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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! LinkedIn write about Hoptimator for auto generated Flink pipeline with multiple stages of systems. Can't we use the vector feature in the existing databases?

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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. Each of these accelerators support multiple legacy systems, including Teradata, Netezza, Oracle, etc. Consideration of both data & metadata in the migration.

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Experts Share the 5 Pillars Transforming Data & AI in 2024

Monte Carlo

As LLMs and new models learn better, more efficient ways of working with data, says John, “It’s going to create a whole new kind of class system of engineering versus what everyone looked to the data scientists for in the last five to ten years. That implies working with new patterns like vector databases.” RAG workflow.

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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. Building the KPay payment system.

Kafka 93
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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.