Data Intensive Applications Powering Artificial Intelligence (AI) Applications

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Data-intensive applications are systems built to handle vast amounts of data. As artificial intelligence (AI) applications increasingly rely on large datasets for training and operation, understanding how data is stored and retrieved becomes critical. The sources explore various strategies for managing data at scale, which are highly relevant to the needs of AI.

Many AI workloads, particularly those involving large-scale data analysis or training, align with the characteristics of Online Analytical Processing (OLAP) systems. Unlike transactional systems (OLTP) that handle small, key-based lookups, analytic systems are optimized for scanning millions of records and computing aggregates across large datasets. Data warehouses, often containing read-only copies of data from various transactional systems, are designed specifically for these analytic patterns.

To handle the scale and query patterns of analytic workloads common in AI, systems often employ techniques like column-oriented storage. Instead of storing all data for a single record together (row-oriented), column-oriented databases store all values for a single column together. This allows queries to read only the necessary columns from disk, minimizing data transfer, which is crucial when dealing with vast datasets. Compression techniques, such as bitmap encoding, further reduce the amount of data read.

Indexing structures also play a role. While standard indexes help with exact key lookups, other structures support more complex queries, like multi-dimensional indexes for searching data across several attributes simultaneously. Fuzzy indexes and techniques used in full-text search engines like Lucene can even handle searching for similar data, such as misspelled words, sometimes incorporating concepts from linguistic analysis and machine learning.

Finally, deploying data systems at the scale needed for many AI applications means dealing with the inherent trouble with distributed systems, including network issues, unreliable clocks, and partial failures. These challenges require careful consideration of replication strategies (like single-leader, multi-leader, or leaderless) and how to ensure data consistency and availability.

In essence, the principles and technologies discussed in the sources – optimized storage for analytics, advanced indexing, and strategies for building reliable distributed systems – form the foundation for effectively managing the data demands of modern AI applications.