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Learn the Differences Between ETL and ELT

Pickl AI

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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What is Integrated Business Planning (IBP)?

IBM Journey to AI blog

IBP brings together various functions, including sales, marketing, finance, supply chain, human resources, IT and beyond to collaborate across business units and make informed decisions that drive overall business success. Data integration and analytics IBP relies on the integration of data from different sources and systems.

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A Beginner’s Guide to Data Warehousing

Unite.AI

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making. The following elements serve as a backbone for a functional data warehouse.

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Big Data vs Data Warehouse

Marktechpost

With well-defined schemas, it is ideal for processing and organizing structured data, allowing for sophisticated queries and aggregations. A data warehouse’s essential characteristics are as follows. Projects that need a lot of scalability in order to handle varying data volumes. When to use each?

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

For more information, see Customize models in Amazon Bedrock with your own data using fine-tuning and continued pre-training. In this post, we primarily focus on the zero-shot and few-shot capabilities of LLMs for customer feedback analysis. For more information, refer to Prompt engineering. No explanation is required.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

These types of data are historical raw data from an ML perspective. For example, each log is written in the format of timestamp, user ID, and event information. Also, there is static data describing the players such as their age and registration date, which is non-historical data.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Understanding Data Engineering Data engineering is collecting, storing, and organising data so businesses can use it effectively. It involves building systems that move and transform raw data into a usable format. Without data engineering , companies would struggle to analyse information and make informed decisions.