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Snowflake Architecture & Key Concepts for Data Warehouse

Analytics Vidhya

Introduction on Snowflake Architecture This article helps to focus on an in-depth understanding of Snowflake architecture, how it stores and manages data, as well as its conceptual fragmentation concepts. By the end of this blog, you will also be able to understand how Snowflake […].

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

Flipboard

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Create dbt models in dbt Cloud.

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Why using Infrastructure as Code for developing Cloud-based Data Warehouse Systems?

Data Science Blog

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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Top 20 Data Warehouse Interview Questions You Must Know in 2025

Pickl AI

Summary : This guide provides an in-depth look at the top data warehouse interview questions and answers essential for candidates in 2025. Covering key concepts, techniques, and best practices, it equips you with the knowledge needed to excel in interviews and demonstrates your expertise in data warehousing.

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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Enter AnalyticsCreator AnalyticsCreator, a powerful tool for data management, brings a new level of efficiency and reliability to the CI/CD process. It offers full BI-Stack Automation, from source to data warehouse through to frontend. It supports a holistic data model, allowing for rapid prototyping of various models.

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Serverless High Volume ETL data processing on Code Engine

IBM Data Science in Practice

By Santhosh Kumar Neerumalla , Niels Korschinsky & Christian Hoeboer Introduction This blogpost describes how to manage and orchestrate high volume Extract-Transform-Load (ETL) loads using a serverless process based on Code Engine. The source data is unstructured JSON, while the target is a structured, relational database.

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Understanding ETL Tools as a Data-Centric Organization

Smart Data Collective

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

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