Postgres has the unique ability to act as a powerful data aggregator in many data centers. This talk shows how Postgres's extensibility, access to foreign data sources, and ability to handle NoSQL-like and data warehousing workloads gives it unmatched capabilities to function in this role.
Duration: 45 minutes, 1 hour with questions
Data needs have changed dramatically in the last few decades, leaving once dominant relational systems with an unclear future. This talk explains why data needs have changed, and how Postgres has uniquely adjusted to those needs. The talk also explains how to store data outside of Postgres while maintaining integrated data management.
Duration: 30 minutes, 45 minutes with questions
Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search,xml,json, and records. This can be presented after myMaking Postgres Central in Your Data Center presentation.
Duration: 45 minutes, 60 minutes with questions
When considering database indexing, many people are confused by the many Postgres indexing structures available, and the many data-type-specific index lookup methods. For example,brin allows for efficient indexing of many columns.gin indexing specializes in the rapid lookup of keys with many duplicates — an area where traditional btree indexes perform poorly. This is particularly useful forjson and full text searching.GiST allows for efficient indexing of two-dimensional values and range types. This talk explores the various indexing features of Postgres and when to use them. This is ideal when presented after myNon-Relational Postgres presentation.
Duration: 45 minutes, 1 hour with questions
Artificial intelligence, machine learning, and deep learning are intertwined capabilities that attempt to solve problems that defy traditional computational solutions — problems include fraud detection, voice recognition, and search result recommendations. While they defy simple computation, they are computationally expensive, involving computation of perhaps millions of probabilities and weights. While these computations can be done outside of the database, there are specific advantages of doing machine learning inside the database, close to where the data is stored. This presentation explains how to do machine learning inside the Postgres database, and includes an example of discriminative AI using stored procedures.
Duration: 45 minutes, 60 minutes with questions
Since the creation of my presentation,Postgres and the Artificial Intelligence Landscape, artificial intelligence use has exploded, with much anticipation about its future. This talk explores many of the advances that has fueled this explosion, including multi-dimensional vectors, text embeddings, semantic/vector search, transformers, generative AI, and Retrieval-Augmented Generation (rag). The talk includes semantic/vector search andrag examples. It finally covers how the valuable data stored in databases can be used to enhance AI usage. This can be combined with the previous presentation.
Duration: 45 minutes, 60 minutes with questions
Relational databases are regularly challenged by new technologies that promise to make SQL obsolete, but the new technologies often fade into obscurity. This talk explores how new NoSQL technologies are unique, and how existing relational database systems like Postgres are adapting to handle NoSQL workloads.
Duration: 45 minutes, 1 hour with questions
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