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Hadoop Ecosystem
Set of open source technologies based on Hadoop for distributed storage and processing of massive data.
Apache Spark
Distributed in-memory processing framework optimized for performance and fast analysis of Big Data.
Distributed File Systems
File systems distributed across multiple machines for storing and accessing petabytes of data.
NoSQL Databases
Distributed non-relational databases optimized for horizontal scalability and flexible data models.
Stream Processing
Technologies for real-time continuous data stream processing with low latency.
Data Lakes
Centralized repositories that allow for the storage of structured and unstructured data at a large scale.
Cloud Computing Platforms
Cloud services (AWS, Azure, GCP) offering managed solutions for Big Data storage and processing.
Distributed Computing Models
Computational paradigms such as MapReduce, Lambda Architecture and Kappa Architecture for distributed processing.
Data Warehousing
Distributed data warehouses optimized for business intelligence analysis on massive volumes.
Graph Processing
Specialized frameworks for distributed graph processing for the analysis of complex networks.
Machine Learning at Scale
ML algorithms and frameworks adapted for training on distributed massive datasets.
Real-time Analytics
Systems providing instant insights on continuous data streams.