The Data Foundation for Google Cloud Cortex Framework is a set of analytical artifacts, that can be automatically deployed together with reference architectures.

The current repository contains the analytical views and models that serve as a foundational data layer for the Google Cloud Cortex Framework in BigQuery. You can find the entity-relationship diagram for SAP ECC here (PDF), the one for SAP S/4 here (PDF) and one for Salesforce.com here (PDF).

If you are in a hurry and already know what you are doing, clone this repository recursively with submodules (--recurse-submodules) .

git clone --recurse-submodules https://github.com/GoogleCloudPlatform/cortex-data-foundation

Then change into the cloned directory execute the following command

gcloud builds submit --project <execution project, likely the source> \
--substitutions \
_PJID_SRC=<project for landing raw data>,_PJID_TGT=<project to deploy user-facing views>,_DS_CDC=<BQ dataset to land the result of CDC processing - must exist before deployment>,_DS_RAW=<BQ dataset to land raw data from replication - must exist before deployment>,_DS_REPORTING=<BQ dataset where Reporting views are created, will be created if it does not exist>,_DS_MODELS=<BQ dataset where ML views are created, will be created if it does not exist>,_GCS_BUCKET=<Bucket for logs - Cloud Build Service Account needs access to write here>,_TGT_BUCKET=<Bucket for DAG scripts - don’t use the actual Airflow bucket - Cloud Build Service Account needs access to write here>,_TEST_DATA=true,_DEPLOY_CDC=true,_GEN_EXT=true

You can find a step-by-step video going through a deployment with sample data in YouTube: [English version] - [Version en Español]

Before continuing with this guide, make sure you are familiar with:

• Google Cloud Platform fundamentals
• How to navigate the Cloud Console, Cloud Shell and Cloud Shell Editor
• Fundamentals of BigQuery
• Fundamentals of Cloud Composer or Apache Airflow
• Fundamental concepts of Change Data Capture and dataset structures.
• General navigation of Cloud Build
• Fundamentals of Identity and Access Management

You will require at least one GCP project to host the BigQuery datasets and execute the deployment process.

This is where the deployment process will trigger Cloud Build runs. In the project structure, we refer to this as the Source Project.

You will need to identify:

• What to deploy (SAP, Salesforce.com or everything)?: Decide whether you want to deploy models for both workloads at the same time or only one set of models (SAP or Salesforce).
• Source Google Cloud Project: Project where the source data is located, from which the data models will consume. This project is normally accessed by technical practitioners.
• Target Google Cloud Project: Project where the Data Foundation predefined data models will be deployed and accessed by end-users. This may or may not be different from the source project depending on your needs.
• Source BigQuery Dataset (RAW): BigQuery dataset where the source SAP or Salesforce data is replicated to or where the test data will be created. The recommendation is to have two separate datasets, one for each source (i.e., one raw dataset for SAP and one raw dataset for Salesforce).
• CDC BigQuery Dataset: BigQuery dataset where the CDC processed data lands the latest available records. This may or may not be the same as the source dataset if the tool landing the data performs the CDC merge operation. The recommendation is to have two separate CDC datasets, one for each source (i.e., one cdc dataset for SAP and one cdc dataset for Salesforce).
• Target BigQuery reporting dataset: BigQuery dataset where the Data Foundation predefined data models will be deployed. The recommendation is to have two separate reporting datasets, one for each source (i.e., one reporting dataset for SAP and one reporting dataset for Salesforce). This dataset is automatically created by the deployer if it does not exist.
• Target BigQuery machine learning dataset: BigQuery dataset where the BQML predefined models will be deployed. This dataset is automatically created by the deployer if it does not exist.

Alternatively, if you do not have a replication tool set up or do not wish to use the replicated data, the deployment process can generate test tables and fake data for you. You will still need to create and identify the datasets ahead of time.

If you currently have a replication tool from SAP ECC or S/4 HANA, you can use the same project (Source Project) or a different one for reporting.

Note: If you are using an existing dataset with data replicated from SAP, you can find the list of required tables in setting.yaml. If you do not have all of the required tables replicated, only the views that depend on missing tables will fail to deploy.

We provide a replication solution based on Python scripts scheduled in Apache Airflow and Salesforce Bulk API 2.0. These Python scripts can be adapted and scheduled in the tool of choice.

There are two sets of scripts:

• API call and load into RAW
• CDC processing scripts

If you have datasets already loaded through a different tool, the RAW processing scripts contain mapping files to map the structure of your tool into the names and data types of the structure required by the reporting views.

The following Google Cloud components are required:

• Google Cloud Project
• BigQuery instance and datasets
• Service Account with Impersonation rights
• Cloud Storage Buckets
• Cloud Build API
• Cloud Resource Manager API
• Optional components:
  • Cloud Composer for change data capture (CDC) processing (SAP and Salesforce), hierarchy flattening (SAP only), and data replication (Salesforce only) through Directed Acyclic Graphs (DAGs). You can find how to set up an instance of Cloud Composer in the documentation.
  • Looker **(optional, connects to reporting templates. Requires manual setup) **
  • Analytics Hub linked datasets (optional) are currently used for some external sources, such as the Weather DAG. You may choose to fill this structure with any other available source of your choice for advanced scenarios.

From the Cloud Shell, you can enable Google Cloud Services using the gcloud command line interface in your Google Cloud project.

Replace the <SOURCE_PROJECT> placeholder with your source project. Copy and paste the following command into the cloud shell:

gcloud config set project <SOURCE_PROJECT>

gcloud services enable bigquery.googleapis.com \
                       cloudbuild.googleapis.com \
                       composer.googleapis.com \
                       storage-component.googleapis.com \
                       cloudresourcemanager.googleapis.com

You should get a success message:

The Data Foundation data models require a number of raw tables to be replicated to BigQuery before deployment. For a full list of tables required to be replicated, check the SAP setting.yaml file and SFDC setting.yaml.

If you do not wish to use replicated data, the deployment process can create the tables and populate them with sample test data. The sample test data caters to very specific demo scenarios and has no guarantee of referential integrity or functional validity.

Notes:

• If a table does not exist during deployment, only the views that require it will fail.
• The SQL files refer to the tables in lowercase. See the documentation in BigQuery for more information on case sensitivity.

Depending on the source system, the following applies as well:

SAP ECC or S/4

• The structure of the source SAP table is expected to be the same as SAP generates it. That is, the same name and the same data types. If in doubt about a conversion option, we recommend following the default table mapping.
• The Data Foundation can work with any replication tool that produces the schema as noted above.

Salesforce.com

• The structure of the source tables follows snake_case naming in plural, i.e. some_objects. The columns have the same data types as how Salesforce represents them internally.
• Data Foundation comes with a set of replication and mapping scripts to load Salesforce data into BigQuery (see Appendix). You can use your own replication tool as long as the schema in the CDC processed dataset matches the schema documented in the mapping
• Any required tables that did not exist within the raw dataset will be created as empty tables during deployment. This is to ensure the CDC deployment step runs correctly.

If not using test data, make sure the replication tool includes the fields required for CDC processing or merges changes when inserting them into BigQuery. You can find more details about this requirement here. For SAP, the BigQuery Connector for SAP can include these fields by default with the Extra Field flag.

For Salesforce.com, the raw DAG generator inserts the timestamp and operation flag when calling the APIs. This is not a requirement for processing if an alternative replication tool takes care of CDC processing.

If you are not planning on deploying test data, and if you are planning on generating CDC DAG scripts during deployment, make sure table DD03L is replicated from SAP in the source project.

This table contains metadata about tables, like the list of keys, and is needed for the CDC generator and dependency resolver to work. This table will also allow you to add tables not currently covered by the model to generated CDC scripts, like custom or Z tables.

If an individual is executing the deployment with their own account, they will need, at minimum, the following permissions in the project where Cloud Build will be triggered:

• Service Usage Consumer
• Storage Object Viewer for the Cloud Build default bucket or bucket for logs
• Object Writer to the output buckets
• Cloud Build Editor
• Project Viewer or Storage Object Viewer

These permissions may vary depending on the setup of the project. Consider the following documentation if you run into errors:

• Permissions to run Cloud Build
• Permissions to storage for the Build Account
• Permissions for the Cloud Build service account
• Viewing logs from Builds

In the source project, navigate to the Cloud Build and locate the account that will execute the deployment process.

Locate the build account in IAM (make sure it says cloudbuild):

Grant the following permissions to the Cloud Build service account in both the source and target projects if they are different:

• BigQuery Data Editor
• BigQuery Job User

The deployment can run through a service account with impersonation rights, by adding the flag --impersonate-service-account. This service account will trigger a Cloud Build job, that will in turn run specific steps through the Cloud Build service account. This allows a user to trigger a deployment process without direct access to the resources.

The impersonation rights to the new, triggering service account need to be granted to the person running the command.

Navigate to the Google Cloud Platform Console and follow the steps to create a service account with the following role:

• Cloud Build Service Account

This role can be applied during the creation of the service account:

Authorize the ID of user who will be running the deployment to impersonate the service account that was created in the previous step. Authorize your own ID so you can run an initial check as well.

Once the service account has been created, navigate to the IAM Service Account administration, click on the service account, and into the Permissions tab.

Click Grant Access, type in the ID of the user who will execute the deployment and has impersonation rights, and assign the following role:

• Service Account Token Creator

Alternatively, you can complete this step from the Cloud Shell:

gcloud iam service-accounts create <SERVICE ACCOUNT> \
    --description="Service account for Cortex deployment" \
    --display-name="my-cortex-service-account"

gcloud projects add-iam-policy-binding <SOURCE_PROJECT> \
--member="serviceAccount:<SERVICE ACCOUNT>@<SOURCE_PROJECT>.iam.gserviceaccount.com" \
--role="roles/cloudbuild.builds.editor"

gcloud iam service-accounts add-iam-policy-binding <SERVICE ACCOUNT>\
  --member="user:<EXECUTING USER EMAIL>" \
  --role="roles/iam.serviceAccountTokenCreator"

A storage bucket will be required to leave any processing scripts that are generated. These scripts will be manually moved into a Cloud Composer or Apache Airflow instance after deployment.

Navigate to Cloud Storage and create a bucket in the same region as your BigQuery datasets.

Alternatively, you can use the following command to create a bucket from the Cloud Shell:

gsutil mb -l <REGION/MULTI-REGION> gs://<BUCKET NAME>

Navigate to the Permissions tab. Grant Storage Object Creator to the user executing the Build command or to the Service account you created for impersonation.

You can create a specific bucket for the Cloud Build process to store the logs. This is useful if you want to restrict data that may be stored in logs to a specific region. Create a GCS bucket with uniform access control, in the same region where the deployment will run.

Alternatively, here is the command line to create this bucket:

gsutil mb -l <REGION/MULTI-REGION> gs://<BUCKET NAME>

You will need to grant Object Admin permissions to the Cloud Build service account.

You can now run a simple script, the Cortex Deployment Checker, to simulate the deployment steps and make sure the prerequisites are fulfilled.

You will use the service account and Storage bucket created as prerequisites to test permissions and make sure the deployment is successful.

1. Clone repository from https://github.com/lsubatin/mando-checker. You can complete this from the Cloud Shell.

git clone  https://github.com/lsubatin/mando-checker

2. From Cloud Shell , change into the directory:

cd mando-checker

3. Run the following build command with the user that will run the deployment

gcloud builds submit \
   --project <SOURCE_PROJECT> \
   --substitutions _DEPLOY_PROJECT_ID=<SOURCE_PROJECT>,_DEPLOY_BUCKET_NAME=<GCS_BUCKET>,_LOG_BUCKET_NAME=<LOG_BUCKET> .

If using a service account for impersonation, add the following flag:

   --impersonate-service-account <SERVICE ACCOUNT>@<SOURCE_PROJECT>.iam.gserviceaccount.com \

Where

• SOURCE_PROJECT is the ID of the project that will receive the deployment
• SERVICE_ACCOUNT is the impersonation service account to run the build
• GCS_BUCKET is the bucket that will contain the CDC information
• LOG_BUCKET is the bucket where logs can be written. You can skip this parameter if using the default.

You may further customize some parameters if desired (see table below).

You can check the progress from the build in the log from Cloud Build:

The build will complete successfully if the setup is ready or fail if permissions need to be checked:

The table below contains the parameters that can be passed to the build for further customizing the test.

The Cortex Deployment Checker is a very simple utility that will try to exercise the required permissions to ensure that they are correctly set up.

1. List the given bucket
2. Write to the bucket
3. Create a BQ dataset
4. Create a BQ table in the dataset
5. Write a test record

The utility will also clean up the test file and dataset - however this operation may fail since the deletion permissions are not actually required for the deployment, then it needs to be manually cleaned up without any repercussions.

If the build completes successfully all mandatory checks passed, otherwise review the build logs for the missing permission and retry.

Clone this repository with submodules (--recurse-submodules) into an environment where you can execute the gcloud builds submit command and edit the configuration files. We recommend using the Cloud Shell.

If you have done a previous deployment, remember to navigate into the previously downloaded folder and execute a git pull --recurse-submodules to pull the latest changes.

You can use the configuration in the file setting.yaml if you need to generate change-data capture processing scripts. See the Appendix - Setting up CDC Processing for options. For test data, you can leave this file as a default.

Make any changes to the DAG templates as required by your instance of Airflow or Cloud Composer. You will find more information in the Appendix - Gathering Cloud Composer settings.

Note: If you do not have an instance of Cloud Composer, you can still generate the scripts and create it later.

This module is optional. If you want to add/process tables individually after deployment, you can modify the setting.yaml file to process only the tables you need and re-execute the specific module with calling src/SAP_CDC/deploy_cdc.sh or src/SAP_CDC/cloudbuild.cdc.yaml directly.

For certain CDC datasets, you may want to take advantages of BigQuery table partitioning, table clustering or both. This choice depends on many factors - the size and data of the table, columns available in the table, and your need of real time data with views vs data materialized as tables. By default, CDC settings do not apply table partitioning or table clustering - the choice is yours to configure it based on what works best for you.

You can read more about partitioning and clustering for SAP here.

NOTE:

1. This feature only applies when a dataset in setting.yaml is configured for replication as a table (e.g. load_frequency = "@daily") and not defined as a view (load_frequency = "RUNTIME").
2. A table can be both - a partitioned table as well as a clustered table.

Partition can be enabled by specifying partition_details property in setting.yaml for any base table.

Example:

   - base_table: vbap
     load_frequency: "@daily"
     partition_details: {
       column: "erdat", partition_type: "time", time_grain: "day"
     }

NOTE: See BigQuery Table Partition documentation details to understand these options and related limitations.

Clustering can be by specifying cluster_details property in setting.yaml for any base table.

Example:

   - base_table: vbak
     load_frequency: "@daily"
     cluster_details: {columns: ["vkorg"]}

NOTE: See BigQuery Table Cluster documentation details to understand these options and related limitations.

You can use the configuration in the file sets.yaml if you need to generate scripts to flatten hierarchies. See the Appendix - Configuring the flattener for options. This step is only executed if the CDC generation flag is set to true.

Some advanced use cases may require external datasets to complement an enterprise system of record such as SAP. In addition to external exchanges consumed from Analytics hub, some datasets may need custom or tailored methods to ingest data and join them with the reporting models. To deploy these sample datasets, if deploying fully for the first time, keep the _GEN_EXT flag as its default (true) and complete the prerequisites listed below. If complementing an existing deployment (i.e., the target datasets have already been generated), execute the cloudbuild.cdc.yaml with _DEPLOY_CDC=false or the script /src/SAP_CDC/generate_external_dags.sh. Use flag -h for help with the parameters.

If you want to only deploy a subset of the DAGs, remove the undesired ones from the EXTERNAL_DAGS variable in the beginning of /src/SAP_CDC/generate_external_dags.sh.

Note: You will need to configure the DAGs as follows:

1. Holiday Calendar: This DAG retrieves the holiday calendars from PyPi Holidays. You can adjust the list of countries and years to retrieve holidays, as well as parameters of the DAG from the file holiday_calendar.ini. Leave the defaults if using test data.
2. Product Hierarchy Texts: This DAG flattens materials and their product hierarchies. The resulting table can be used to feed the Trends list of terms to retrieve Interest Over Time. You can adjust the parameters of the DAG from the file prod_hierarchy_texts.py. Leave the defaults if using test data. You will need to adjust the levels of the hierarchy and the language under the markers for ## CORTEX-CUSTOMER:. If your product hierarchy contains more levels, you may need to add an additional SELECT statement similar to the CTE h1_h2_h3.
3. Trends: This DAG retrieves Interest Over Time for a specific set of terms from Google Search trends. The terms can be configured in trends.ini. You will need to adjust the time frame to 'today 7-d' in trends.py after an initial run. We recommend getting familiarized with the results coming from the different terms to tune parameters. We also recommend partitioning large lists to multiple copies of this DAG running at different times. For more information about the underlying library being used, see Pytrends.
4. Weather: By default, this DAG uses the publicly available test dataset bigquery-public-data.geo_openstreetmap.planet_layers. The query also relies on an NOAA dataset only available through Analytics Hub, noaa_global_forecast_system. This dataset needs to be created in the same region as the other datasets prior to executing deployment. If the datasets are not available in your region, you can continue with the following instructions and follow additional steps to transfer the data into the desired region.

You can skip this configuration if using test data.

• Navigate to BigQuery > Analytics Hub
• Click Search Listings. Search for "NOAA Global Forecast System"
• Click Add dataset to project. When prompted, keep "noaa_global_forecast_system" as the name of the dataset. If needed, adjust the name of the dataset and table in the FROM clauses in weather_daily.sql.
• Repeat the listing search for Dataset "OpenStreetMap Public Dataset".
• Adjust the FROM clauses containing bigquery-public-data.geo_openstreetmap.planet_layers in postcode.sql.

Analytics hub is currently only supported in EU and US locations and some datasets, such as NOAA Global Forecast, are only offered in a single multilocation. If you are targeting a location different from the one available for the required dataset, we recommend creating a scheduled query to copy the new records from the Analytics hub linked dataset followed by a transfer service to copy those new records into a dataset located in the same location or region as the rest of your deployment. You will then need to adjust the SQL files (e.g., src/SAP_CDC/src/external_dag/weather/weather_daily.sql)

Important Note: Before copying these DAGs to Cloud Composer, you will need to add the required python modules (holidays, pytrends) as dependencies.

You will need the following parameters ready for deployment, based on your target structure:

Optional parameters:

As of release 4.0, a deployment file config/config.json is introduced as an option to provide more flexibility and additional configuration parameters, like those specific to Salesforce.

For backwards compatibility, the parameters in the TL;DR section can continue to be passed to the deployment command through the --substitutions parameter in the command line.

Open the file in cortex-data-foundation/config/config.json. You can use the Cloud Shell to edit this file. Use the previous table to populate the file according to your needs.

🔻 Note: If a parameter is passed when invoking the gcloud builds submit through the --substitutions flag, it overrides the value in the config.json file.

For example, consider this config.json file with these values for SAP datasets:

[...]
"SAP" : {
        "datasets" : {
            "cdc" : "json_cdc_value",
            "raw" : "SCARYJSON13",
[...]

And the following sample call:

gcloud builds submit  \
--substitutions \
_DS_CDC=UNICORN,_GEN_EXT=false,_TGT_BUCKET=some_gcs_bucket,_GCS_BUCKET=some_logging_cbucket

The previous call will point the CDC dataset to a dataset called UNICORN from the substitution parameter, and the RAW dataset to one called SCARYJSON13 as no substitution parameter is passed. The generation of extra data is disabled by the parameter passed in the call (_GEN_EXT=false), while all other boolean parameters that control the execution of build steps (i.e., _TEST_DATA,_DEPLOY_SAP,_DEPLOY_SFDC,_DEPLOY_CDC) are defaulted to true in the cloudbuild.yaml file.

Many SAP and Salesforce customers will have specific customizations of their systems, such as additional documents in a flow or specific types of a record. These are specific to each customer and configured by functional analysts as the business needs arise. The spots on the SQL code where these specific enhancements could be done are marked with a comment starting with ## CORTEX-CUSTOMER. You can check for these comments after cloning the repository with a command like:

grep -R CORTEX-CUSTOMER

The UNION option for _SQL_FLAVOUR allows for an additional implementation to apply a UNION operation between views sourcing data from an ECC system and views sourcing data from an S/4HANA system. This feature is currently experimental and we are seeking feedback on it. The UNION dataset depends on the ECC and S/4HANA reporting datasets to exist. The following chart explains the flow of data when using UNION:

After deploying the reporting datasets for S/4 and ECC, open the file config/config.json and configure the datasets for ECC and S/4 respectively (i.e., SAP.datasets.cdcECC, SAP.datasets.cdcS4, SAP.datasets.rawECC, SAP.datasets.rawS4). You can leave the datasets that do not have a _SQL_FLAVOUR in their name empty.

Since the deployment for CDC and DAG generation needs to occur first and for each source system, the UNION option will only execute the SAP_REPORTING submodule. You can execute the submodule directly or from the Data Foundation deployer. If using the Data Foundation, after configuring the config.json file, this is what the command would look like:

gcloud builds submit --project <Source Project> \
--substitutions _PJID_SRC=<Source Project>,_PJID_TGT=<Target Project>,_GCS_BUCKET=<Bucket for Logs>,_GEN_EXT=false,_SQL_FLAVOUR=union

NOTE: If you are looking to ONLY execute the Reporting build from the build in the sap_reporting submodule, the configuration is currently taken from sap_config.env. This configuration file will be replaced in future releases for config.json.

Clone the repository and its submodules:

git clone --recurse-submodules https://github.com/GoogleCloudPlatform/cortex-data-foundation

Switch to the directory where the data foundation was cloned:

cd cortex-data-foundation

Run the Build command with the parameters you identified earlier.

The following command assumes the file config/config.json has been populated with values:

gcloud builds submit --project <execution project, likely the source> \
--substitutions=_GCS_BUCKET=<Bucket for logs - Cloud Build Service Account needs access to write here>,_TGT_BUCKET=<Bucket for DAG scripts - don’t use the actual Airflow bucket - Cloud Build Service Account needs access to write here>,_TEST_DATA=true,_DEPLOY_SAP=true,_DEPLOY_SFDC=true,_DEPLOY_CDC=true,_GEN_EXT=true

If you have enough permissions, you can see the progress from Cloud Build.

Alternatively, the following example shows a call with all the available substitution values. These override the values in config.json.

gcloud builds submit --project <execution project, likely the source> \
--substitutions \
_PJID_SRC=<project for landing raw data>,_PJID_TGT=<project to deploy user-facing views>,_DS_CDC=<BQ dataset to land the result of CDC processing - must exist before deployment>,_DS_RAW=<BQ dataset to land raw data from replication - must exist before deployment>,_DS_REPORTING=<BQ dataset where Reporting views are created, will be created if it does not exist>,_DS_MODELS=<BQ dataset where ML views are created, will be created if it does not exist>,_GCS_BUCKET=<Bucket for logs - Cloud Build Service Account needs access to write here>,_TGT_BUCKET=<Bucket for DAG scripts - don’t use the actual Airflow bucket - Cloud Build Service Account needs access to write here>,_TEST_DATA=true,_DEPLOY_CDC=true

After the test data and CDC generation steps, you will see the step that generates views triggers one build process per view. If a single view fails, the parent process will appear as failed:

If TURBO=false, You can check the results for each individual view , following the main Build process.

And identify any issues with individual builds:

We recommend pasting the generated SQL into BigQuery to identify and correct the errors more easily. Most errors will be related to fields that are selected but not present in the replicated source. The BigQuery UI will help identify and comment those out.

If you opted to generate the CDC files and have an instance of Airflow, you can move them into their final bucket with the following command:

gsutil cp -r  gs://<output bucket>/dags* gs://<composer dag bucket>/dags
gsutil cp -r  gs://<output bucket>/data/* gs://<composer sql bucket>/data/

Instructions for deploying the pre-built Looker blocks can be found here. Please follow the individual block READMEs for detailed instructions on required dependencies to ensure successful deployment.

Optionally, you may also customize the blocks by forking the GitHub repositories into your own Looker project. Instructions can be found here.

Available blocks for the Data Foundation include the following:

• SAP Operational Dashboards for Cortex
• Salesforce Dashboards for Cortex

You can deploy the Demand Sensing use case from the Marketplace. Learn more from the documentation.

Deploy a sample micro-services based application through the Google Cloud Marketplace.

We strongly encourage you to fork this repository and apply your changes to the code in your own fork. You can make use of the delivered deployment scripts in your development cycles and incorporate your own test scripts as needed. When a new release is published, you can compare the new release from our repository with your own changes by merging our code into your own fork in a separate branch. Suggestions for changes or possible customizations in the code are flagged with the comment ## CORTEX-CUSTOMER. We recommend listing these after the initial deployment.

For your own customizations and a faster deployment in your own development pipelines, you can use the turboMode variable in config/config.json. When set to true, the deployment process will dynamically generate a cloudbuild.views.yaml file with each view in the right dependencies file (e.g., dependencies_ecc.txt or dependencies_s4.txt) as a single step of the build. This allows for a 10x faster deployment. The limitation is that if an error occurs when deploying a view, the build process will stop, and this is a slow way to find out all possible mismatches between the Cortex views and your datasource when compared to the turboMode=false option. If you are still fixing potential structure mismatches between the SELECT clauses in the views and the fields available in your replicated tables, TURBO=false will take longer but will attempt to generate all views even if one fails. This will help identify and fix more errors in a single run.

To file issues and feature requests against these models or deployers, create an issue in this repo.

The goal of the Data Foundation is to expose data and analytics models for reporting and applications. The models consume the data replicated from an SAP ECC or SAP S/4HANA system using a preferred replication tool, like those listed in the Data Integration Guides for SAP.

Data from SAP ECC or S/4HANA is expected to be replicated in raw form, that is, with the same structure as the tables in SAP and without transformations. The names of the tables in BigQuery should be lower case for cortex data model compatibility reasons.

For example, fields in table T001 are replicated using their equivalent data type in BigQuery, without transformations:

BigQuery is an append preferred database. This means that the data is not updated or merged during replication. For example, an update to an existing record can be replicated as the same record containing the change. To avoid duplicates, a merge operation needs to be applied afterwards. This is referred to as Change Data Capture processing.

The Data Foundation for SAP includes the option to create scripts for Cloud Composer or Apache Airflow to merge or “upsert” the new records resulting from updates and only keep the latest version in a new dataset. For these scripts to work the tables need to have a field with an operation flag named operation_flag **(I = insert, U = update, D = delete) **and a timestamp named recordstamp.

For example, the following image shows the latest records for each partner record, based on the timestamp and latest operation flag:

Data from SAP or Salesforce is replicated into a BigQuery dataset -the source or replicated dataset- and the updated or merged results are inserted into another dataset- the CDC dataset. The reporting views select data from the CDC dataset, to ensure the reporting tools and applications always have the latest version of a table.

Some replication tools can merge or upsert the records when inserting them into BigQuery, so the generation of these scripts is optional. In this case, the setup will only have a single dataset. The REPORTING dataset will fetch updated records for reporting from that dataset.

Some customers choose to have different projects for different functions to keep users from having excessive access to some data. The deployment allows for using two projects, one for processing replicated data, where only technical users have access to the raw data, and one for reporting, where business users can query the predefined models or views.

Using two different projects is optional. A single project can be used to deploy all datasets.

During deployment, you can choose to merge changes in real time using a view in BigQuery or scheduling a merge operation in Cloud Composer (or any other instance of Apache Airflow).

Cloud Composer can schedule the scripts to process the merge operations periodically. Data is updated to its latest version every time the merge operations execute, however, more frequent merge operations translate into higher costs.

The scheduled frequency can be customized to fit the business needs.

Download and open the sample file using gsutil from the Cloud Shell as follows:

gsutil cp gs://cortex-mando-sample-files/mando_samples/settings.yaml .

You will notice the file uses scheduling supported by Apache Airflow.

The following example shows an extract from the configuration file:

data_to_replicate:
  - base_table: adrc
    load_frequency: "@hourly"
  - base_table: adr6
    target_table: adr6_cdc
    load_frequency: "@daily"

This configuration will:

1. Create a copy from source_project_id.REPLICATED_DATASET.adrc into target_project_id.DATASET_WITH_LATEST_RECORDS.adrc if the latter does not exist
2. Create a CDC script in the specified bucket
3. Create a copy from source_project_id.REPLICATED_DATASET.adr6 into target_project_id.DATASET_WITH_LATEST_RECORDS.adr6_cdc if the latter does not exist
4. Create a CDC script in the specified bucket

SAP only: If you want to create DAGs or runtime views to process changes for tables that exist in SAP and are not listed in the file, add them to this file before deployment. For example, the following configuration creates a CDC script for custom table “zztable_customer” and a runtime view to scan changes in real time for another custom table called “zzspecial_table”:

  - base_table: zztable_customer
    load_frequency: "@daily"
  - base_table: zzspecial_table
    load_frequency: "RUNTIME"

This will work as long as the table DD03L is replicated in the source dataset and the schema of the custom table is present in that table.

The following template generates the processing of changes. Modifications, such as the name of the timestamp field, or additional operations, can be done at this point:

MERGE `${target_table}` T
USING (SELECT * FROM `${base_table}` WHERE recordstamp > (SELECT IF(MAX(recordstamp) IS NOT NULL, MAX(recordstamp),TIMESTAMP("1940-12-25 05:30:00+00")) FROM `${target_table}`)) S
ON ${p_key}
WHEN MATCHED AND S.operation_flag='D' AND S.is_deleted = true THEN
  DELETE
WHEN NOT MATCHED AND S.operation_flag='I' THEN
  INSERT (${fields})
  VALUES
  (${fields})
WHEN MATCHED AND S.operation_flag='U' THEN
UPDATE SET
    ${update_fields}

SAP Only: Alternatively, if your business requires near-real time insights and the replication tool supports it, the deployment tool accepts the option RUNTIME. This means a CDC script will not be generated. Instead, a view will scan and fetch the latest available record at runtime for immediate consistency.

The following parameters will be required for the automated generation of change-data-capture batch processes:

• Source project + dataset: Dataset where the SAP data is streamed or replicated. For the CDC scripts to work by default, the tables need to have a timestamp field (called recordstamp) and an operation field with the following values, all set during replication:
  • I: for insert
  • U: for update
  • D: for deletion
• Target project + dataset for the CDC processing: The script generated by default will generate the tables from a copy of the source dataset if they do not exist.
• Replicated tables: Tables for which the scripts need to be generated
• Processing frequency: Following the Cron notation, how frequently the DAGs are expected to run
• Target GCS bucket where the CDC output files will be copied
• The name of the connection used by Cloud Composer
• Optional: If the result of the CDC processing will remain in the same dataset as the target, you can specify the name of the target table.

If Cloud Composer is available, create connection(s) to the Source Project in Cloud Composer.

• For SAP deployments, create a connection named sap_cdc_bq.
• For Salesforce deployments, create a connection named sfdc_cdc_bq.

Notes:

• If you are deploying for both SAP and Salesforce, we recommend creating to create both connections assuming security limitations will be applied to each service account. Alternatively, modify the name of the connection in the templste prior to deployment to use the same connection to write into BigQuery as shone below.
• If you have Secret Manager Backend enabled for Airflow, you can also create these connections within Secret Manager under the same name.

The GCS bucket structure in the template DAG expects the folders to be in /data/bq_data_replication. You can modify this path prior to deployment.

If you do not have an environment of Cloud Composer available yet, you can create one afterwards and move the files into the DAG bucket.

Note: The scripts that process data in Airflow or Cloud Composer are purposefuly generated separately from the Airflow-specific scripts. This allows you to port those scripts to another tool of choice.

The deployment process can optionally flatten hierarchies represented as sets (e.g. transaction GS03) in SAP. The process can also generate the DAGs for these hierarchies to be refreshed periodically and automatically. This process requires configuration prior to the deployment and should be known by a Financial or Controlling consultant or power user.

Download and open the sample file using gsutil from the Cloud Shell as follows:

gsutil cp gs://cortex-mando-sample-files/mando_samples/sets.yaml .

This video explains how to perform the configuration to flatten hierarchies.

The deployment file takes the following parameters:

• Name of the set
• Class of the set (as listed by SAP in standard table SETCLS)
• Organizational Unit: Controlling Area or additional key for the set
• Client or Mandant
• Reference table for the referenced master data
• Reference key field for master data
• Additional filter conditions (where clause)

The following are examples of configurations for Cost Centers and Profit Centers including the technical information. If unsure about these parameters, consult with a Finance or Controlling SAP consultant.

sets_data:
#Cost Centers:
# table: csks, select_fields (cost center): 'kostl', where clause: Controlling Area (kokrs), Valid to (datbi)
- setname: 'H1'
  setclass: '0101'
  orgunit: '1000'
  mandt:  '800'
  table: 'csks'
  key_field: 'kostl'
  where_clause: [ kokrs = '1000', datbi >= cast('9999-12-31' as date)]
  load_frequency: "@daily"
#Profit Centers:
# setclass: 0106, table: cepc, select_fields (profit center): 'cepc', where clause: Controlling Area (kokrs), Valid to (datbi)
- setname: 'HE'
  setclass: '0106'
  orgunit: '1000'
  mandt:  '800'
  table: 'cepc'
  key_field: 'prctr'
  where_clause: [ kokrs = '1000', datbi >= cast('9999-12-31' as date) ]
  load_frequency: "@monthly"
#G/L Accounts:
# table: ska1, select_fields (GL Account): 'saknr', where clause: Chart of Accounts (KTOPL), set will be manual. May also need to poll Financial Statement versions.

This configuration will generate two separate DAGs. For example, if there were two configurations for Cost Center hierarchies, one for Controlling Area 1000 and one for 2000, the DAGs would be 2 different files and separate processes but the target, flattened table would be the same.

Important: If re-running the process and re-initializing the load, make sure the tables are truncated. The CDC and initial load processes do not clear the contents of the tables which means the flattened data will be inserted again.

These are the generic steps to use the Salesforce to BigQuery extraction module provided by Data Foundation. Your requirements and flow may vary depending on your system and existing configuration. You can alternatviely use other available tools.

You need to login as an administrator to your Salesforce instance to complete the following:

1. Create or identify a profile in Salesforce that
   • Is granted permission for Apex REST Services & API Enabled under System Permissions.
   • Is granted View All permission for all objects that you would like to replicate. For example, Account, Cases, etc. Check for restrictions or issues with your security administrator.
   • Is ideally not granted any permissions related to user interface login like Salesforce Anywhere in Lightning Experience,Salesforce Anywhere on Mobile,Lightning Experience User,Lightning Login User & etc. Check for restrictions or issues with your security administrator.
2. Create or use identify existing user in Salesforce. You need to know the user's user name, password, and security token.
   • This should ideally be a user dedicated to execute this replication.
   • The user should be assigned to the profile you have created or identified in Step 1.
   • You can see User Name and reset Password here.
   • You can reset the security token if you do not have it and it is not used by another process.
3. Create a Connected App. It will be the only communication channel to establish connection to Salesforce from the external world with the help of profile, Salesforce API, standard user credentials and its security token.
   • Follow the instructions to enable OAuth Settings for API Integration.
   • Make sure Require Secret for Web Server Flow and Require Secret for Refresh Token Flow are enabled in API (Enabled OAuth Settings) section.
   • See the documentation on how to get your consumer key (which will be later used as your Client ID). Check with your security adminsitrator for issues or restrictions.
4. Assign your Connected App created in Step 3 to the profile created in Step 1.
   • Select Setup from the top right of the Salesforce home screen.
   • In the Quick Find box, enter profile, then select Profile. Search for the profile created / identified in Step 1.
   • Open the profile.
   • Click the Assigned Connected Apps link.
   • Click Edit.
   • Add the newly created Connected App from Step 3.
   • Click on the Save button.

Note down User Name, Password, Secret Token and Client ID from steps above.

The Salesforce-to-BigQuery module relies on Google Cloud Secret Manager to work. This process is thoroughly documented in the documentation for Cloud Composer

Please create a secret as follows:

Secret Name:

airflow-connections-salesforce-conn

Secret Value:

http://<username>:<password>@https%3A%2F%2F<instance-name>.lightning.force.com?client_id=<client_id>&security_token=<secret-token>

Where User Name, Password, Secret Token and Client ID were noted from the steps above.

See the documentation on how to find your Instance Name.

To execute the Python scripts in the DAGs provided by the Data Foundation, you need to install some dependencies.

For Airflow version 1.10, follow the documentation to install the following packages, in order:

packaging~=21.3
requests~=2.28
urllib3~=1.26.8
apache-airflow-backport-providers-salesforce~=2021.3.3

For Airflow version 2.x, follow the documentation to install apache-airflow-providers-salesforce~=52.0.

Here is a command to install each required package:

$ gcloud composer environments update <ENVIRONMENT_NAME> \
    --location <LOCATION> \
     --update-pypi-package <PACKAGE_NAM><EEXTRAS_AND_VERSION>

For example,

$ gcloud composer environments update my-composer-instance \
    --location us-central-1 \
     --update-pypi-package apache-airflow-backport-providers-salesforce>=2021.3.3

Enable Google Secret Manager as the security backend. See details here.

Make sure your Composer service account (default: GCE service account) has Secret Manager Secret Accessor permission. See details in the access control documentation.

This source code is licensed under Apache 2.0. Full license text is available in LICENSE.