article thumbnail

Data Engineering Weekly #137

Data Engineering Weekly

Editors Note: 🔥 DEW is thrilled to announce a developer-centric Data Eng & AI conference in the tech hub of Bengaluru, India, on October 12th! Atlan AI leverages metadata that Atlan captures across the data stack to make AI part of your data stack. Can't we use the vector feature in the existing databases?

article thumbnail

Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. The data from your existing data warehouse is migrated to the storage option you choose, and all the metadata is migrated into SDX (Shared Data Experiences) layer of Cloudera Data Platform.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Experts Share the 5 Pillars Transforming Data & AI in 2024

Monte Carlo

RAG involves integrating a real-time database into the LLM’s response generation process, while fine-tuning trains models on targeted datasets to improve domain-specific responses. That implies working with new patterns like vector databases.” It can show me how it built that chart, which dataset it used, and show me the metadata.”

article thumbnail

Journey to Event Driven – Part 4: Four Pillars of Event Streaming Microservices

Confluent

Storing events in a stream and connecting streams via stream processors provide a generic, data-centric, distributed application runtime that you can use to build ETL, event streaming applications, applications for recording metrics and anything else that has a real-time data requirement. Payment processing is an interesting problem.

Kafka 93
article thumbnail

The Just-In-Time Revolution for Data-Driven Enterprises

The Modern Data Company

Each Data Product is designed for a specific purpose, equipped with the necessary data, transformations, and metadata. Here’s a breakdown of their key components: Data Source: Defines the raw data used to build the Data Product, including internal databases, external feeds, or sensor data. Your business will thank you for it.