Analytics and Lakehouse for Oracle Applications Integration Options Explained

Analytics and Lakehouse for Oracle
Applications…IntegrationOptions Explained
Red Hot
Ray Fevrier
Analytics & Lakehouse Cloud Design Specialist
March 3rd, 2023
Contributors
• Morgan Russell
• Wilbert Poeliejoe
• Anis Zerelli
• Carmine Acanfora
• Alina Stuparu

The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
material, code, or functionality, and should not be relied upon in making purchasing decisions.
The development, release, timing, and pricing of any features or functionality described for
change and remains at the sole discretion of Oracle Corporation.
Safe Harbor Statement
Copyright © 2023, Oracle and/or its affiliates | Confidential: Internal
2

Agenda
Modern Data Platform
Fusion and EPM Data Extraction
Best Practices
Tipping our toes into Data Mesh
Specialists Assistance

4 Copyright © 2022, Oracle and/or its affiliates | Confidential: Internal
The Modern data platform is an enabler for successful data driven organizations.

Support Modern Data Platforms in OCI
with Lakehouse as the foundation
Red Hot
Jose Cruz
Analytics & Lakehouse Cloud Design Specialist
Leader
January 18th, 2022
OTube Link: https://otube.oracle.com/media/Red+Hot+-
+Support+Modern+Data+Platforms+in+OCI+with+Lakehouse+as+the+foundation/1_sk0c3ty3

Data Ecosystem & Conceptual Architecture
6
Data
Decisions
People
Processes
Decisions
generate data
Data influences
decisions
The enabler to support a distributed data economy
The data ecosystem is the overall
enabler for the data economy and it
is key to find and use the
hidden data capital.
The modern data platform is the technology enabler and provides c
apabilities to address several architectural styles used to find, curate a
nd use data capital.

Data Lake
 Agile, new data sets may be quickly
onboarded
 Any data, any history, cost effective storage
storage
 “Schema on read” for raw data, quick to add
quick to add new data sets
 Scalable storage, processing, and access
access
 Data repository for analytics, ML, data labs,
data labs, etc.
DataWarehouse
 Consistent, integrated data with data
model and constraints
 All analytical data in single place
 Strong emphasis on data quality and
consistency
 “Schema on write” to conform data from
sources
 SingleVersion of (analytical)Truth
Data Lakehouse
 Best of both worlds
 Combines benefits of Data Lake and Data
Warehouse:
 Agility, scalability, and costs of Data
Lake
 Data governance and SQL access of
DataWarehouse
 Offers close integration between Data
Warehouse and Data Lake
Building a Modern Data Platform with Data Lakehouse
Lakehouse Value Proposition

Oracle Lakehouse Reference Architecture
8
The data refinery is
responsible for the
ingestion, orchestration and
partially the transformation
into the persistence
platform.
The persistence platform
stores data in multiple layers
of storage, from economical
scale-out object storage, to
formalized data warehouse
and/or transactional systems.
Data enrichment occurs, data
quality is scrutinized, and the
data definition is formalized
as data moves between the
storage layers.
The access and interpretation
platform includes visualization
capabilities for analytics of
both static and streaming
data.
In addition, the results
of executed machine learning
models are curated and
exposed
for analysis.
Cloud data lake house - process enterprise and streaming data for analysis and machine learning
Overall concepts

9
Gold(curatedinformation)
This is where the past, current and future
future interpretation of enterprise
informationresides. In this the layer the
layer the data is structured to support
support agile access and navigation.
navigation.
Silver (curateddata)
In this layer that is has usually been
enriched and/or has passed stringent data
quality measures. The data is in an
immutable modelled form and Business
Process neutral.
Bronze(rawdata)
Data is loaded unchangedfrom sources.
Enrichment of the data would promote it
to the Curated data layers. Data in this
layer could be represented logically in the
Curated layers or queried for data science
purposes.
Data Persistency and Processing Layer

10
Real Time Ingest
Cloud
Storage
Analytics &
Visualization
AI Services
Governance
Streaming
Ingest
Machine
Learning
Bulk
Transfer
Batch
Processing
Batch Ingest
Serving Data
Store
Streaming
Processing
Hadoop
Ecosyste
m
APIs
Streaming
Analytics
Streaming
Analytics
Capabilities and Key Characteristics
 Inclusive of all data types
 Agile in using all data
 Wide range of processing
engines
 Flexible and modular
 Open and interoperable
 Faster innovation
 Highly available and resilient
 Scalable and elastic
 Secure and compliant by design
 Governed and trustable
 Cost efficient
 Adaptable to current needs
 Future proof
 Self serve to deploy and
consume
 Entice data democratization
 Embedded intelligence

11
Translytical Modern Data Platform
 What happened with
my business this
quarter?
 How that compares
with last year’s quarter?
 What is the trend of my
sales?
 How can I optimize my
costs and operations?
 How can I leverage
information to drive
business?
 How can I be
prescriptive based on
 How can I infuse
intelligence in my
processes?
 What if I change my
strategy?

 Can be deployed in across
public, hybrid and multicloud
 Interoperable with any
based system deployed on
prem or in 3rd party clouds
 Seamless integration with
services in Oracle Cloud,
including SaaS
 Leverage OCI features for
running and operating the
workloads
 Can be extended with OCI
Marketplace
 Can be extended with IaaS
services to address highly
heterogeneous workloads
 Can be part of a larger
Customer workload
 Leverages implicitly all OCI
capabilities
3/10/2023
Copyright © 2021, Oracle and/or its affiliates | Confidential: Internal/Restricted/Highly Restricted
12
Compute Storage Networking Oracle
Databases
Open Source
Databases
Operating Systems,
Native VMWare
Developer
Services
Containersand
Functions
Application
Integration
Data Lakehouse
Machine Learning
and AI
Analyticsand BI
Oracle Applications Custom Applications
Global Cloud Datacenter Infrastructure
Public Cloud Regions | Hybrid Cloud: Cloud@Customer, Dedicated Regions, Roving Edge | Multicloud: Azure, AWS
Security | Observability | Compliance | Messaging | Governance
ISV Applications
Part of a wider cloud infrastructure and platform

Fusion and EPM Data Extraction

Fusion and EPM (ERPM) Logical Architecture
14
A Few Key Points…
 Interaction with EPM Cloud is done via REST
APIs.
 EPM Automate is a REST API wrapper that
allows users to interact with their EPM pods;
however, it needs to be installed on-prem or in
a VM.
 Preferred method to extract data from Fusion is
through Business Intelligence Cloud Connector
(BICC).
 Oracle Analytics Cloud (OAC) is the only BI tool
able to connect directly to both ERP and EPM.
 Data Persistency is highly recommended to
reduce the dependency on the SaaS system,
especially when doing historical analysis. Also
helps with API throttling.

Oracle Analytics Cloud (OAC)
15
The Oracle Analytics platform is a cloud native service that
provides the capabilities required to address the entire analytics
process including data ingestion and modeling, data preparation
and enrichment, and visualization and collaboration, without
compromising security and governance.
 Embedded machine learning (ML) and natural language
processing (NLP) technologies help increase productivity and
build an analytics-driven culture in organizations
 Oracle Analytics supports a hybrid deployment strategy (start
on-premises or in the cloud)
 Connectors to dozens of data sources including: Amazon
Redshift, Fusion Apps, EPM Cloud, Microsoft SQL Server,
Snowflake, Autonomous Oracle Database etc.
 Data Flows and Pipelines help move and transform data
across Analytic pipeline stages, built-in to OAC flows
 Data Replicator, a user friendly (vs most data integration)
data replication tool, allows users to copy data from
Fusion to ADW
First choice for OAC centered use cases

Autonomous Data Warehouse (ADW)
16
First choice for ADB centered use cases
Autonomous Database (ADB) is a fully automated database service that makes it
easy for organizations to develop and deploy application workloads regardless of
complexity, scale, or criticality. ADB’s Autonomous Data Warehouse (ADW)
supports analytics and warehouse workloads.
 Data Transform (powered by Oracle Data Integrator) provides a powerful ELT
engine that is heavily optimized for Oracle SQL and database utilities
 Analytic Views provides a single version of the truth by creating a single
definition of structure and calculation rules (aggregations, measures) in the
database
 Defines relationships between dimension and fact tables (star schema)
 Defines the business model (sematic layer) and calculations expressions
 Primary use cases include: visualization agnostic, enhance data sets
for OAC, application development using APEX
 Process data from multiple sources/Clouds
 Autonomous Databases (ADW, ATP…)
 Cloud Storage: OCI Object Storage, AWS Simple Storage Service (S3),
Azure Data Lake Storage (ADLS)
 ADW processes data: Flat Files (CSV, Parquet, JSON, Avro, ORC)

Oracle Integration Cloud (OIC)
17
A visual application integration and automation tool that
provides prebuilt connectivity to SaaS and on-premises
applications
Benefits
 A run-ready process automation templates with drag and
drop designers
 A low-code visual builder for web and mobile application
development.
 Business insight across end-to-end digital processes
 B2B capabilities for EDI and secure file transfer
 Machine learning recommendations for easy data
mapping
Key Highlights
 Mainly used as an orchestration tool or for application to
application integration
 Files managed by reference up to a maximum size of
1GB
 Ideal when interacting with applications using REST API

Oracle Data Integrator (ODI)
18
Next Generation Architecture
“E-L-T”
Transform Transform
Extract Load
ADW Spark Hive HBase Pig Sqoop
BICC
Oracle
Datapump
Oracle
DBLink
JMS
External
Tables
Teradata
SAP Siebel
eBusiness
Suite
IBM DB2 Netezza SCD
Bulk Data
Transformation
Most Apps,
Databases
& Cloud
Bulk Data
Movement
Cloud
DBs
Big
Data
Oracle Data Integrator (ODI) provides data migration with its innovative
extract, load, and transform (E-L-T) technology, that is optimized for
most on-premises and cloud databases.
Original E-LT Innovator
 E-L-T approach allows users to perform transformations at either
source or target
 E-L-T provides a flexible architecture for optimized performance on
any platform
 ODI provides high performance bulk data movement, massively
parallel data transformation using database or big data
technologies, and block-level data loading that leverages native
data utilities
Benefits
 Prebuilt connectors for many databases and technologies
 Fewer network hops which improves performance for loading
 Takes advantage of existing infrastructure
 Pluggable Knowledge Module Architecture
 Available on-prem and OCI MarketPlace

OCI Data Integration (OCI DI)
19
First choice for OCI platform-wide use cases
What is it?
OCI DI is a fully-managed, cloud native ETL tool that simplifies complex data extract,
transform, and load processes (ETL/E-LT) into data lakes and warehouses for data
science and analytics with a modern, no-code dataflow designer.
Key Features
 Intuitive, no-code, graphical user interface that makes it easier to design and
manage data flows.
 Easy-to-use graphic designer that automatically generates execution code and
provides visualize dataflow prior to load
 Pay As You Go pricing which makes OCI DI cost effective (no need to invest in
new servers and software)
 Reusable templates and dataflows
 Data flow validation
 Native integration with Oracle Cloud Infrastructure and SaaS
 Rule-based design protects from schema drift by handling schema changes
dynamically
 Innovative Optimizer for Spark ETL and pushdown E-LT

Fusion Data Extract - All Options Explored
20
Using BICC and OAC Replicator
The OAC Replicator allows native connectivity to Fusion SaaS
1. OAC calls BICC to request Public View Objects (PVOs) extracts
to Object Storage
2. BICC extracts the PVOs to Object Storage
3. OAC will read the BICC extracts from Object Storage and load
them to ADW
Using BICC and ODI MP or OCI DI
The ODI Marketplace and OCI Data Integration both have a native
connector to extract data from Fusion SaaS. These connectors use
BICC to extract data into Object Storage.
1. DI tool calls BICC to request PVOs extracts to Object Storage
2. DI tool leverages the target database technology for BICC extract
data transformations and ingestion into ADW.
Using ADW Self-Service E-L-T
Note: ADBs can ingest files from Object Storage or from local system.

Overview of Business Intelligence Cloud Connector (BICC)
21
About BICC
 BICC is the preferred method to extract business intelligence and other data in
bulk and load it into designated external storage areas.
 BICC is available as part of the Oracle Applications Cloud subscription.
 User provisioning to perform tasks related to data extraction is done in BICC.
Key Features of BICC
 Configure an external storage location that works for your data needs.
 Extract complete or partial data. You can select offerings or specific objects.
 Run extracts on-demand or schedule them to run at specified intervals during the
day, in a week, or throughout the month.
 Run incremental extracts if you need only the data that changed since your last
extract.
 Schedule multiple independent extracts at convenient intervals.
 Monitor extracts and review logs.
 Export configured offerings and associated data stores.
 Manage refresh metadata and specify dates for incremental refresh comparison.
First choice for Fusion centered use cases

Load and transform data from Oracle Fusion Cloud Applications
to build a data lake or data warehouse
22
Loading and transforming data
from Fusion into Object Storage
(data lake) in Parquet format and
in Autonomous Data Warehouse
using OCI Data Integration.
Load and transform data from Oracle Fusion Cloud Applications to build a data lake or data warehouse

EPM Cloud Data Extract - All Options Explored
23
EPM Automate and ODI Marketplace
We can use EPM Automate and ODI MP to:
1. After installing EPM Automate and ODI MP in a VM.
2. ODI can orchestrate the a REST call (through EPM Automate) to
EPM, extract the data then download the file locally.
3. ODI can then load the data into ADW.
Oracle Integration Cloud or OCI Data Integration
Both OIC and OCI DI can make REST calls directly to EPM and perform
the following tasks:
1. Extract the data from EPM Cloud into EPM Outbox folder in a zip file.
2. Copy to the zip file from the Outbox folder to Object Storage
3. Unzip the file
4. Load the data into ADW
Using ADW Self-Service E-L-T
Note: ADBs can ingest files from Object Storage or from local system.

About REST API for Oracle EPM Cloud
24
REST APIs allow service administrators and infrastructure consultants to perform
administration tasks in EPM Cloud.
EPM Cloud services with REST API capabilities:
 Planning (PLN)
 FreeForm (FF)
 Planning Modules
 Account Reconciliation (ARCS)
 Financial Consolidation and Close (FCC)
 Enterprise Profitability and Cost Management (EPCM)
 Tax Reporting (TR)
 Strategic Workforce Planning (SWP)
 Narrative Reporting (NR)
 Oracle Enterprise Data Management Cloud (EDMC)
 Data Management (DM)
Users can programmatically interact with EPM Cloud using:
 REST APIs
 EPM Automate Utility
 Groovy business rules (see Oracle Enterprise Performance Management Cloud Groovy
Rules Java API Reference)

EPM Automate
25
What is it?
EPM Automate is a command line utility that enables users to remotely interact
with their EPM applications and perform various repeatable tasks, such as:
 Import and export metadata and data
 Upload and download files,
 Run Data Management integrations
 Etc.
Key points regarding EPM Automate:
 Most interaction with EPM Automate is via a scripting language (python, bash,
batch…)
 Integrate with other scripts (such as Python) for end-to-end processes
 Orchestration, NOT an ETL tool
 You can create scripts that are capable of completing a wide array of tasks
and automate their execution using a scheduler.
 EPM Automate would need to be installed in a VM on OCI
Sample Usage: Copy a file to an Object Storage bucket:
 epmautomate copyToObjectStorage SOURCE_FILE_NAME USERNAME PASSWORD URL
 epmautomate copyToObjectStorage example_file.txt oracleidentitycloudservice/jDoe
example_pwd https://swiftobjectstorage.us-ashburn-
1.oraclecloud.com/v1/axaxnpcrorw5/bucket-20210301-1359

EPM Cloud Data Replication into FAW’s ADW Using ODI Marketplace
26
Data Flow for this sample architecture
1. An EPM Extract will be created in EPM Cloud
2. EPM Automate and ODI MP will be installed in the same Linux
VM
3. ODI will call EPM Automate to login to EPM and execute the
extract job from EPM Cloud to store it in EPM Cloud as a ZIP file
format (containing mutliple CSV files)
4. ODI will call EPM automate to download the EPM Extract ZIP file
and decompress the EPM CSV extract files, merge them into one
single CSV and load them in ADW
5. All further transformations (unpivoting, aggregation, lookups, soft
deletes,...) will happen inside the ADW as subsequent ODI
processes (not documented as part of the scope of this blog).
6. EPM will be configured in such a way that EPM data can be
extracted on the convenient schedule
7. Semantic model will be extended by customizing an existing
subject area or addition of a new subject area to allow reporting
on EPM data extracted in ADW, as per the CEAL Team blog
available here.
Reference Architecture - EPM Cloud Data Replication into FAW ADW : Using ODI Marketplace

EPM to ADW: OIC Flow
27
Features
 Scheduled to run via cron expression or ad-hoc
 ADW bulk load for improved performance
 Wait for the completion of the EPM jobs
 Files managed by reference up to a maximum size of 1GB
Components
 Integrations: export data, export metadata
 Connections: EPM (REST API), ADW

Comprehensive Data Integration in Oracle Cloud
28
Focus /
When to Use:
Oracle ERP /
SaaS
Social & 3rd
Party Apps
Ingest
Data Prep &
Transforms
Analytics /
Warehouse
OAC Data
Prep/Replicator
Analytics Cloud focus, not for
IT/Enterprise DI  Salesforce
  
ADB Data Integrator
Autonomous DB focus, for ELT
processing in ADB  SAP ABAP
  ADW
OCI Data Integration
Obj Store and Data Science
focused ETL/ELT    
OCI Data Flow
Serverless Spark, use with OCI-DI
or for bespoke ETL  data science
OCI Streaming
Message based ingestion
using Kafka APIs  event based
OCI GoldenGate &
Stream Analytics
CDC, Replication &
Streaming ETL + Analytics * SAP ECC DB
  
Oracle Integration
Cloud
As MDW supplement
for Social / SaaS Feeds    message payloads
Data Integrator
(Not an OCI Native application but is
available via Marketplace)
Innovator and leader
of ELT data processing  SAP ABAP
 
Oracle &
3rd Party
Integrations
* Limited availability and requires GVP approval

Best Practices

OAC Replicator vs Oracle Data Integrator (ODI)
30
• No additional cost for OAC
• End-to-end process (Fusion -> Object Storage -> OAC -> ADW)
• User friendly (vs most data integration tool)
• Little to no data transformation needed
• Data Movements are happening within the Oracle Network (Fusion pushes
the data to Object Storage)
• Supports incremental extracts
• PaaS component, limited to no additional maintenance (automated patching)
• Allows user to schedule tasks
• Data flows through OAC instance
• Not recommended for high data volumes
• Target has to be an Oracle DB in the Cloud (on-prem DB on the roadmap)
• Limited orchestration and error handling capabilities
• Notifications only on the BICC side
• Limited source systems
Option A: OAC Replicator
• Enterprise Data integration platform for a more strategic approach to DWH
• High performance for bulk load
• Data transformation happens at the same time as the load
• Supports the use of process design through workflows and email
notifications and scheduling
• Native integration with Fusion SaaS and Oracle Databases (ADB, DBaaS,
on-prem DBs…) and many other source systems.
• Native Integration with 50+ target data store (Cloud or on-prem), including
Azure SQL, Snowflake
• Data Movements are happening within the Oracle Network when using
Object Storage as the external storage for BICC Extracts (Fusion SaaS
pushes the data to Object Storage and target is in OCI)
• Reads BICC Extracts data from Object Storage
• Provisions in OCI MarketPlace, require VM.Standard2.4 shape or higher VM
Image
• Less intuitive, complex configuration, product knowledge required.
• IaaS components, requires planning/resources for patching and upgrades
Option B: ODI MarketPlace

OAC Replicator vs OCI Data Integration (OCI DI)
31
• No additional cost for OAC
• End-to-end process (Fusion -> Object Storage -> OAC -> ADW)
• User friendly (vs most data integration tool)
• Little to no data transformation needed
• Data Movements are happening within the Oracle Network (Fusion pushes
the data to Object Storage)
• PaaS component, limited to no additional maintenance (automated patching)
• Allows user to schedule tasks
• Data flows through OAC instance
• Not recommended for high data volumes
• Target has to be an Oracle DB in the Cloud (on-prem DB on the roadmap)
• Limited orchestration and error handling capabilities
• Notifications only on the BICC side
• Limited source systems
Option A: OAC Replicator
• OCI Data Integration (OCI DI) has a fully-managed serverless ETL
architecture that reduces maintenance efforts and auto scales to manage
unpredictable data workloads
• Innovate faster by simplifying integration into data warehouses and data
lakes to make data-driven decisions
• Integrate more easily with a graphical, no-code user experience and preview
data to see the results of a transformation
• Rules-based integration enables ETL developers and data engineers to
handle schema evolutions
• Discover and connect quickly to popular databases, data lakes and
applications which allows you to quickly prepare data sets for data science
projects
• Hybrid execution powered by Spark ETL or push-down E-LT
• Store and process data on the same network reduces data latency and
improves business performance
• Complements other OCI data and AI services (OCI Data Catalog, OCI Data
Flow, OCI Data Science) to manage data lakes
• Only supports Oracle Cloud Infrastructure data management
• Launched in 2020, may have limited features
Option B: OCI DI

Fusion Data Extraction Guidelines from Oracle A-Team
32
 Explore available data extract/export options and choose the right one based on your requirements.
 Understand the supported objects and volume/size limitations from pillar (HCM / CX / ERP / SCM)
documentation based on your extract option.
 Consider the mode of integration (batch or real-time), type of integration (synchronous or asynchronous),
frequency (daily, weekly, monthly, …), data volume (# of records to be retrieved/processed), expected file size,
storage/retrieval options, duration, and performance while choosing the extract option.
 Document the data extract requirements and design the solution by considering usage of data, automation, and
performance.
 Do not use OTBI for data extract. Note that OTBI is a reporting tool and not recommended for synchronous
integrations. OTBI analysis has limitations on 25,000 records for exporting to Excel.
 Avoid developing BIP reports using custom SQLs for data extracts and integration requirements. Consider
performance, timeout, file size, and formatting if you are developing custom BIP reports.
 BICC is the recommended option of extracting bulk data for ERP, SCM, CX in batch out of Fusion Cloud
Applications for external applications/data warehouse/reporting. BICC supports incremental extract. For HCM
BICC is used only with Fusion Analytics Warehouse and HCM analytics.
Data Extraction Options and Guidelines for Oracle Fusion Applications Suite
 Consider Security, compliance, data privacy, encryption requirements while designing the data extraction/replication solution.
 Leverage Oracle Cloud Infrastructure Cloud (OCI) and Oracle Integration Cloud (OIC) capabilities for orchestrating and automating the data extracts and integrations.
 Avoid using REST API / SOAP services for extracting/exporting high-volume data set from Oracle Fusion Cloud Application. REST API / SOAP services are recommended only for
real-time integrations.
 Always refer to the latest documentation to get updates on features.
 Research cloud customer connect (https://community.oracle.com/customerconnect/)if you find gaps and create ideas as needed.
 Log a Service Request with Oracle if any of the above extract options don't meet your needs to get the right guidance.

Implementation Best Practices for EPM Cloud REST APIs
33
Use the implementation best practices listed in this topic when working with the EPM Cloud
REST APIs.
Best practices:
• Before using the REST APIs, complete the prerequisites.
• Use the correct authentication, as described in OAuth 2 and Basic Authentication for EPM
Cloud REST APIs.
• Understand the URL structure.
• Know how to get the current REST API version.
• Review the sample scenarios to get started quickly.
• Be aware of REST API compatibility.
• Use the Quick Reference to find all of the Oracle Enterprise Performance Management
Cloud REST APIs at a glance.
• Use API development tools (i.e. Postman…)

Best Practices
34
Leverage the best set of services for the specific use case – use as needed
 OCI has an extensive set of services and capabilities,
continuously being improved, that can address any
workload; use as needed, to address the specific use case
 Leverage serverless services as much as possible (less
operational overhead, increased chance of success)
 Leverage a just enough approach (don’t simplify, don’t over
complicate)
 Data storage should be decided based on use case (for
telemetry in Object Storage, financial data in ADW)
 Lakehouse data should be organized leveraging a
medallion architecture (bronze, silver, gold) or similar for
governance and security
 For schema on write data understand what data
modelling works best for the use case; traditionally a star
good option as it is widely supported by OCI services
 Leverage data ingestion and processing engines that
better support requirements (for timely data feeds leverage
CDC and real time, for massive data processing leverage Spark
scale out processing,…)
 Don’t use a single use case pattern to address all
the law of the hammer as what works for a use case might not work
for another)

Tipping our toes into Data Mesh
Exploring the art of possible

Unravel Data Mesh
Future of Decentralized Information Management
RED HOT
Jakub ILLNER
Analytics & Lakehouse Cloud Design Specialist Leader
Technology Cloud Engineering, EMEA
19 January 2023
10.03.2023
36
OTube Link: https://otube.oracle.com/media/1_b82m570m

10.03.2023
37
Data Mesh is a decentralized, organizational
and architectural* approach to share and
manage analytical data in complex and large-
scale environments – within and across
organizations.
From “Data Mesh – Delivering Data-Driven Value at Scale” by Zhamak Dehghani, published in March 2022 by O’Reilly
* Zhamak Dehghani used term “Sociotechnical”, instead of “Organizational and Architectural”.

Data Mesh Principles
38
• Enterprise Data integration platform for a more strategic approach to DWH
• High performance for bulk load
• Data transformation happens at the same time as the load
• Supports the use of process design through workflows and email
notifications and scheduling
• Native integration with Fusion SaaS and Oracle Databases (ADB, DBaaS,
on-prem DBs…) and many other source systems.
• Native Integration with 50+ target data store (Cloud or on-prem), including
Azure SQL, Snowflake
• Data Movements are happening within the Oracle Network when using

Characteristics of Usable Data Products
39
Discoverable
Data must be easily
discoverable, e.g., by using
company wide data catalog
with all data products and
related metadata
information.
Addressable
Data must be reachable via
long lasting unique address,
using common addressing
conventions for polyglot data
types.
Understandable
Data product semantics,
structures, and technical
features must be described
in a form that is easily
accessible and
understandable.
Trustworthy
Data product guarantees
service level objectives
(SLOs) that include change
frequency, timeliness, shape,
granularity, quality,
completeness, etc.
Natively Accessible
Data must be accessible
over formats suitable to
customer requirements
(stream, file, table, API, etc.),
possible by using polyglot
stores.
Interoperable
Data products must comply
with harmonization rules that
allow correlation across
domains (identifiers, schema
compatibility, metadata, etc.).
Valuable on its Own
Data product must be
valuable and meaningful to
business; technical objects
should be hidden from users
of data products.
Secure
Access to data product must
be protected via access
control. Data must be
encrypted and protected
according to its
classification.

Examples of Data Products on OCI
40
Streaming database events as data
products
 OCI GoldenGate to ingest database
transactions
 OCI Streaming as event store
 OCI GoldenGate Stream Analytics to
curate and transform data streams
 OCI Object Storage for archive of
events (for auditing, logging, re-
processing)
Publishing data from Fusion Apps as
data products
 OCI Data Integration invokes BICC,
which exports data to OCI Object
Storage
 OCI Data Integration transforms and
aggregates BICC data into target
dimensional data model
 Autonomous Data Warehouse
manages data and provides query
services
eBS
JDE
Psoft
…

Specialists Assistance
How Cloud Solution Specialists can assist with Lakehouse workloads?

Cloud Solution Specialists – Analytics & Lakehouse Design
42
We are here to assist you and your Customer on their journey to OCI
 We provide assistance throughout
the complete sales cycle with
discrete activities
 We partner and collaborate with
Product Management to create
assets to be reused with
Customers
 We engage and help close Oracle
Data Platform & Lakehouse
workloads
 We leverage years of expertise in
Analytics & Lakehouse and
combine it with best practices to
bring value to your Customers
Check out our confluence page for
more information and reusable assets
1. Workload
Qualification
2. Workload
Validation
3. Workload
Confirmation
4. Adoption 5. Consuming
Workshop
Workload
Solution Assistance
Architecture Design
Healthcheck
Workload Architecture
Solution Assistance
Lift Implementation Design
Healthcheck
Workload Architecture

Useful URLs
43
Oracle Analytics Cloud (OAC)
 Oracle Analytics Cloud - Get Started: https://docs.oracle.com/en/cloud/paas/analytics-cloud/index.html
 Oracle Analytics Cloud and Server Roadmap: https://www.oracle.com/business-analytics/cloud-and-server-roadmap.html
 What’s New for Oracle Analytics Cloud: https://docs.oracle.com/en/cloud/paas/analytics-cloud/acswn/index.html#ACSWN-GUID-CFF90F44-BCEB-49EE-B40B-
8D040F02D476
 Create Services with Oracle Analytics Cloud: https://docs.oracle.com/en/cloud/paas/analytics-cloud/acoci/create-services.html#GUID-47022452-65CC-4345-
8F7F-A447BB24A48A
 SaaS Data Replication in Oracle Analytics Cloud (OAC): https://www.ateam-oracle.com/saas-data-replication-in-oracle-analytics-cloud-oac-and-oaac
Fusion Cloud
 Creating a Business Intelligence Cloud Extract: https://docs.oracle.com/en/cloud/saas/applications-common/21b/biacc/get-started.html#get-started
 OCI Data Integration (ODI DI) Help Center: https://docs.oracle.com/en-us/iaas/data-integration/using/index.htm
 Load and transform data from Oracle Fusion Cloud Applications to build a data lake or data warehouse: https://blogs.oracle.com/cloud-
infrastructure/post/load-and-transform-data-from-oracle-fusion-cloud-applications-to-build-a-data-lake-or-data-warehouse
Enterprise Performance Management (EPM)
 EPM Automate - Copying a Snapshot to or from Oracle Object Storage: https://docs.oracle.com/en/cloud/saas/enterprise-performance-management-
common/cepma/sample_script_15_object_store.html
 REST API for Oracle EPM: https://docs.oracle.com/en/cloud/saas/enterprise-performance-management-common/prest/index.html
 Working with EPM Automate for Oracle EPM: https://docs.oracle.com/en/cloud/saas/enterprise-performance-management-common/cepma/index.html

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data in new ways, discover insights,
unlock endless possibilities.

Analytics and Lakehouse for Oracle Applications Integration Options Explained

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