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You're reading from  Vector Search for Practitioners with Elastic

Product typeBook
Published inNov 2023
PublisherPackt
ISBN-139781805121022
Edition1st Edition
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Authors (2):
Bahaaldine Azarmi
Bahaaldine Azarmi
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Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

Jeff Vestal
Jeff Vestal
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Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal

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Eland

Eland is a Python library developed by Elastic that allows users to interface with Elasticsearch seamlessly for data manipulation and analysis. The library is built on top of the official Elasticsearch Python client and extends the pandas API to Elasticsearch. This enables users to interact with Elasticsearch data using familiar pandas-like syntax and conventions, making it easier to integrate Elasticsearch with existing data analysis workflows and tools.

Eland is particularly useful for handling large datasets that cannot fit in memory and require distributed processing. Elasticsearch can scale horizontally by distributing data across multiple nodes in a cluster. This allows the user to efficiently work with far larger datasets than what would be possible on a laptop. Let’s look at some features of Eland:

  • One key use case for Eland is querying data stored in Elasticsearch. Instead of writing raw Elasticsearch queries, users can write Python code that resembles...
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Vector Search for Practitioners with Elastic
Published in: Nov 2023Publisher: PacktISBN-13: 9781805121022

Authors (2)

author image
Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

author image
Jeff Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.
Read more about Jeff Vestal