{"id":328,"date":"2026-08-25T06:25:45","date_gmt":"2026-08-25T06:25:45","guid":{"rendered":"https:\/\/tobolist.com\/?page_id=328"},"modified":"2026-08-30T19:34:29","modified_gmt":"2026-08-30T19:34:29","slug":"case-industrial","status":"publish","type":"page","link":"https:\/\/tobolist.com\/es\/case-industrial\/","title":{"rendered":"Caso Industrial"},"content":{"rendered":"\n<div class=\"tb-next tb-page\">\n\n  <!-- ============================================================\n       BARRA DE MARCA\n       ============================================================ -->\n  <nav class=\"tb-nav\" aria-label=\"Main\">\n    <a class=\"tb-nav-brand\" href=\"\/\" aria-label=\"Tobolist - home\">\n      <svg viewBox=\"0 0 402 58\" role=\"img\" aria-hidden=\"true\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n      <path fill=\"currentColor\" d=\"M 203.97 1.55 C 204.73 3.77 205.5 5.96 206.26 8.13 C 207.02 10.3 207.76 12.55 208.47 14.88 C 208.06 14.98 207.68 15.11 207.32 15.26 C 207.02 15.42 206.66 15.55 206.26 15.65 C 205.29 13.48 204.35 11.72 203.43 10.38 C 202.57 8.99 201.7 7.88 200.84 7.05 C 199.72 6.02 198.4 5.32 196.87 4.96 C 195.34 4.6 193.84 4.41 192.37 4.41 C 189.42 4.41 187.48 4.65 186.57 5.11 C 185.55 5.58 185.04 6.51 185.04 7.9 L 185.04 49.04 C 185.04 50.54 185.6 51.59 186.72 52.21 C 187.74 52.78 189.95 53.07 193.36 53.07 L 193.36 55.55 L 171.84 55.55 L 171.84 53.07 C 174.89 53.07 176.95 52.81 178.02 52.29 C 179.45 51.62 180.16 50.54 180.16 49.04 L 180.16 7.28 C 180.16 6.2 179.75 5.45 178.94 5.04 C 178.18 4.62 176.62 4.41 174.28 4.41 C 172.5 4.41 170.67 4.57 168.79 4.88 C 166.96 5.19 165.38 5.91 164.05 7.05 C 163.5 7.52 162.96 8.08 162.45 8.75 C 161.95 9.38 161.36 10.2 160.7 11.23 C 160.29 11.91 159.91 12.57 159.55 13.25 C 159.2 13.87 158.82 14.54 158.41 15.26 C 158.11 15.11 157.8 14.98 157.49 14.88 C 157.24 14.72 156.96 14.56 156.65 14.41 C 157.42 12.24 158.18 10.1 158.94 7.98 C 159.71 5.86 160.47 3.72 161.23 1.55 Z M 203.97 1.55 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 234.5 37.96 C 234.5 43.74 232.88 48.37 229.62 51.83 C 226.52 55.29 222.55 57.02 217.71 57.02 C 212.83 57.02 208.84 55.29 205.73 51.83 C 202.48 48.37 200.85 43.74 200.85 37.96 C 200.85 32.18 202.48 27.55 205.73 24.09 C 208.84 20.63 212.83 18.9 217.71 18.9 C 222.55 18.9 226.52 20.63 229.62 24.09 C 232.88 27.55 234.5 32.18 234.5 37.96 Z M 229.31 37.96 C 229.31 33.21 228.19 29.31 225.95 26.26 C 223.87 23.21 221.12 21.69 217.71 21.69 C 214.36 21.69 211.55 23.21 209.32 26.26 C 207.13 29.31 206.04 33.21 206.04 37.96 C 206.04 42.66 207.11 46.56 209.24 49.66 C 211.43 52.65 214.25 54.15 217.71 54.15 C 221.02 54.15 223.77 52.65 225.95 49.66 C 228.19 46.61 229.31 42.71 229.31 37.96 Z M 229.31 37.96 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 269.72 37.8 C 269.72 43.43 268.14 48 264.99 51.52 C 263.52 53.17 261.89 54.46 260.11 55.39 C 258.38 56.37 256.37 56.86 254.08 56.86 C 249.65 56.86 246.04 55.52 243.24 52.83 C 242.83 53.35 242.45 53.84 242.09 54.3 C 241.79 54.82 241.43 55.34 241.03 55.86 L 239.27 55.86 L 239.27 9.3 C 239.27 8.16 239.02 7.36 238.51 6.89 C 238 6.38 236.78 6.12 234.84 6.12 C 234.79 5.86 234.74 5.58 234.69 5.27 C 234.64 4.96 234.59 4.68 234.54 4.41 C 236.17 3.8 237.75 3.18 239.27 2.55 C 240.8 1.94 242.35 1.32 243.93 0.7 L 243.93 22 C 246.42 19.93 249.8 18.9 254.08 18.9 C 258.76 18.9 262.52 20.58 265.37 23.94 C 268.27 27.35 269.72 31.97 269.72 37.8 Z M 264.61 37.88 C 264.61 33.18 263.52 29.39 261.33 26.5 C 260.26 25.1 258.96 24.02 257.43 23.24 C 255.96 22.46 254.28 22.08 252.4 22.08 C 249.14 22.08 246.32 23.27 243.93 25.64 L 243.93 49.81 C 246.27 52.34 249.09 53.61 252.4 53.61 C 255.8 53.61 258.71 52.09 261.1 49.04 C 263.44 46.2 264.61 42.48 264.61 37.88 Z M 264.61 37.88 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 308.05 37.96 C 308.05 43.74 306.42 48.37 303.16 51.83 C 300.06 55.29 296.09 57.02 291.26 57.02 C 286.37 57.02 282.38 55.29 279.27 51.83 C 276.02 48.37 274.39 43.74 274.39 37.96 C 274.39 32.18 276.02 27.55 279.27 24.09 C 282.38 20.63 286.37 18.9 291.26 18.9 C 296.09 18.9 300.06 20.63 303.16 24.09 C 306.42 27.55 308.05 32.18 308.05 37.96 Z M 302.86 37.96 C 302.86 33.21 301.74 29.31 299.5 26.26 C 297.41 23.21 294.66 21.69 291.26 21.69 C 287.9 21.69 285.1 23.21 282.86 26.26 C 280.68 29.31 279.58 33.21 279.58 37.96 C 279.58 42.66 280.65 46.56 282.79 49.66 C 284.97 52.65 287.8 54.15 291.26 54.15 C 294.56 54.15 297.31 52.65 299.5 49.66 C 301.74 46.61 302.86 42.71 302.86 37.96 Z M 302.86 37.96 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 324.46 55.55 L 309.81 55.55 L 309.81 53.07 C 311.8 53.07 313.14 52.78 313.86 52.21 C 314.46 51.7 314.77 50.59 314.77 48.88 L 314.77 8.52 C 314.77 7.64 314.46 7.02 313.86 6.66 C 313.25 6.25 311.95 6.04 309.96 6.04 L 309.51 4.18 C 311.13 3.56 312.79 2.97 314.46 2.4 C 316.14 1.78 317.8 1.16 319.43 0.54 L 319.43 48.88 C 319.43 50.48 319.71 51.57 320.27 52.14 C 320.67 52.39 321.21 52.6 321.87 52.76 C 322.58 52.96 323.45 53.07 324.46 53.07 Z M 324.46 55.55 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 340.23 4.34 C 340.23 5.58 339.82 6.61 339.01 7.44 C 338.19 8.26 337.18 8.68 335.95 8.68 C 334.84 8.68 333.89 8.26 333.13 7.44 C 332.42 6.61 332.06 5.58 332.06 4.34 C 332.06 3.1 332.44 2.07 333.21 1.24 C 333.97 0.41 334.89 0 335.95 0 C 337.18 0 338.19 0.41 339.01 1.24 C 339.82 2.07 340.23 3.1 340.23 4.34 Z M 343.81 55.62 L 329.54 55.62 L 329.54 53.07 C 331.63 53.07 332.98 52.76 333.59 52.14 C 334.1 51.67 334.35 50.82 334.35 49.58 L 334.35 27.27 C 334.35 26.13 334.12 25.36 333.66 24.95 C 333.11 24.48 331.76 24.25 329.62 24.25 L 329.31 22.54 L 339.01 18.82 L 339.01 49.58 C 339.01 51.03 339.23 51.93 339.69 52.29 C 340.36 52.81 341.73 53.07 343.81 53.07 Z M 343.81 55.62 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 375.11 46.64 C 375.11 48.75 374.52 50.64 373.36 52.29 C 373.1 52.55 372.84 52.81 372.59 53.07 C 372.39 53.32 372.16 53.58 371.9 53.84 C 369.61 55.86 366.51 56.86 362.59 56.86 C 359.9 56.86 357.56 56.42 355.57 55.55 C 355.32 55.44 355.06 55.36 354.81 55.31 C 354.61 55.31 354.38 55.31 354.12 55.31 L 353.36 55.31 C 352.8 55.57 352.27 55.78 351.76 55.93 C 351.25 56.14 350.71 56.34 350.16 56.55 C 349.44 54.59 348.7 52.68 347.94 50.82 C 347.18 48.91 346.44 46.97 345.73 45.01 C 345.93 44.86 346.11 44.7 346.26 44.54 C 346.46 44.39 346.7 44.26 346.95 44.16 C 349.14 47.26 351.22 49.53 353.21 50.97 C 356.36 53.04 359.46 54.07 362.52 54.07 C 364.91 54.07 366.92 53.32 368.55 51.83 C 369.87 50.48 370.53 48.86 370.53 46.95 C 370.53 45.71 370.05 44.6 369.08 43.61 C 368.88 43.46 368.62 43.28 368.32 43.07 C 368.01 42.87 367.66 42.63 367.25 42.38 C 366.59 41.96 365.72 41.55 364.66 41.14 C 363.59 40.67 362.29 40.18 360.76 39.66 C 358.37 38.84 356.39 38.09 354.81 37.42 C 353.23 36.7 352.04 36 351.22 35.32 C 348.17 33.41 346.64 30.88 346.64 27.73 C 346.64 24.84 347.99 22.54 350.69 20.84 C 352.72 19.55 355.27 18.9 358.32 18.9 C 360.96 18.9 363.69 19.42 366.48 20.45 C 366.89 20.35 367.32 20.17 367.79 19.91 C 368.24 19.6 368.75 19.24 369.31 18.82 C 370.02 20.68 370.73 22.57 371.45 24.48 C 372.16 26.34 372.87 28.22 373.58 30.14 C 373.33 30.24 373.07 30.37 372.82 30.52 C 372.62 30.68 372.39 30.81 372.13 30.91 C 370.86 28.43 369.16 26.42 367.02 24.87 C 363.87 22.75 360.96 21.69 358.32 21.69 C 356.39 21.69 354.86 22.03 353.74 22.7 C 352.06 23.63 351.22 25.23 351.22 27.5 C 351.22 28.95 351.96 30.19 353.44 31.22 C 353.64 31.32 353.84 31.43 354.05 31.53 C 354.25 31.63 354.48 31.74 354.73 31.84 C 355.45 32.25 356.34 32.69 357.41 33.16 C 358.47 33.62 359.67 34.06 360.99 34.47 C 363.74 35.35 366 36.23 367.79 37.11 C 369.61 37.93 371.02 38.71 371.98 39.43 C 374.07 41.14 375.11 43.54 375.11 46.64 Z M 375.11 46.64 \"><\/path>\n      <path fill=\"currentColor\" d=\"M 396.65 47.95 C 396.91 48.16 397.14 48.34 397.34 48.5 C 397.59 48.65 397.85 48.8 398.10 48.96 C 397.19 51.13 395.94 52.94 394.36 54.38 C 392.58 56.09 390.65 56.94 388.56 56.94 C 385.82 56.94 383.73 55.93 382.3 53.92 C 381.9 53.2 381.57 52.37 381.31 51.44 C 381.06 50.51 380.93 49.48 380.93 48.34 L 380.93 22.7 L 374.98 22.7 L 374.98 20.22 C 377.73 20.22 379.76 19.16 381.08 17.04 C 382.56 14.62 383.3 11.16 383.3 6.66 L 385.59 6.66 L 385.59 19.68 L 395.58 19.68 L 395.58 22.7 L 385.59 22.7 L 385.59 48.88 C 385.59 51.62 387.01 52.99 389.86 52.99 C 392.15 52.99 394.41 51.31 396.65 47.95 Z M 396.65 47.95 \"><\/path>\n      <path fill=\"var(--tb-mark,#FF4001)\" d=\"M 102.74 0.61 L 30.22 0.61 C 15.14 0.61 2.91 13 2.91 28.29 L 2.91 29.11 C 2.91 44.4 15.14 56.8 30.22 56.8 L 102.74 56.8 C 117.82 56.8 130.05 44.4 130.05 29.11 L 130.05 28.29 C 130.05 13 117.82 0.61 102.74 0.61 Z M 117.73 45.16 C 113.47 49.48 107.8 51.86 101.78 51.86 C 95.75 51.86 90.08 49.48 85.82 45.16 C 84.41 43.72 83.19 42.11 82.21 40.38 C 78.98 34.67 72.98 31.15 66.5 31.14 L 66.48 31.14 C 60 31.14 54.01 34.65 50.77 40.35 C 49.79 42.09 48.56 43.7 47.12 45.16 C 42.86 49.48 37.2 51.86 31.17 51.86 C 25.14 51.86 19.47 49.48 15.21 45.16 C 6.41 36.24 6.41 21.72 15.21 12.8 C 19.48 8.48 25.14 6.11 31.17 6.11 C 37.2 6.11 42.87 8.48 47.12 12.8 C 48.56 14.25 49.79 15.87 50.77 17.61 C 54.01 23.31 60 26.82 66.48 26.82 L 66.5 26.82 C 72.99 26.81 78.98 23.29 82.21 17.59 C 83.19 15.85 84.41 14.24 85.82 12.8 C 90.09 8.48 95.75 6.11 101.78 6.11 C 107.8 6.11 113.48 8.48 117.73 12.8 C 122 17.12 124.34 22.87 124.34 28.98 C 124.34 35.09 122 40.84 117.73 45.16 Z M 117.73 45.16 \"><\/path>\n      <\/svg>\n    <\/a>\n    <ul class=\"tb-nav-links\">\n      <li><a href=\"\/services\/\">Services<\/a><\/li>\n      <li><a href=\"\/cases\/\" class=\"tb-on\">Work<\/a><\/li>\n      <li><a href=\"\/insights\/\">Insights<\/a><\/li>\n      <li><a href=\"\/about\/\">About<\/a><\/li>\n    <\/ul>\n    <a class=\"tb-nav-cta\" href=\"\/contact\/\">Contact<\/a>\n  <\/nav>\n\n\n\n  <!-- ============================================================\n       CABECERA DE CASO\n       ============================================================ -->\n  <header class=\"tb-case-head\">\n    <p class=\"tb-eyebrow-2\">Case study \u00b7 Industrial<\/p>\n    <h1 class=\"tb-case-title\">Automated Recognized Revenue and analytical P&amp;L<\/h1>\n    <p class=\"tb-lede\">\n      A full Databricks data warehouse on SAP data, surfaced through live Power BI dashboards.\n      Manual monthly closes became numbers you can trust any day.\n    <\/p>\n  <\/header>\n\n\n  <!-- ============================================================\n       THE SITUATION\n       ============================================================ -->\n  <section class=\"tb-sec tb-case-sec\">\n    <h2 class=\"tb-case-sec-h\">The situation<\/h2>\n    <p class=\"tb-case-sec-p\">\n      Financial and hiring reports lived inside SAP, rebuilt by hand every month \u2014 a process\n      that ate days before anyone could trust the numbers.\n    <\/p>\n  <\/section>\n\n\n  <!-- ============================================================\n       WHAT WE BUILT\n       ============================================================ -->\n  <section class=\"tb-sec tb-case-sec\">\n    <h2 class=\"tb-case-sec-h\">What we built<\/h2>\n    <p class=\"tb-case-sec-p\">\n      A Databricks warehouse centralizing SAP data end to end, feeding live Power BI dashboards\n      the finance team checks directly \u2014 no manual export, no reconciliation. We focus on the data\n      modelling from SAP raw tables and explode the data to be reviewed daily instead monthly.\n    <\/p>\n\n    <div class=\"tb-tech-tags\">\n      <span class=\"tb-tech-tag\">SAP<\/span>\n      <span class=\"tb-tech-tag\">Databricks<\/span>\n      <span class=\"tb-tech-tag\">Power BI<\/span>\n    <\/div>\n  <\/section>\n\n\n  <!-- ============================================================\n       SERVICES INVOLVED\n       ============================================================ -->\n  <section class=\"tb-sec tb-case-sec\">\n    <h2 class=\"tb-case-sec-h\">Services involved<\/h2>\n\n    <div class=\"tb-svc-cards\">\n      <a class=\"tb-svc-card\" href=\"\/services\/data-architecture\/\">\n        <h3 class=\"tb-svc-card-h\">Data architecture<\/h3>\n        <span class=\"tb-svc-card-arrow\">\u2192<\/span>\n      <\/a>\n\n      <a class=\"tb-svc-card\" href=\"\/services\/business-intelligence\/\">\n        <h3 class=\"tb-svc-card-h\">Business intelligence<\/h3>\n        <p class=\"tb-svc-card-p\">\n          Live Power BI dashboards, semantic layer for financial metrics, automated refresh schedules\n        <\/p>\n        <span class=\"tb-svc-card-arrow\">\u2192<\/span>\n      <\/a>\n    <\/div>\n  <\/section>\n\n\n  <!-- ============================================================\n       CTA\n       ============================================================ -->\n  <section class=\"tb-scta\">\n    <div class=\"tb-scta-in\">\n      <div class=\"tb-scta-l\">\n        <p class=\"tb-eyebrow-2\">Next steps<\/p>\n        <h2 class=\"tb-scta-h\">This kind of work starts with architecture.<\/h2>\n        <p class=\"tb-scta-d\">\n          If your data lives in SAP, Oracle, or legacy systems, we can design the warehouse\n          structure and build the pipelines that make live reporting possible.\n        <\/p>\n      <\/div>\n      <div class=\"tb-scta-r\">\n        <a class=\"tb-cta-btn\" href=\"\/contact\/\">\n          Start a conversation\n          <svg width=\"15\" height=\"11\" viewBox=\"0 0 15 11\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M1 5.5h12M9 1l4 4.5L9 10\" stroke=\"#fff\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path>\n          <\/svg>\n        <\/a>\n        <p class=\"tb-scta-alt\">\n          Or <a href=\"\/cases\/\">back to all cases<\/a>\n        <\/p>\n      <\/div>\n    <\/div>\n  <\/section>\n\n<\/div>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@graph\": [\n    {\n      \"@type\": \"Organization\",\n      \"@id\": \"https:\/\/tobolist.com\/#organization\",\n      \"name\": \"Tobolist\",\n      \"url\": \"https:\/\/tobolist.com\/\"\n    },\n    {\n      \"@type\": \"Article\",\n      \"@id\": \"https:\/\/tobolist.com\/case-industrial\/#case-study\",\n      \"url\": \"https:\/\/tobolist.com\/case-industrial\/\",\n      \"mainEntityOfPage\": \"https:\/\/tobolist.com\/case-industrial\/\",\n      \"inLanguage\": \"en\",\n      \"articleSection\": \"Case study\",\n      \"headline\": \"Automated Recognized Revenue and analytical P&L\",\n      \"description\": \"A full Databricks data warehouse on SAP data, surfaced through live Power BI dashboards. Manual monthly closes became numbers you can trust any day.\",\n      \"abstract\": \"Financial and hiring reports lived inside SAP and were rebuilt by hand every month. Tobolist centralised SAP data in a Databricks warehouse and fed live Power BI dashboards, moving the finance team from a monthly manual close to figures they can review daily.\",\n      \"datePublished\": \"<<2026-08-25>>\",\n      \"dateModified\": \"<<2026-08-25>>\",\n      \"author\": {\n        \"@id\": \"https:\/\/tobolist.com\/#organization\"\n      },\n      \"publisher\": {\n        \"@id\": \"https:\/\/tobolist.com\/#organization\"\n      },\n      \"keywords\": \"SAP, Databricks, Power BI, data warehouse, recognised revenue, analytical P&L, financial reporting, industrial\",\n      \"isPartOf\": {\n        \"@type\": \"CollectionPage\",\n        \"name\": \"Work\",\n        \"url\": \"https:\/\/tobolist.com\/cases\/\"\n      },\n      \"about\": [\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"Data warehouse\",\n          \"sameAs\": \"https:\/\/en.wikipedia.org\/wiki\/Data_warehouse\"\n        },\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"Financial reporting\",\n          \"sameAs\": \"https:\/\/en.wikipedia.org\/wiki\/Financial_statement\"\n        },\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"Business intelligence\",\n          \"sameAs\": \"https:\/\/en.wikipedia.org\/wiki\/Business_intelligence\"\n        },\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"SAP ERP\",\n          \"sameAs\": [\n            \"https:\/\/en.wikipedia.org\/wiki\/SAP_ERP\",\n            \"https:\/\/www.wikidata.org\/wiki\/Q167533\"\n          ]\n        },\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"Databricks\",\n          \"sameAs\": [\n            \"https:\/\/en.wikipedia.org\/wiki\/Databricks\",\n            \"https:\/\/www.wikidata.org\/wiki\/Q18350420\",\n            \"https:\/\/www.databricks.com\/\"\n          ]\n        },\n        {\n          \"@type\": \"Thing\",\n          \"name\": \"Microsoft Power BI\",\n          \"sameAs\": [\n            \"https:\/\/en.wikipedia.org\/wiki\/Microsoft_Power_BI\",\n            \"https:\/\/www.wikidata.org\/wiki\/Q23542287\"\n          ]\n        }\n      ],\n      \"subjectOf\": [\n        {\n          \"@type\": \"Service\",\n          \"name\": \"Data architecture\",\n          \"url\": \"https:\/\/tobolist.com\/services\/data-architecture\/\",\n          \"provider\": {\n            \"@id\": \"https:\/\/tobolist.com\/#organization\"\n          }\n        },\n        {\n          \"@type\": \"Service\",\n          \"name\": \"Business intelligence\",\n          \"url\": \"https:\/\/tobolist.com\/services\/business-intelligence\/\",\n          \"provider\": {\n            \"@id\": \"https:\/\/tobolist.com\/#organization\"\n          }\n        }\n      ]\n    }\n  ]\n}\n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>Services Work Insights About Contact Case study \u00b7 Industrial Automated Recognized Revenue and analytical P&amp;L A full Databricks data warehouse on SAP data, surfaced through live Power BI dashboards. Manual monthly closes became numbers you can trust any day. The situation Financial and hiring reports lived inside SAP, rebuilt by hand every month \u2014 a &#8230; <a title=\"Caso Industrial\" class=\"read-more\" href=\"https:\/\/tobolist.com\/es\/case-industrial\/\" aria-label=\"Leer m\u00e1s sobre Case Industrial\">Leer m\u00e1s<\/a><\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-328","page","type-page","status-publish"],"_links":{"self":[{"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/pages\/328","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/comments?post=328"}],"version-history":[{"count":6,"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/pages\/328\/revisions"}],"predecessor-version":[{"id":444,"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/pages\/328\/revisions\/444"}],"wp:attachment":[{"href":"https:\/\/tobolist.com\/es\/wp-json\/wp\/v2\/media?parent=328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}