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Glossary – Data, Cloud & Infrastructure

Data and cloud infrastructure form the backbone for scalable AI adoption. This category defines systems and processes that ensure data reliability, real-time access, and operational efficiency across enterprise ecosystems.

Understanding Data, Cloud & Infrastructure

Data and cloud infrastructure form the backbone for scalable AI adoption. This category defines systems and processes that ensure data reliability, real-time access, and operational efficiency across enterprise ecosystems.

Glossary Terms

TermDescription
Data PipelineAutomated flow for ingesting, cleaning, transforming, and delivering data to applications.
CDC (Change Data Capture)Real-time data synchronization method tracking changes in databases for continuous analytics.
Streaming DataContinuous data feed processed in real time for instant decision-making.
Apache FlinkOpen-source framework used for large-scale stream processing and CDC in data pipelines.
KafkaDistributed streaming platform managing real-time data flow between systems.
Text-to-SQL InterfaceAI module that translates natural language questions into SQL queries for instant data retrieval.
Natural Query InterfaceSearch feature allowing data access without technical query knowledge.
Secure Data LakeCentral, encrypted repository for structured and unstructured data used in AI analytics.
Cloud TransformationMigration of applications and infrastructure to scalable cloud-native environments.
ScalabilitySystem capacity to handle increased workloads or data volume without performance loss.
Performance EngineeringDesigning and tuning systems for high-speed computation, minimal latency, and reliability.

Insights into Data, Cloud & Infrastructure

Strong data architecture ensures that AI initiatives are sustainable and trustworthy. By aligning data pipelines, cloud scalability, and security, enterprises build the foundation for automation and advanced analytics to operate at scale.