Database Database-centric Systems 
Data Engineering Weekly
JULY 28, 2024
Adopting LLM in SQL-centric workflow is particularly interesting since companies increasingly try text-2-SQL to boost data usage. link] Booking.com: The Engineering Behind High-Performance Ranking Platform: A System Overview Booking.com writes about its ranking platform, which is pivotal in its wider search platform.
Striim
NOVEMBER 11, 2024
A leading home improvement retailer recognized the need to modernize its data infrastructure in order to move data from legacy systems to the cloud and improve operational efficiency. Known for its customer-centric approach and expansive product offerings, the company has maintained its leadership position in the industry for decades.
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Grouparoo
MAY 13, 2021
In this post, we’ll look at the historical reasons for the 191 character limit as a default in most relational databases. The first question you might ask is why limit the length of the strings you can store in a database at all? MySQL wanted to ensure that its index files could fit within a single page block on older file systems.
Data Engineering Weekly
AUGUST 25, 2024
Google’s search ranking system is a complex, multi-step process that begins with indexing new content, assigning it a unique DocID, and calculating its relevance based on keyword presence. The author writes an overview of the performance implication of disaggregated systems compared to traditional monolithic databases.
DareData
MARCH 5, 2024
At DareData Engineering, we believe in a human-centric approach, where AI agents work together with humans to achieve faster and more efficient results. At its core, RAG harnesses the power of large language models and vector databases to augment pre-trained models (such as GPT 3.5 ).
Data Science Blog: Data Engineering
NOVEMBER 15, 2023
The database for Process Mining is also establishing itself as an important hub for Data Science and AI applications, as process traces are very granular and informative about what is really going on in the business processes. The creation of this data model requires the data connection to the source system (e.g.
Monte Carlo
MAY 30, 2024
Retrieval augmented generation (RAG) is an architecture framework introduced by Meta in 2020 that connects your large language model (LLM) to a curated, dynamic database. Here’s how a RAG flow works: Query processing: The process begins when a user submits a query to the system. A RAG flow in Databricks can be visualized like this.
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