Data Platform Modernization

Data Platform Specialists

Data platform modernization is the process of restructuring code, infrastructure, microservices and database technologies to improve the design of the solution. In most cases, modernization does not change core functionality and preserves original behavior.

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Modernizing solutions on the cloud generally refers to adopting cloud-native services in order to leverage all aspects of the cloud computing model. Often, organizations don't have a firm grasp of all the technologies available and how to implement a cloud-native solution.

Global Development Flexibility with On-Demand Delivery
Cost Savings with Pay-Per-Use Model Elasticity and Scalability
Fault-Tolerant Reliability Ease of Use with Managed Services

Depending on the organization's current cloud footprint, modernization can be completed via a stepped or all-in strategy. A stepped approach is generally done with an organization's initial transition into the cloud. It requires slight changes to the data platform while underlying technology such as security, networking and identification management are switched to cloud-native solutions.

Data System Refactoring - On-premise database technologies are assessed and evaluated for modernization. As we modernize your solution it's imperative that the correct cloud services and technologies are selected. Factors that drive which cloud services are used are:

Velocity and Volume of data
Elasticity and Scalability- building solutions for variable workloads
Loosening tightly coupled solutions
High-Availability and Disaster Recovery
Future data needs of the organization

Application Refactoring - In our experience, modernization of the data platform's front-end application usually consists of moving to cloud-native serverless solutions and containerization to efficiently and effectively scale to meet the data workload.

Looking into the Future of your data - After a data platform solution has been successfully modernized to the cloud, new ideas and ways to use the data emerge due to the flexibility and agility offered in the cloud. Customers then seek new ways to use their data and want to create new insights and revenue streams. New functionality and features can include:

Detect Fraudulent Activity Automatically detect potentially fraudulent credit card transactions by processing transactions through a machine learning model.

Deploy a Data Lake Build a framework that automatically deploys a data lake with consoles capable of cataloging, searching and visualizing data sets.

Predict Customer Churn Deploy a solution that automates the collection of customer data, predicts customer churn using ML, and maintains a tailored audience segment for messaging.

Real-Time IoT Device Monitoring Stream real-time IoT data to dashboards for user monitoring. Send notifications out to a tailored audience based on events.

Media Analysis Solution Automatically extract valuable information from your media files using AI.