Enterprise Data Platform

Modernize Your Enterprise Data Platform and Unleash Its Full Potential

Your enterprise data platform (EDP) is the foundation of your digital transformation journey. But if it is outdated, siloed, or complex, it is likely holding you back.

Kloud9s data platform modernization service can help you overcome these challenges and unlock the full potential of your EDP. We work with you to:

  • Assess your current EDP landscape and identify areas for improvement.
  • Understand your data storage and retrieval systems with data consumption needs.
  • Develop a customized modernization plan that meets your specific needs and budget.
  • Migrate your data to a modern, well architected private or public cloud-based platform.
  • Implement industy leading practices for data governance, data quality, and data management.
  • Train your team on how to use the new platform and get the most out of your data.

As a result, you'll have a modern, agile data platform that can support your evolving business needs and deliver the insights you need to make better decisions faster.

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Benefits of Modernizing Your Data Platform

  • Improved performance and scalability: Handle more data and traffic, so you can keep up and scale with the demands of your business.
  • Reduced costs: A cloud-based EDP can help you save significant costs on hardware, software, and maintenance costs.
  • Increased agility: Easier to add new data sources, applications, and users, so you can respond to change quickly.
  • Improved data quality and governance: Provides tools and features you need to ensure your data is accurate, complete, and secure.
  • Enhanced data accessibility and insights: Democratize access to data and insights with easier access to data and analytics, so you can get the insights you need to make better and faster decisions.
  • Serve as the Foundation for Artificial Intelligence Capabilities: Build, experiment and deploy AI-at-scale within your enterprise with predictive models, hyper-personalized experiences and stay a step beyond your competitors.

Key Considerations

Data Warehouse

  • Start with a well-defined data model
  • Plan ETL processes carefully
  • Optimize query performance
  • Ensure data accuracy & consistency
  • Protect sensitive data
  • Plan for scalability

Data Lake / Lake House

  • Decide between schema-on-write & schema-on-read
  • Organize data into directories & use partition keys
  • Implement data quality checks & maintain metadata
  • Secure data lake storage & control access
  • Utilize data catalogs & document data lineage

Data Mesh

  • Data owned by business domains
  • Empower domain teams for data management
  • Treat data as a product aligned with business needs
  • Prioritize data quality & usability

Best Practices

Define clear data models &
ETL processes

Optimize query performance with indexing & partitioning

Implement data quality checks & maintain metadata

Secure data access with role-based controls & encryption

Plan for scalability & consider
cloud adoption

Typical Data Lake Architecture

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. The overall architecture is based on specific tools and processes for data security, governance and monitoring.

Key layers of an EDP include:

  • Raw Layer: This layer is responsible for collecting data from a variety of sources (such as on-premises databases, cloud applications, and sensors) and manage the data in a way that is accessible, efficient, and scalable.
  • Transform Layer: 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. It is also responsible for establishing and enforcing data access policies and related governance procedures for managing data throughout its lifecycle, including data security, privacy, and compliance.
  • Data Access, Analytics & Visualization Layer: This layer provides the tools and capabilities for users to develop on-demand data products, analyze and visualize data to gain insights, utilized enriched data for AI/ML use cases and much more.

Contact us today to learn more about how our data platform modernization service can help you unleash the full potential of your enterprise data assets.

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