Validation

Content validated on 05/08/2023 with

• ADW Version Oracle Database 19c Enterprise Edition Release – Production Version 19.11.0.0.0

• Oracle Cloud Application 22C (11.13.22.07.0)

• Fusion Analytics Warehouse Application Version  23.R1

• Snowflake instance

 Background

Oracle Fusion Analytics provides analytics for Oracle Cloud applications, powered by Autonomous Data Warehouse and Oracle Analytics. More specifically:

• Fusion ERP Analytics provides accounting data sourced from ERP Cloud in a warehouse designed to support broad set of analytics use cases. 
• Fusion HCM Analytics provides comprehensive workforce, turnover data sourced from Fusion HCM Cloud.
• Fusion SCM Analytics give customers the ability to gain visibility into Supply Chain performance, correlate Supply Chain processes with business goals and detect, understand and predict Supply Chain issues.

Making the right business decisions is intrinsinclty tied to the system of records data accuracy of a company. In many specific use cases, Fusion Analytics data can be complemented with additional data from external source such as On Premise, Legacy applications, Non Fusion SaaS application, OCI source, Third party cloud applications, and so on…

To support these data enrichment and consilidation requirements, Fusion Analytics is introducing a new Connectors to leverage the Fusion Analytics Oracle data pipeline infrastructure to extract data (full and incremental) from these external sources following a defined schedule, potentially reducing custom data extraction code, infrastructure and platform costs.

This blog describes how to utilize Fusion Analytics (FAW) Data Augmentation feature to replicate transaction data from Snowflake Warehouse into ADW to extend the Data Layer of FAW. 

Suggested Prerequisite Reading Material:

* Data Augmentation with FAW 

* Data Replication with OAC 

* FAW Semantic Model Customization

Overall prerequisites

* Have access to an Fusion Analytics Instance (OAC, ADW) with Administrative privileges

* Have access to a Snowflake Account

* Have worked with Fusion Analytics Support to open egress Port 80 on Fusion Analytics tenant (Service Request)

* Have configured SQL level access to Fusion Analytics ADW, as described in Krithika and Gunaranjan blog post here.

 Architecture Overview

FAW Data Augmentation allows customers to leverage the FAW data pipeline to augment reports with datasets created :

1. By adding a new dimension in the target instance,
2. By adding a new fact in the target instance.

The diagram below details the architecture for the Connectors :

Figure 1: FAW Data Augmentation flow with Snowflake Connectors

The high level configuration flow is as follow:

• Snowflake Configuration

  • Install snowsql on your local environment

  • Create RSA Key Pair for Authentication

  • Update the Snowflake user to assign the public key (RSA_PUBLIC_KEY) 

  • Test the RSA key

• FAW Configuration

  • Enable Snowflake feature

  • Create Snowflake Connection

  • Test Connection

  • Refresh metadata for Snowflake connection

  • Set Pipeline Settings

  • Create Snowflake data augmentations

• FAW Data Pipeline processes the data augmentation pipeline jobs, extract the data from Snowflake tables and load it into ADW, queryable via synonyms starting with DW_SFLK_X

• Augmented data can be then queried directly using OAC Data set or a SQL client like SQL developer, through the database synonym named after the table name give in the first step 

• Semantic model can be extended by customizing an existing subject area or addition of a new subject area to allow reporting on augmented data extracted in ADW. This will require the creation of database objects (views and grants). We will cover the basics here to be able to build a visualization against the extracted Snowflake Data.

Snowflake and Fusion Analytics Configuration

Let’s watch the video

Figure 2: Snowflake and Fusion Analytics Configuration

Accessing an OAC Workbook to Gain Business Insight with Snowflake Data

Now that the Data Augmentations have run,  we can create Snowflake Data Sets and visualization in OAC.

Figure 3: Snowflake visualization

This concludes the activities in the blog.

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 Summary

This blog described how to utilize Fusion Analytics (FAW) Data Augmentation Connector to replicate transactions data from Snowflake Cloud Applications into Autonomous Data Warehouse (ADW) to extend the Data Layer of FAW. This blog also detailed the steps to extend the semantic model to ultimate allow customers to answer business questions and gaining business insight with Snowflake and Fusion Analytics data.

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