generated mechanically from the warehouse \u2014 regenerate, never hand-edit.", "source": "/data/mcps_consolidated.parquet", "n_domains": 43, "eav_domains": [ "accountability", "attendance", "climate_survey", "college_readiness", "directory", "educators", "english_proficiency", "enrollment", "enrollment_by_grade", "finance", "graduation", "kindergarten_readiness", "mcap", "mcap_alt", "mcap_participation", "mobility", "special_services", "staff_roster", "swd_participation" ], "flat_domains": [ "algebra2", "ap_ib", "ccr", "class_size", "dibels_lectura", "discipline_incidents", "drills", "enrollment_subgroup", "lre", "map_r", "msde_performance", "on_track_grad", "programs", "safety_events", "sat", "sped_ap_ib", "sped_diploma", "sped_enrollment_subgroup", "sped_lre", "sped_msde_performance", "sped_sat", "sped_staff_services", "staff_positions", "suspension_rate" ], "domains": { "accountability": { "kind": "eav", "row_count": 90194, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Accountability Span", "Additional Targeted Support and Improvement Schools", "Annual Target Met Flag", "Comprehensive Support and Improvement Schools", "Grade Span", "Improvement Flag", "Indicator Name", "Measure Name", "NonAssessed-Paired School", "SDS Span Code", "School Type", "Student Group", "Subject Title", "Summary Group Title" ], "fact_metrics": { "Indicator Earned Pct": 1.0, "Indicator Earned Points": 1.0, "Indicator Possible Points": 1.0, "Measure Earned Points": 1.0, "Measure Possible Points": 1.0, "Measure Result": 0.996, "Percentile Rank Elementary": 0.652, "Percentile Rank High": 0.13, "Percentile Rank Middle": 0.208, "Rating": 1.0, "School Identification Year": 1.0, "Total Points Earned Percentage": 0.985 }, "n_shapes": 7, "dimension_year_gaps": { "Additional Targeted Support and Improvement Schools": [ 2022, 2024, 2025 ], "Comprehensive Support and Improvement Schools": [ 2022, 2024, 2025 ], "Grade Span": [ 2019 ], "Measure Name": [ 2019 ], "NonAssessed-Paired School": [ 2019, 2022, 2023 ], "Subject Title": [ 2019 ], "Summary Group Title": [ 2019 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Indicator Name' THEN value_text END) AS indicator_name,\n max(CASE WHEN metric='Grade Span' THEN value_text END) AS grade_span,\n max(CASE WHEN metric='Measure Name' THEN value_text END) AS measure_name,\n max(CASE WHEN metric='Subject Title' THEN value_text END) AS subject_title,\n max(CASE WHEN metric='Measure Earned Points' THEN value END) AS measure_earned_points,\n max(CASE WHEN metric='Measure Possible Points' THEN value END) AS measure_possible_points,\n max(CASE WHEN metric='Measure Result' THEN value END) AS measure_result,\n max(CASE WHEN metric='Percentile Rank Elementary' THEN value END) AS percentile_rank_elementary\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='accountability'\nGROUP BY record_id, year" }, "algebra2": { "kind": "flat", "row_count": 1143, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent_met": 0.951 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='algebra2' AND metric='percent_met'" }, "ap_ib": { "kind": "flat", "row_count": 102, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "graduates": 0.824, "met_ap3_ib4": 0.824, "percent_met": 0.824 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='ap_ib' AND metric='graduates'" }, "attendance": { "kind": "eav", "row_count": 15888, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "School Type" ], "fact_metrics": { "Attend Rate Pct": 0.703, "Chronic Absentee Cnt": 0.952, "Chronic Absentee Denom": 0.952, "Chronic Absentee Pct": 0.952, "Days Attended Cnt": 0.703, "Days Member Cnt": 0.703, "Fewer 5 Cnt": 0.977, "Fewer 5 Pct": 0.977, "More 20 Cnt": 0.878, "More 20 Pct": 0.878, "More 90 Member Cnt": 0.993 }, "n_shapes": 1, "example_sql": "SELECT year,\n max(CASE WHEN metric='School Type' THEN value_text END) AS school_type,\n max(CASE WHEN metric='Attend Rate Pct' THEN value END) AS attend_rate_pct,\n max(CASE WHEN metric='Chronic Absentee Cnt' THEN value END) AS chronic_absentee_cnt,\n max(CASE WHEN metric='Chronic Absentee Denom' THEN value END) AS chronic_absentee_denom,\n max(CASE WHEN metric='Chronic Absentee Pct' THEN value END) AS chronic_absentee_pct\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='attendance'\nGROUP BY record_id, year" }, "ccr": { "kind": "flat", "row_count": 2116, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent_met": 0.716 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='ccr' AND metric='percent_met'" }, "class_size": { "kind": "flat", "row_count": 3175, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "avg_class_size": 0.506 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='class_size' AND metric='avg_class_size'" }, "climate_survey": { "kind": "eav", "row_count": 92987, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Accountability Span", "Domain Name", "SDS Span Code", "Topic Name" ], "fact_metrics": { "Educator Count": 1.0, "Score Average": 1.0, "Student Count": 1.0 }, "n_shapes": 2, "example_sql": "SELECT year,\n max(CASE WHEN metric='Domain Name' THEN value_text END) AS domain_name,\n max(CASE WHEN metric='SDS Span Code' THEN value_text END) AS sds_span_code,\n max(CASE WHEN metric='Topic Name' THEN value_text END) AS topic_name,\n max(CASE WHEN metric='Accountability Span' THEN value_text END) AS accountability_span,\n max(CASE WHEN metric='Score Average' THEN value END) AS score_average,\n max(CASE WHEN metric='Educator Count' THEN value END) AS educator_count,\n max(CASE WHEN metric='Student Count' THEN value END) AS student_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='climate_survey'\nGROUP BY record_id, year" }, "college_readiness": { "kind": "eav", "row_count": 17059, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "AP Subject Group", "Collection Type Title", "Student Group" ], "fact_metrics": { "% Exams with Grades 3-5": 0.836, "Composite Score Mean": 0.872, "English Score Mean": 0.872, "Enrolled Count": 0.992, "Enrolled Percentage": 0.992, "Evidence-Based Reading and Writing Score Mean": 0.939, "Exams per Student": 0.887, "Exams with Grades 3-5": 0.836, "Math Score Mean": 0.907, "Mean Grade per Exam": 0.887, "Number of Exams": 0.887, "Reading Score Mean": 0.872, "Science Score Mean": 0.872, "Student Count": 0.992, "Students Tested": 0.891, "Total Score Mean": 0.939 }, "n_shapes": 7, "dimension_year_gaps": { "Collection Type Title": [ 2025 ], "Student Group": [ 2021 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='AP Subject Group' THEN value_text END) AS ap_subject_group,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Collection Type Title' THEN value_text END) AS collection_type_title,\n max(CASE WHEN metric='Students Tested' THEN value END) AS students_tested,\n max(CASE WHEN metric='% Exams with Grades 3-5' THEN value END) AS exams_with_grades_3_5,\n max(CASE WHEN metric='Exams per Student' THEN value END) AS exams_per_student,\n max(CASE WHEN metric='Exams with Grades 3-5' THEN value END) AS exams_with_grades_3_5\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='college_readiness'\nGROUP BY record_id, year" }, "dibels_lectura": { "kind": "flat", "row_count": 4837, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent_met": 0.955 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='dibels_lectura' AND metric='percent_met'" }, "directory": { "kind": "eav", "row_count": 24675, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Address", "City", "Grade Span", "School Type", "State" ], "fact_metrics": { "Comparison Group Number": 1.0, "Comparison Overall": 1.0, "Comparison_Academic Achievement": 1.0, "Comparison_Academic Progress": 0.862, "Comparison_English Language Proficiency": 0.966, "Comparison_Graduation": 0.125, "Comparison_Readiness Postsecondary": 0.132, "Comparison_School Quality Student Success": 1.0, "NCES Number": 1.0, "Phone": 1.0, "School Overall": 1.0, "School Star Rating": 1.0, "School_Academic Achievement": 1.0, "School_Academic Progress": 0.862, "School_English Language Proficiency": 0.966, "School_Graduation": 0.125, "School_Readiness Postsecondary": 0.132, "School_School Quality Student Success": 1.0, "Zip": 1.0 }, "n_shapes": 5, "dimension_year_gaps": { "Grade Span": [ 2019, 2021 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Grade Span' THEN value_text END) AS grade_span,\n max(CASE WHEN metric='Address' THEN value_text END) AS address,\n max(CASE WHEN metric='City' THEN value_text END) AS city,\n max(CASE WHEN metric='State' THEN value_text END) AS state,\n max(CASE WHEN metric='Phone' THEN value END) AS phone,\n max(CASE WHEN metric='Zip' THEN value END) AS zip,\n max(CASE WHEN metric='NCES Number' THEN value END) AS nces_number,\n max(CASE WHEN metric='Comparison Group Number' THEN value END) AS comparison_group_number\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='directory'\nGROUP BY record_id, year" }, "discipline_incidents": { "kind": "flat", "row_count": 62965, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "group", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='discipline_incidents' AND metric='count'" }, "drills": { "kind": "flat", "row_count": 10312, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "group", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='drills' AND metric='count'" }, "educators": { "kind": "eav", "row_count": 36311, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Poverty Level", "Students of Color Percent" ], "fact_metrics": { "Educators Count": 1.0, "Inexperienced Educators Count": 1.0, "Inexperienced Educators Pct": 1.0, "Inexperienced Teachers Count": 1.0, "Inexperienced Teachers Pct": 1.0, "Out-of-field Teachers Count": 1.0, "Out-of-field Teachers Pct": 1.0, "Teachers Count": 1.0, "Teachers Teaching with Emergency or Provisional Credentials Count": 1.0, "Teachers Teaching with Emergency or Provisional Credentials Pct": 1.0 }, "n_shapes": 2, "example_sql": "SELECT year,\n max(CASE WHEN metric='Poverty Level' THEN value_text END) AS poverty_level,\n max(CASE WHEN metric='Students of Color Percent' THEN value_text END) AS students_of_color_percent,\n max(CASE WHEN metric='Educators Count' THEN value END) AS educators_count,\n max(CASE WHEN metric='Inexperienced Educators Count' THEN value END) AS inexperienced_educators_count,\n max(CASE WHEN metric='Inexperienced Educators Pct' THEN value END) AS inexperienced_educators_pct,\n max(CASE WHEN metric='Inexperienced Teachers Count' THEN value END) AS inexperienced_teachers_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='educators'\nGROUP BY record_id, year" }, "english_proficiency": { "kind": "eav", "row_count": 9930, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "School Name", "Student Group" ], "fact_metrics": { "Proficient Count": 0.469, "Proficient Percent": 0.469, "Tested Count": 0.732 }, "n_shapes": 2, "dimension_year_gaps": { "School Name": [ 2019 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='School Name' THEN value_text END) AS school_name,\n max(CASE WHEN metric='Proficient Count' THEN value END) AS proficient_count,\n max(CASE WHEN metric='Proficient Percent' THEN value END) AS proficient_percent,\n max(CASE WHEN metric='Tested Count' THEN value END) AS tested_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='english_proficiency'\nGROUP BY record_id, year" }, "enrollment": { "kind": "eav", "row_count": 20466, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Grade", "Race" ], "fact_metrics": { "Enrolled Count": 0.844 }, "n_shapes": 2, "dimension_year_gaps": { "Grade": [ 2021, 2022, 2023, 2024, 2025, 2026 ], "Race": [ 2019 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Race' THEN value_text END) AS race,\n max(CASE WHEN metric='Grade' THEN value_text END) AS grade,\n max(CASE WHEN metric='Enrolled Count' THEN value END) AS enrolled_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='enrollment'\nGROUP BY record_id, year" }, "enrollment_by_grade": { "kind": "eav", "row_count": 24183, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "school_type", "grade", "category", "year" ], "dimension_metrics": [ "Grade" ], "fact_metrics": { "Enrolled Count": 0.967, "count": 1.0, "percent": 1.0 }, "n_shapes": 1, "example_sql": "SELECT year,\n max(CASE WHEN metric='Grade' THEN value_text END) AS grade,\n max(CASE WHEN metric='Enrolled Count' THEN value END) AS enrolled_count,\n max(CASE WHEN metric='count' THEN value END) AS count,\n max(CASE WHEN metric='percent' THEN value END) AS percent\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='enrollment_by_grade'\nGROUP BY record_id, year" }, "enrollment_subgroup": { "kind": "flat", "row_count": 28845, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent": 0.422 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='enrollment_subgroup' AND metric='percent'" }, "finance": { "kind": "eav", "row_count": 3758, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "School Comment" ], "fact_metrics": { "Federal Amount": 1.0, "State/Local Amount": 1.0, "Total Amount": 1.0 }, "n_shapes": 2, "example_sql": "SELECT year,\n max(CASE WHEN metric='School Comment' THEN value_text END) AS school_comment,\n max(CASE WHEN metric='Federal Amount' THEN value END) AS federal_amount,\n max(CASE WHEN metric='State/Local Amount' THEN value END) AS state_local_amount,\n max(CASE WHEN metric='Total Amount' THEN value END) AS total_amount\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='finance'\nGROUP BY record_id, year" }, "graduation": { "kind": "eav", "row_count": 11452, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Cohort" ], "fact_metrics": { "Adjusted Cohort Count": 0.9, "CTE Requirements Count": 0.137, "CTE Requirements Pct": 0.137, "Certificate Count": 0.016, "Certificate Pct": 0.016, "Diploma Count": 0.016, "Diploma Pct": 0.016, "Diplomas Earned": 0.606, "Dropout Rate": 0.291, "Dropout Rate Denom": 0.291, "Dropout Rate Numer": 0.291, "Dropouts": 0.668, "Four Year Adjusted Cohort Dropout Rate": 0.668, "Grad Rate": 0.606, "Grade": 1.0, "Met Rigorous Indicator Count": 0.857, "Met Rigorous Indicator Pct": 0.857, "Non-Promotion Rate": 0.731, "Number Not Promoted": 0.731, "Number Promoted": 0.731, "Promotion Rate": 0.731, "Rigorous Foreign Language Count": 0.89, "Rigorous Foreign Language Pct": 0.89, "Rigorous GPA Count": 0.912, "Rigorous GPA Pct": 0.912, "Rigorous Math Count": 0.885, "Rigorous Math Pct": 0.885, "Rigorous SAT OR ACT Count": 0.808, "Rigorous SAT OR ACT Pct": 0.808, "Rigorous Science Count": 0.863, "Rigorous Science Pct": 0.863, "Rigorous Tech Ed Count": 0.0, "Rigorous Tech Ed Pct": 0.0, "Total HS Completers": 0.918, "Total Students Enrolled": 0.731, "USM Requirements Count": 0.885, "USM Requirements Pct": 0.885, "USM and CTE Requirements Count": 0.648, "USM and CTE Requirements Pct": 0.648 }, "n_shapes": 5, "example_sql": "SELECT year,\n max(CASE WHEN metric='Cohort' THEN value_text END) AS cohort,\n max(CASE WHEN metric='Grade' THEN value END) AS grade,\n max(CASE WHEN metric='Non-Promotion Rate' THEN value END) AS non_promotion_rate,\n max(CASE WHEN metric='Number Not Promoted' THEN value END) AS number_not_promoted,\n max(CASE WHEN metric='Number Promoted' THEN value END) AS number_promoted\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='graduation'\nGROUP BY record_id, year" }, "kindergarten_readiness": { "kind": "eav", "row_count": 5333, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2022, 2023, 2024, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Assessment", "Student Group", "Test Type", "Type" ], "fact_metrics": { "Beginning Pct": 0.847, "Demonstrating Count": 0.798, "Demonstrating Pct": 0.798, "Developing Pct": 0.816, "Established Pct": 0.858, "High Risk Pct": 0.404, "Low Risk Pct": 0.954, "Participant Count": 0.954, "Some Risk Pct": 0.921, "Tested Count": 0.83 }, "n_shapes": 4, "dimension_year_gaps": { "Assessment": [ 2023, 2024, 2026 ], "Test Type": [ 2022, 2023, 2024 ], "Type": [ 2022, 2023, 2024 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Test Type' THEN value_text END) AS test_type,\n max(CASE WHEN metric='Type' THEN value_text END) AS type,\n max(CASE WHEN metric='Assessment' THEN value_text END) AS assessment,\n max(CASE WHEN metric='Tested Count' THEN value END) AS tested_count,\n max(CASE WHEN metric='Demonstrating Count' THEN value END) AS demonstrating_count,\n max(CASE WHEN metric='Demonstrating Pct' THEN value END) AS demonstrating_pct,\n max(CASE WHEN metric='Beginning Pct' THEN value END) AS beginning_pct\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='kindergarten_readiness'\nGROUP BY record_id, year" }, "lre": { "kind": "flat", "row_count": 2552, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent": 0.606 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='lre' AND metric='percent'" }, "map_r": { "kind": "flat", "row_count": 4841, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent_met": 0.977 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='map_r' AND metric='percent_met'" }, "mcap": { "kind": "eav", "row_count": 1303838, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "grade", "category", "year" ], "dimension_metrics": [ "Assessment", "Student Group" ], "fact_metrics": { "Grade": 1.0, "Level 1 Pct": 0.474, "Level 2 Pct": 0.621, "Level 3 Pct": 0.541, "Level 4 Pct": 0.207, "Level 5 Pct": 0.533, "Pass Count": 0.938, "Pass Pct": 0.938, "Proficient Count": 0.663, "Proficient Pct": 0.527, "Tested Count": 0.69, "count": 1.0, "percent_met": 0.85 }, "n_shapes": 10, "example_sql": "SELECT year,\n max(CASE WHEN metric='Assessment' THEN value_text END) AS assessment,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Proficient Pct' THEN value END) AS proficient_pct,\n max(CASE WHEN metric='Level 2 Pct' THEN value END) AS level_2_pct,\n max(CASE WHEN metric='Level 3 Pct' THEN value END) AS level_3_pct,\n max(CASE WHEN metric='Level 1 Pct' THEN value END) AS level_1_pct\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='mcap'\nGROUP BY record_id, year" }, "mcap_alt": { "kind": "eav", "row_count": 40797, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Assessment", "Student Group" ], "fact_metrics": { "Grade": 0.143, "Level 1 Pct": 0.21, "Level 2 Pct": 0.206, "Level 3 Pct": 0.128, "Level 4 Pct": 0.064, "Proficient Count": 0.181, "Proficient Pct": 0.181, "Tested Count": 0.213 }, "n_shapes": 5, "dimension_year_gaps": { "Student Group": [ 2019 ] }, "gap_note": "these dimension metrics are absent in the listed years \u2014 coalesce() them when pivoting or those vintages silently drop (the 2019 mcap 'Student Group' trap, QUERYING.md recipe 1)", "example_sql": "SELECT year,\n max(CASE WHEN metric='Assessment' THEN value_text END) AS assessment,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Level 1 Pct' THEN value END) AS level_1_pct,\n max(CASE WHEN metric='Level 2 Pct' THEN value END) AS level_2_pct,\n max(CASE WHEN metric='Level 3 Pct' THEN value END) AS level_3_pct,\n max(CASE WHEN metric='Proficient Count' THEN value END) AS proficient_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='mcap_alt'\nGROUP BY record_id, year" }, "mcap_participation": { "kind": "eav", "row_count": 356929, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Assessment", "Student Group" ], "fact_metrics": { "Alternate Assessment Participant Pct": 0.087, "Count of recently arrived English learners exempted from the ELA assessment": 0.027, "General Assessment Participant Pct": 0.188, "Total Non-Participant Pct": 0.099, "Total Participant Pct": 0.099 }, "n_shapes": 2, "example_sql": "SELECT year,\n max(CASE WHEN metric='Assessment' THEN value_text END) AS assessment,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Alternate Assessment Participant Pct' THEN value END) AS alternate_assessment_participant_pct,\n max(CASE WHEN metric='General Assessment Participant Pct' THEN value END) AS general_assessment_participant_pct,\n max(CASE WHEN metric='Total Non-Participant Pct' THEN value END) AS total_non_participant_pct,\n max(CASE WHEN metric='Total Participant Pct' THEN value END) AS total_participant_pct\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='mcap_participation'\nGROUP BY record_id, year" }, "mobility": { "kind": "eav", "row_count": 10592, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "School Type" ], "fact_metrics": { "Average Daily Member Count": 0.826, "Entrants Count": 0.311, "Entrants Rate": 0.311, "Mobility Count": 0.824, "Mobility Rate": 0.824, "Withdrawals Count": 0.561, "Withdrawals Rate": 0.561 }, "n_shapes": 1, "example_sql": "SELECT year,\n max(CASE WHEN metric='School Type' THEN value_text END) AS school_type,\n max(CASE WHEN metric='Average Daily Member Count' THEN value END) AS average_daily_member_count,\n max(CASE WHEN metric='Entrants Count' THEN value END) AS entrants_count,\n max(CASE WHEN metric='Entrants Rate' THEN value END) AS entrants_rate,\n max(CASE WHEN metric='Mobility Count' THEN value END) AS mobility_count\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='mobility'\nGROUP BY record_id, year" }, "msde_performance": { "kind": "flat", "row_count": 5223, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "rate": 0.505 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='msde_performance' AND metric='rate'" }, "on_track_grad": { "kind": "flat", "row_count": 1236, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent_met": 0.855 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='on_track_grad' AND metric='percent_met'" }, "programs": { "kind": "flat", "row_count": 5437, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "school_type", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "program_offered": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='programs' AND metric='program_offered'" }, "safety_events": { "kind": "flat", "row_count": 51114, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "group", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='safety_events' AND metric='count'" }, "sat": { "kind": "flat", "row_count": 341, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "pct_tested": 1.0, "score": 0.914 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sat' AND metric='score'" }, "special_services": { "kind": "eav", "row_count": 36546, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2021, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "School Type" ], "fact_metrics": { "Ada504 Cnt": 0.269, "Ada504 Pct": 0.269, "Economically Disadvantaged Cnt": 0.889, "Economically Disadvantaged Pct": 0.889, "FARMS Cnt": 0.937, "FARMS Pct": 0.937, "Foster Care Cnt": 0.0, "Foster Care Pct": 0.0, "Gifted Talented Cnt": 0.721, "Gifted Talented Pct": 0.721, "Homeless Cnt": 0.001, "Homeless Pct": 0.001, "Migrant Cnt": 0.0, "Migrant Pct": 0.0, "Military Connected Cnt": 0.002, "Military Connected Pct": 0.002, "Multilingual Learner Cnt": 0.879, "Multilingual Learner Pct": 0.879, "Students with Disabilities Cnt": 0.93, "Students with Disabilities Pct": 0.93, "Title1 Cnt": 0.012, "Title1 Pct": 0.012, "Total Student Cnt": 0.972 }, "n_shapes": 2, "example_sql": "SELECT year,\n max(CASE WHEN metric='School Type' THEN value_text END) AS school_type,\n max(CASE WHEN metric='Ada504 Cnt' THEN value END) AS ada504_cnt,\n max(CASE WHEN metric='Ada504 Pct' THEN value END) AS ada504_pct,\n max(CASE WHEN metric='Economically Disadvantaged Cnt' THEN value END) AS economically_disadvantaged_cnt,\n max(CASE WHEN metric='Economically Disadvantaged Pct' THEN value END) AS economically_disadvantaged_pct\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='special_services'\nGROUP BY record_id, year" }, "sped_ap_ib": { "kind": "flat", "row_count": 234, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024 ], "id_columns": [ "source", "dataset", "year" ], "dimension_metrics": [], "fact_metrics": { "graduates": 1.0, "met_ap3_ib4": 1.0, "percent_met": 0.756 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_ap_ib' AND metric='graduates'" }, "sped_diploma": { "kind": "flat", "row_count": 500, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent": 0.787 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_diploma' AND metric='percent'" }, "sped_enrollment_subgroup": { "kind": "flat", "row_count": 22477, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent": 0.683 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_enrollment_subgroup' AND metric='percent'" }, "sped_lre": { "kind": "flat", "row_count": 2552, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "subgroup", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "percent": 0.551 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_lre' AND metric='percent'" }, "sped_msde_performance": { "kind": "flat", "row_count": 4163, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "rate": 0.531 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_msde_performance' AND metric='rate'" }, "sped_sat": { "kind": "flat", "row_count": 324, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "category", "year" ], "dimension_metrics": [], "fact_metrics": { "pct_tested": 1.0, "score": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_sat' AND metric='score'" }, "sped_staff_services": { "kind": "flat", "row_count": 1191, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023 ], "id_columns": [ "source", "dataset", "level", "school_type", "group", "subgroup", "year" ], "dimension_metrics": [ "service_offered" ], "fact_metrics": { "fte": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='sped_staff_services' AND metric='fte'" }, "staff_positions": { "kind": "flat", "row_count": 23859, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "staff_count": 1.0 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='staff_positions' AND metric='staff_count'" }, "staff_roster": { "kind": "eav", "row_count": 594181, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2019, 2022, 2023, 2024, 2025, 2026 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "1st Subject", "2nd Subject", "3rd Subject", "4th Subject", "5th Subject", "6th Subject", "Degree", "Location", "Position", "Type of Experience (New Hires)", "Where Previously Employed (New Hires)" ], "fact_metrics": { "Years Experience": 1.0 }, "n_shapes": 22, "example_sql": "SELECT year,\n max(CASE WHEN metric='Location' THEN value_text END) AS location,\n max(CASE WHEN metric='Position' THEN value_text END) AS position,\n max(CASE WHEN metric='Degree' THEN value_text END) AS degree,\n max(CASE WHEN metric='1st Subject' THEN value_text END) AS 1st_subject,\n max(CASE WHEN metric='Years Experience' THEN value END) AS years_experience\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='staff_roster'\nGROUP BY record_id, year" }, "suspension_rate": { "kind": "flat", "row_count": 23702, "grain": "one row per school x year x metric \u2014 filter, no pivot needed", "years": [ 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "school_type", "population", "group", "subgroup", "year" ], "dimension_metrics": [], "fact_metrics": { "count": 1.0, "pct_suspended": 0.635 }, "example_sql": "-- no pivot needed: each row is already one fact\nSELECT school_code_str, year, population, subgroup, category, value\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='suspension_rate' AND metric='pct_suspended'" }, "swd_participation": { "kind": "eav", "row_count": 12070, "grain": "one source record melts into sibling rows sharing record_id \u2014 pivot with GROUP BY record_id", "years": [ 2022, 2023, 2024, 2025 ], "id_columns": [ "source", "dataset", "level", "category", "year" ], "dimension_metrics": [ "Assessment", "Student Group" ], "fact_metrics": { "Count Participating in Regular Assessments": 0.891, "Count Provided with Accommodations": 0.633, "Percent Provided Accommodations": 0.633 }, "n_shapes": 1, "example_sql": "SELECT year,\n max(CASE WHEN metric='Assessment' THEN value_text END) AS assessment,\n max(CASE WHEN metric='Student Group' THEN value_text END) AS student_group,\n max(CASE WHEN metric='Count Participating in Regular Assessments' THEN value END) AS count_participating_in_regular_assessments,\n max(CASE WHEN metric='Count Provided with Accommodations' THEN value END) AS count_provided_with_accommodations,\n max(CASE WHEN metric='Percent Provided Accommodations' THEN value END) AS percent_provided_accommodations\nFROM read_parquet('https://mocoparents.org/data/mcps_consolidated.parquet')\nWHERE domain='swd_participation'\nGROUP BY record_id, year" } } }