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Our Data Engineering Approach
When you fail to recognize the advances in data management, you’re left with legacy tools, high costs, poorly developed staff, and ultimately slower business growth. How you build your data systems matters. Our approach helps our customers establish an efficient data system to support AI & ML development.
Data Ingestion
- Capture
- Move
- Stream
Data Engineering
- Cleanse
- Conform
- Transform
- Enrich
Data Stewardship
- Store
- Secure
- Govern
- Tag
Data Science
- Model
- Score
- Enrich
- Predict
Data Analytics
- Bl
- Online
- APIs
Data is Software Engineering
Proper source control and build-process automation make it faster and cheaper to deliver and maintain your code (yes, even SQL).
Iterative Approach
The implementation of continuous feedback loops during AI & ML development ensures that your business needs are met and that progress is kept on track.
Automate
Using people or automation can solve data problems. By preventing human error and repeatability, automation wins.
Build to Operate
Getting proactive with problem-solving requires monitoring and logging operational visibility.
Use Specialists
The world of data technologies is both broad and deep. Theory doesn't translate to the real world; expertise and experience do.
Data Engineering Services
Computing resources and data are needed to run machine learning models. Data issues such as poor quality and lack of production readiness continue to remain major hurdles in different stages of planning and adopting an AI strategy. Every customer’s data landscape is unique and our data engineering solutions look at individual customers’ situations to make their AI journey successful.
Connect
APIs
Data Flow Apps
External
Enterprise Data Warehouses
Data Flow Apps
Store
Big Data
SQL DBs/ Warehouse
Processing Framework
Stream
Batch
Buffer
Caches
Message Queues
Visualize
Web UIs
BI Tools
Mobile Apps
Our Capabilities
Data Ingestion
Data Pipelines
Data Lakes
Data Modernization
Insights from our experts
Chatbot for Food Service Fulfillment
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