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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?

Systems 83
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Data Testing Tools: Key Capabilities and 6 Tools You Should Know

Databand.ai

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. This is part of a series of articles about data quality.

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What is ELT (Extract, Load, Transform)? A Beginner’s Guide [SQ]

Databand.ai

The Transform Phase During this phase, the data is prepared for analysis. This preparation can involve various operations such as cleaning, filtering, aggregating, and summarizing the data. The goal of the transformation is to convert the raw data into a format that’s easy to analyze and interpret.

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Data testing tools: Key capabilities you should know

Databand.ai

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

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ELT Explained: What You Need to Know

Ascend.io

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

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Redefining Data Engineering: GenAI for Data Modernization and Innovation – RandomTrees

RandomTrees

Transformation: Shaping Data for the Future: LLMs facilitate standardizing date formats with precision and translation of complex organizational structures into logical database designs, streamline the definition of business rules, automate data cleansing, and propose the inclusion of external data for a more complete analytical view.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management. These practices help ensure that the data being ingested is accurate, complete, and consistent across all sources.