Enterprise Data Platform Modernization

EDP reference architectures are typically designed to be modular and scalable, so that organizations can leverage the components and features that meet their specific needs. They are also designed to be cloud-agnostic, so that organizations can deploy their EDP on the cloud platform of their choice. Kloud9 partners with all the major cloud providers. The overall architecture is based on specific tools and processes for data security, governance and monitoring.

This architecture shows that the data analytics layer is built based onthe data processing and storage layers, and the data ingestion layer is at the beginning allowing data to flow seamlessly from ingestion to processing to storage and finally to analytics and data science.

• Data ingestion: This layer is responsible for collecting and bringing data into the platform from a variety of sources, such as on-premises databases, cloud applications, and sensors.
• Raw Data Store: This layer is responsible for storing and managing the data in a way that is accessible, efficient, and scalable.
• Data Enrichment: This layer is responsible for cleaning, transforming, and loading the data into various formats and destinations, such as data warehouses, data lakes, and machine learning models.
• Enterprise Data Store Layer: This layer is responsible for establishing and enforcing policies and procedures for managing data throughout its lifecycle, including data security, privacy, and compliance.
• Data Analytics and Visualization: This layer provides the tools and capabilities for users to develop machine learning models, analyze and visualize data to gain insights and make informed decisions.

Organizations can use this reference architecture as a starting point to design and build their own enterprise data platform. They can add or remove components and features as needed to meet their specific requirements.