What is Teradata’s approach to handling big data?

Teradata’s approaches to handling big data include several core technologies and methods.

1. Parallel processing: Teradata utilizes a shared architecture to process large-scale data through parallel processing and distributed computing. It distributes data to multiple nodes for parallel processing, thus enhancing computational performance and scalability.
2. Data compression: Teradata utilizes data compression technology to reduce storage space and improve data retrieval speed. Compressing data helps to decrease disk I/O operations, speeding up data access.
3. Data partitioning: Teradata organizes and stores data in different partitioning methods to improve query performance and optimize data access. Data partitioning can be flexibly configured based on business needs and query patterns.
4. Parallel loading: Teradata supports loading data into the database in parallel, allowing large amounts of data to be quickly loaded into the database, thus improving data import efficiency.
5. Data compression: Teradata utilizes data compression technology to reduce storage space and enhance data retrieval speed. Compressing data helps decrease disk I/O operations and speed up data access.

In general, Teradata’s methods for handling big data primarily include parallel processing, data compression, data partitioning, parallel loading, and other techniques to enhance the efficiency and performance of data processing, meeting the demands of large-scale data processing.