Integration with the o9 Platform - A Step-by-Step Guide
This video explains a flexible and fully featured integration platform. It supports all forms of data ingestion from batch transfers via SFTP, rest API, soap XML, and streaming data. Also, o9 has connectors and mapping templates to connect to ERPs and enterprise systems, including SAP Snowflake, Oracle, Google BigQuery, and many more.
The integration process involves setting up the connection, data mapping, ingestion logic, and oversight and monitoring. The o9 integration framework connects many types of sources from ERP systems to cloud data stores with structured and unstructured data.
In this video, we will show how to set up a data load from SAP, but the same steps apply for other ERPs and data sources.
o9 has a flexible and fully featured integration platform.
Supporting all forms of data ingestion from batch transfers via SFTP, rest API, soap XML, and streaming data. o9 has connectors and mapping templates to connect to ERPs and enterprise systems, including SAP Snowflake, Oracle, Google BigQuery, and many more.
Let's look at a typical o9 integration process.
Here we will set up a data load from SAP, but the same steps apply for other ERPs and data sources.
There are four basic steps: setting up the connection, the data mapping, the ingestion logic, and finally the oversight and monitoring of the process.
The connection to SAP requires minimal configuration on the SAP side.
The o9 adapter monitors the SAP change logs and extracts the data for transfer via SFTP or API.
The o9 platform ingests the data via platform staging APIs.
The o9 data staging layers prepares transforms and optimizes the data for loading into o9's graph cube data model.
The first step is to set up the connection and configured the data transfer.
o9 supports SFTP, rest API, and streaming data protocols.
Within SAP, the target tables and frequency are configured for extraction and upload.
Data mapping, filtering and conditional logic is set up within the o9 integration framework.
Data mapping, transformation, normalization, error handling, and outlier detection take place within o9's data staging layer. This is also where the data is formatted and optimized for EKG's graph cube data architecture.
The data staging layer is where data is cleansed, transformed, and ultimately optimized for Graph cube store.
The graph cube allows o9 to model relationships as well as aggregations of data elements and provides high performance through hybrid queries, of cube nodes and graph relations.
Once the data is loaded into the graph cube EKG, it is directly accessible through o9's user interface.
The data can be displayed in many ways, according to user requirements, and the graph cube structures can be configured and manipulated if required to align with any specific data requirements.
Oversight and monitoring of the integration process is built into the o9 platform.
Alerts are sent to admin users and can be configured to track key steps in the integration and transformation process.
Metrics and statistics give visibility on performance over time.
o9's integration framework connects many types of sources from ERP systems to cloud data stores with structured and unstructured data.
o9 supports batch ingestion for large master datasets as well as real time integration via APIs for transactional data.
o9's data staging area and transformation logic brings all of these types of data together in o9's in memory graph cube EKG.
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