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The Five Key Technologies of Big Data

The Five Key Technologies of Big Data

1. Data Storage Technology

Hadoop HDFS: A distributed file system that supports large-scale data storage and can handle both structured and unstructured data.

NoSQL Databases: Such as MongoDB and Cassandra, which are suitable for high-performance, flexible data storage needs, especially excelling in handling non-relational data.

2. Data Processing Frameworks

Apache Hadoop: Provides batch processing capabilities and efficiently processes large-scale datasets using the MapReduce model.

Apache Spark: Supports fast data processing and real-time analytics, offering in-memory computing and various data processing APIs (such as SQL, machine learning, and graph processing).

3. Stream Processing Technologies

Apache Kafka: A distributed streaming platform that can process and transmit data streams in real-time, suitable for real-time data processing and analysis.

Apache Flink: Supports both stream and batch processing, characterized by high throughput and low latency, making it suitable for complex event processing.

4. Data Analysis and Mining

Machine Learning and Deep Learning: Using libraries like TensorFlow and Scikit-learn for data mining, predictive analysis, and model training to extract valuable insights.

Graph Analysis: Analyzing complex relational data using graph databases (like Neo4j) and graph processing frameworks (like GraphX).

5. Data Visualization

Data Visualization Tools: Such as Tableau, Power BI, and D3.js, which help users present data analysis results in graphical forms, promoting better understanding and decision-making.

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