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How to get started with dbt

Christophe Blefari

In the ELT, the load is done before the transform part without any alteration of the data leaving the raw data ready to be transformed in the data warehouse. In a simple words dbt sits on top of your raw data to organise all your SQL queries that are defining your data assets.

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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

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The Hidden Threats in Your Data Warehouse Layers (And How to Fix Them)

Monte Carlo

Integration Layer : Where your data transformations and business logic are applied. Stage Layer: The Foundation The Stage Layer serves as the foundation of a data warehouse. Its primary purpose is to ingest and store raw data with minimal modifications, preserving the original format and content of incoming data.

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Column-Level Lineage, Model Performance, and Recommendations: ship trusted data products with dbt Explorer

dbt Developer Hub

That has often meant a manual, painstaking process of cross checking run logs and your dbt documentation site to get the stakeholder the information they need. dbt Explorer centralizes documentation, lineage, and execution metadata to reduce the work required to ship trusted data products faster. Enter dbt Explorer !

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AI Success – Powered by Data Governance and Quality

Precisely

Compliance: Review legal agreements on data usage and address intellectual property concerns with generative artificial intelligence (GenAI) outputs. Compliance measures also involve security risk assessments to identify potential gaps and ensure data isn’t compromised. Data discoverability is a key part of data governance.

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Leveraging AI & Automation in Data Engineering: 4 Essential Frameworks

Ascend.io

Access to Non-Public Knowledge: At this level, AI systems can access internal documents, non-confidential reports, and other proprietary information not available to the public. This helps the AI tools provide more contextual and relevant outputs, improving their utility without compromising critical data security.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

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