From 9d95d349fae7e5c720b0b56f771e86ac68e7bcc8 Mon Sep 17 00:00:00 2001 From: medic-ci Date: Wed, 4 Sep 2024 14:59:04 +0000 Subject: [PATCH] deploy: medic/cht-docs@e02b485c9ece189d81dfc3b9f92554859fa37061 --- 404.html | 2 +- apps/concepts/access/index.html | 2 +- apps/concepts/care-guides/index.html | 2 +- apps/concepts/forms/index.html | 2 +- apps/concepts/hierarchy/index.html | 2 +- apps/concepts/index.html | 2 +- apps/concepts/interoperability/index.html | 2 +- apps/concepts/navigation/index.html | 2 +- apps/concepts/prerequisites/index.html | 2 +- apps/concepts/users/index.html | 2 +- apps/concepts/workflows/index.html | 2 +- apps/examples/anc/index.html | 2 +- apps/examples/contact-tracing/index.html | 2 +- apps/examples/covid-education/index.html | 2 +- .../covid-rdt-reference-app/index.html | 2 +- apps/examples/direct-to-client/index.html | 2 +- apps/examples/ebs/index.html | 2 +- apps/examples/index.html | 2 +- apps/examples/interoperability/index.html | 2 +- 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    • Environment Variables

    Environment variables for running CHT Sync

    There are three environment variable groups in the .env file. To successfully set up CHT Sync, it is important to understand the difference between them.

    1. POSTGRES_: Used by PostgreSQL to establish the PostgreSQL database to synchronize CouchDB data to. They define the schema and table names to store the CouchDB data, as well as where the tables and views for the models defined in CHT_PIPELINE_BRANCH_URL will be created.
    2. COUCHDB_: Used by CouchDB to define the CouchDB instance to sync with. With COUCHDB_DBS, we can specify a list of databases to sync.

    All the variables in the .env file:

    NameDefaultDescription
    COMPOSE_PROJECT_NAMEpipeline(Optional) Docker Compose name
    POSTGRES_USERpostgresUsername of the PostgreSQL database
    POSTGRES_PASSWORDpostgresPassword of the PostgreSQL database
    POSTGRES_DBdataPostgreSQL database
    POSTGRES_SCHEMAv1PostgreSQL schema
    POSTGRES_TABLEcouchdbPostgreSQL table where the CouchDB data is copied
    POSTGRES_HOSTpostgresPostgreSQL instance
    POSTGRES_PORT5432PostgreSQL port
    CHT_PIPELINE_BRANCH_URL"https://github.com/medic/cht-pipeline.git#main"CHT Pipeline branch containing the DBT models
    DATAEMON_INTERVAL5Interval (in minutes) for looking for new changes in the CouchDB data
    COUCHDB_USERmedicUsername of the CouchDB instance
    COUCHDB_PASSWORDpasswordPassword of the CouchDB instance
    COUCHDB_DBS"medic"Space separated list of databases to sync e.g "medic medic_sentinel"
    COUCHDB_HOSTcouchdbHost of the CouchDB instance
    COUCHDB_PORT5984Port of the CouchDB instance
    COUCHDB_SECUREfalseIs connection to CouchDB instance secure?
    -


    Last modified 30.08.2024: chore(#1513): remove outdated CHT Sync environment variables (#1514) (a53f2ba6)
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    Environment Variables

    Environment variables for running CHT Sync

    There are three environment variable groups in the .env file. To successfully set up CHT Sync, it is important to understand the difference between them.

    1. POSTGRES_: Used by PostgreSQL to establish the PostgreSQL database to synchronize CouchDB data to. They define the schema and table names to store the CouchDB data, as well as where the tables and views for the models defined in CHT_PIPELINE_BRANCH_URL will be created.
    2. COUCHDB_: Used by CouchDB to define the CouchDB instance to sync with. With COUCHDB_DBS, we can specify a list of databases to sync.

    All the variables in the .env file:

    NameDefaultDescription
    COMPOSE_PROJECT_NAMEpipeline(Optional) Docker Compose name
    POSTGRES_USERpostgresUsername of the PostgreSQL database
    POSTGRES_PASSWORDpostgresPassword of the PostgreSQL database
    POSTGRES_DBdataPostgreSQL database
    POSTGRES_SCHEMAv1PostgreSQL schema
    POSTGRES_TABLEcouchdbPostgreSQL table where the CouchDB data is copied
    POSTGRES_HOSTpostgresPostgreSQL instance
    POSTGRES_PORT5432PostgreSQL port
    CHT_PIPELINE_BRANCH_URL"https://github.com/medic/cht-pipeline.git#main"cht-pipeline branch containing the dbt models
    DATAEMON_INTERVAL5Interval (in minutes) for looking for new changes in the CouchDB data
    COUCHDB_USERmedicUsername of the CouchDB instance
    COUCHDB_PASSWORDpasswordPassword of the CouchDB instance
    COUCHDB_DBS"medic"Space separated list of databases to sync e.g "medic medic_sentinel"
    COUCHDB_HOSTcouchdbHost of the CouchDB instance
    COUCHDB_PORT5984Port of the CouchDB instance
    COUCHDB_SECUREfalseIs connection to CouchDB instance secure?
    +


    Last modified 04.09.2024: chore(#1521): remove CHT Pipeline mentions (#1522) (e02b485c)
    Edit this page + Create documentation issue Create project issue
    \ No newline at end of file diff --git a/apps/guides/data/analytics/index.html b/apps/guides/data/analytics/index.html index 12cdc0ba8..b0fb65496 100644 --- a/apps/guides/data/analytics/index.html +++ b/apps/guides/data/analytics/index.html @@ -1,8 +1,8 @@ Data Synchronization and Analytics | Community Health Toolkit -

    Data Synchronization and Analytics

    Using CHT Sync and CHT Pipeline for data synchronization and analytics
    Introduction & Prerequisites to data synchronization and analytics

    High level approach to data synchronization and analytics with CHT applications

    Local CHT Sync Setup

    Setting up a local deployment of CHT Sync with the CHT

    Production CHT Sync Setup

    Setting up a production deployment of CHT Sync with the CHT

    Environment Variables

    Environment variables for running CHT Sync

    Testing dbt models

    Guide for testing dbt models in CHT Pipeline

    -


    Last modified 08.03.2024: chore(#1297): Update CHT Sync doc Part 1 (#1300) (6e5c7753)
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    Data Synchronization and Analytics

    Using CHT Sync for data synchronization and analytics
    Introduction & Prerequisites to data synchronization and analytics

    High level approach to data synchronization and analytics with CHT applications

    Local CHT Sync Setup

    Setting up a local deployment of CHT Sync with the CHT

    Production CHT Sync Setup

    Setting up a production deployment of CHT Sync with the CHT

    Environment Variables

    Environment variables for running CHT Sync

    Testing dbt models

    Guide for testing dbt models

    +


    Last modified 04.09.2024: chore(#1521): remove CHT Pipeline mentions (#1522) (e02b485c)
    Edit this page + Create documentation issue Create project issue
    \ No newline at end of file diff --git a/apps/guides/data/analytics/introduction/index.html b/apps/guides/data/analytics/introduction/index.html index f22a37f8f..20db04b7f 100644 --- a/apps/guides/data/analytics/introduction/index.html +++ b/apps/guides/data/analytics/introduction/index.html @@ -1,8 +1,8 @@ Introduction & Prerequisites to data synchronization and analytics | Community Health Toolkit

    Introduction & Prerequisites to data synchronization and analytics

    High level approach to data synchronization and analytics with CHT applications

    The pages in this section apply to both CHT 3.x (beyond 3.12) and CHT 4.x.

    Most CHT deployments require some sort of analytics so that stakeholders can make data driven decisions. CouchDB, which is the database used by the CHT, is not designed for analytics. It is a document database, which means that it is optimized for storing and retrieving documents, and not for aggregating data. For example, if you wanted to know how many patients were registered in a particular area, you would have to query the database for all the patients in that area, and then count them. This is not a very efficient process. It is much more efficient to store the number of patients in a particular area in a separate database, and update that number whenever a patient is registered or unregistered. This is what CHT Sync paired with CHT Pipeline is designed to do.

    CHT Sync Introduction

    CHT Sync is an integrated solution designed to enable data synchronization between CouchDB and PostgreSQL for the purpose of analytics. It can easily be deployed using Docker. It is supported on CHT 3.12 and later, including CHT 4.x. By using CHT Sync, a CHT deployment can easily get analytics by using a data visualization tool. All tools are open-source and have no licensing fees.

    CHT Sync has been designed to work in both local development environments for testing models or workflows, and in production environments. The setup can accommodate the needs of different environments.

    CHT Sync Prerequisites

    Related Content

    CHT Core Framework > + Create documentation issue + Create project issue

    Introduction & Prerequisites to data synchronization and analytics

    High level approach to data synchronization and analytics with CHT applications

    The pages in this section apply to both CHT 3.x (beyond 3.12) and CHT 4.x.

    Most CHT deployments require some sort of analytics so that stakeholders can make data driven decisions. CouchDB, which is the database used by the CHT, is not designed for analytics. It is a document database, which means that it is optimized for storing and retrieving documents, and not for aggregating data. For example, if you wanted to know how many patients were registered in a particular area, you would have to query the database for all the patients in that area, and then count them. This is not a very efficient process. It is much more efficient to store the number of patients in a particular area in a separate database, and update that number whenever a patient is registered or unregistered. This is what CHT Sync is designed to do.

    CHT Sync Introduction

    CHT Sync is an integrated solution designed to enable data synchronization between CouchDB and PostgreSQL for the purpose of analytics. It can easily be deployed using Docker. It is supported on CHT 3.12 and later, including CHT 4.x. By using CHT Sync, a CHT deployment can easily get analytics by using a data visualization tool. All tools are open-source and have no licensing fees.

    CHT Sync has been designed to work in both local development environments for testing models or workflows, and in production environments. The setup can accommodate the needs of different environments.

    CHT Sync Prerequisites

    Related Content

    CHT Core Framework > Overview > CHT Sync

    Data synchronization tools to enable analytics

    CHT Core Framework > Overview > Data Flows

    An overview of data flows in the CHT for analytics, impact monitoring, and data science

    -


    Last modified 04.09.2024: chore: Cleanup data pipeline pages (#1520) (c80ade78)
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    Last modified 04.09.2024: chore(#1521): remove CHT Pipeline mentions (#1522) (e02b485c)
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    \ No newline at end of file diff --git a/apps/guides/data/analytics/production/index.html b/apps/guides/data/analytics/production/index.html index 5cf04bed3..7222c8a5e 100644 --- a/apps/guides/data/analytics/production/index.html +++ b/apps/guides/data/analytics/production/index.html @@ -1,8 +1,8 @@ Production CHT Sync Setup | Community Health Toolkit

    Production CHT Sync Setup

    Setting up a production deployment of CHT Sync with the CHT

    We recommend running CHT Sync in production using Kubernetes. This guide will walk you through setting up a production deployment of CHT Sync with the CHT using Kubernetes.

    Prerequisites:

    • A Kubernetes cluster: You can use a managed Kubernetes service like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS), or you can set up a cluster using a tool like Minikube.
    • kubectl: The Kubernetes command-line tool. You can install it using the kubectl installation instructions.
    • Helm: The Kubernetes package manager. You can install it using the helm installation guide.

    Setup

    • Using git, clone the CHT Sync repository from GitHub: git clone https://github.com/medic/cht-sync.git
    • In the cht-sync folder, copy the values in deploy/cht_sync/values.yaml.template file to a new file named deploy/cht_sync/values.yaml.
    • If you require a Postgres database to be set up in the cluster, you can use the postgres.enabled flag in the values.yaml file. If you already have a Postgres database outside the cluster, you can set the postgres.enabled flag to false.
    • If outside the cluster, specify host and port in this section
    • In either case, specify user, password, db, schema, and table
      • schema can be used to separate CHT models from any other data that may already be in the database
      • table is the name of the table that couch2pg will write couch documents to, and the source table for dbt models. It is recommended to leave this as couchdb.
    postgres:
   enabled: true
   user: "postgres"
@@ -332,7 +332,7 @@
     secure: "false"
   - host: "host3.cht-core.test"
     password: "password3"
-
    • Set the CHT Pipeline Branch URL in the values.yaml file.
    cht_pipeline_branch_url: "https://github.com/medic/cht-pipeline.git#main"
+
    • Set the cht-pipeline branch URL in the values.yaml file.
    cht_pipeline_branch_url: "https://github.com/medic/cht-pipeline.git#main"
    • (Optional) Configure the Metrics Exporter. If enabled, this will create a sql exporter that queries the database for couch2pg status, number of changes pending, and current sequence and exposes these metrics in prometheus format at a service with name metrics at port 9399, for use with cht watchdog or any other monitoring service. An HTTP ingress needs to be created to allow access from outside the cluster.
    metrics_exporter:
   enabled: true
@@ -344,8 +344,8 @@
 CHT Core
    The different pieces of a CHT project, how they interact, and what they’re used for
    CHT Core Framework > Overview > CHT Sync

    Data synchronization tools to enable analytics

    -


    Last modified 04.09.2024: chore: Cleanup data pipeline pages (#1520) (c80ade78)
    Edit this page - Create documentation issue +


    Last modified 04.09.2024: chore(#1521): remove CHT Pipeline mentions (#1522) (e02b485c)
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    \ No newline at end of file diff --git a/apps/guides/data/analytics/setup/index.html b/apps/guides/data/analytics/setup/index.html index e9366298e..6c4dbc67d 100644 --- a/apps/guides/data/analytics/setup/index.html +++ b/apps/guides/data/analytics/setup/index.html @@ -188,7 +188,7 @@
  • -
  • +
  • diff --git a/apps/guides/data/analytics/testing-dbt-models/index.html b/apps/guides/data/analytics/testing-dbt-models/index.html index 83c2cc23f..d16eadfd5 100644 --- a/apps/guides/data/analytics/testing-dbt-models/index.html +++ b/apps/guides/data/analytics/testing-dbt-models/index.html @@ -1,8 +1,8 @@ Testing dbt models | Community Health Toolkit -

    • Testing dbt models

    Guide for testing dbt models in CHT Pipeline

    Overview

    To ensure code accuracy and data integrity, and also to prevent data quality regressions on dbt models, it is recommended to write dbt tests. dbt tests help validate the accuracy and reliability of data and data models and identify issues before they cause downstream impacts on analytics and decision-making. Additionally, they increase developer confidence in making changes to the data models.

    Types of dbt tests

    There are two main types of dbt tests:

    • data tests - meant to be executed with every pipeline run to validate data integrity. They ensure the warehouse data meets specific criteria and are run at every data refresh.
    • unit tests - meant to be executed with every CI run to validate transformation logic integrity. They allow you to validate your SQL modeling logic on a small set of static inputs (typically defined using seeds or fixtures) before you materialize your full model in production.

    Data tests

    Data tests can be further divided into two types:

    • generic tests: These are foundational tests provided by dbt core, focusing on basic schema validation and source freshness. dbt core provides four built-in generic tests that are essential for data modeling and ensuring data integrity. Generic tests can accept additional test configurations. -It is also possible to define your own custom generic tests.

    • singular tests: These are written in an SQL file with a query that returns records that fail the test. This type of test is straightforward and focuses on specific conditions or rules that data must meet.

    Unit tests

    Unit tests are essential for validating complex SQL logic and transformations in dbt models. These tests are especially valuable when working with intricate SQL expressions. They help catch errors before deploying changes, ensuring the logic behaves as expected, particularly in critical models or when handling edge cases.

    For more details on formatting unit tests, refer to the official dbt documentation.

    Guidelines for CHT Pipeline tests

    To ensure data integrity and the reliability of the dbt models in the CHT Pipeline, it is essential to follow these testing guidelines:

    Testing dbt models

    Guide for testing dbt models

    Overview

    To ensure code accuracy and data integrity, and also to prevent data quality regressions on dbt models, it is recommended to write dbt tests. dbt tests help validate the accuracy and reliability of data and data models and identify issues before they cause downstream impacts on analytics and decision-making. Additionally, they increase developer confidence in making changes to the data models.

    Types of dbt tests

    There are two main types of dbt tests:

    • data tests - meant to be executed with every pipeline run to validate data integrity. They ensure the warehouse data meets specific criteria and are run at every data refresh.
    • unit tests - meant to be executed with every CI run to validate transformation logic integrity. They allow you to validate your SQL modeling logic on a small set of static inputs (typically defined using seeds or fixtures) before you materialize your full model in production.

    Data tests

    Data tests can be further divided into two types:

    • generic tests: These are foundational tests provided by dbt core, focusing on basic schema validation and source freshness. dbt core provides four built-in generic tests that are essential for data modeling and ensuring data integrity. Generic tests can accept additional test configurations. +It is also possible to define your own custom generic tests.

    • singular tests: These are written in an SQL file with a query that returns records that fail the test. This type of test is straightforward and focuses on specific conditions or rules that data must meet.

    Unit tests

    Unit tests are essential for validating complex SQL logic and transformations in dbt models. These tests are especially valuable when working with intricate SQL expressions. They help catch errors before deploying changes, ensuring the logic behaves as expected, particularly in critical models or when handling edge cases.

    For more details on formatting unit tests, refer to the official dbt documentation.

    Guidelines for dbt tests

    To ensure data integrity and the reliability of the dbt models in the cht-pipeline, it is essential to follow these testing guidelines:

    • Basic generic tests for all models: Every model should have generic tests to enforce critical constraints and relationships. Use the generic tests provided in dbt core.

    • Singular or custom generic data tests for aggregation models: For models that perform data aggregation, it is crucial to include singular data tests or custom generic data tests to ensure that the aggregated data meets the expected criteria. These tests help verify that:

      • Data is accurately aggregated according to the defined logic.
      • The results align with business expectations and requirements.
      • Custom data tests are particularly important for aggregation models, as errors in these models can lead to significant discrepancies in reports and analyses.

    • Unit tests for complex logic: -Unit tests are not strictly required but are highly recommended, especially for models with complex transformation logic. Examples of when to use unit tests include:

      • Complex SQL Logic: Such as regex, date math, window functions, or extensive CASE WHEN statements.
      • Custom calculations: When creating functions or applying unique data processing logic.
      • Edge cases: To handle scenarios that are not typically found in actual data but may arise unexpectedly.

    Writing CHT Pipeline tests

    CHT Pipeline contains a /models directory containing SQL files and YAML files for generic tests and a /test directory with folders for fixtures and singular tests.

    ./
+Unit tests are not strictly required but are highly recommended, especially for models with complex transformation logic. Examples of when to use unit tests include:
    • Complex SQL Logic: Such as regex, date math, window functions, or extensive CASE WHEN statements.
    • Custom calculations: When creating functions or applying unique data processing logic.
    • Edge cases: To handle scenarios that are not typically found in actual data but may arise unexpectedly.
    Writing dbt tests
    cht-pipeline contains a /models directory containing SQL files and YAML files for generic tests and a /test directory with folders for fixtures and singular tests.
    ./
     /models
         /contacts
             /tests
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 DBT tests passed
    -


    Last modified 02.09.2024: feat(#1507) add guide for testing dbt models (#1508) (7a7e93be)
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    Last modified 04.09.2024: chore(#1521): remove CHT Pipeline mentions (#1522) (e02b485c)
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    • Managing Data

    Creating and managing data in CHT applications
    Database document hydration

    Overview of database document hydration

    How to bulk load users

    How to create users in bulk

    CHT Impact Metrics

    A set of impact metrics for monitoring priority use cases across the Community Health Toolkit

    Detecting and fixing production data on training instances

    How to monitor for production data on a training instance, and remediation techniques

    Revalidate invalid reports

    How to revalidate an invalid report

    CSV to Docs

    Seeding data with cht-conf

    Data Synchronization and Analytics

    Using CHT Sync and CHT Pipeline for data synchronization and analytics

    + Create project issue

    Managing Data

    Creating and managing data in CHT applications
    Database document hydration

    Overview of database document hydration

    How to bulk load users

    How to create users in bulk

    CHT Impact Metrics

    A set of impact metrics for monitoring priority use cases across the Community Health Toolkit

    Detecting and fixing production data on training instances

    How to monitor for production data on a training instance, and remediation techniques

    Revalidate invalid reports

    How to revalidate an invalid report

    CSV to Docs

    Seeding data with cht-conf

    Data Synchronization and Analytics

    Using CHT Sync for data synchronization and analytics



    Last modified 05.06.2020: Categorized guides (d26e182e)
    Edit this page Create documentation issue Create project issue