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Data Modeling in Machine Learning Pipelines: Best Practices Using SQL and NoSQL Databases

Dataversity

Data, undoubtedly, is one of the most significant components making up a machine learning (ML) workflow, and due to this, data management is one of the most important factors in sustaining ML pipelines.

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

Data Science Blog

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. It also supports a wide range of data warehouses, analytical databases, data lakes, frontends, and pipelines/ETL. Mixed approach of DV 2.0

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SQL vs. NoSQL: Decoding the database dilemma to perfect solutions

Data Science Dojo

Welcome to the world of databases, where the choice between SQL (Structured Query Language) and NoSQL (Not Only SQL) databases can be a significant decision. In this blog, we’ll explore the defining traits, benefits, use cases, and key factors to consider when choosing between SQL and NoSQL databases.

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Traditional vs Vector databases: Your guide to make the right choice

Data Science Dojo

This blog delves into a detailed comparison between the two data management techniques. In today’s digital world, businesses must make data-driven decisions to manage huge sets of information. Hence, databases are important for strategic data handling and enhanced operational efficiency.

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Object-centric Process Mining on Data Mesh Architectures

Data Science Blog

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

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

Data Science Blog

So why using IaC for Cloud Data Infrastructures? This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure. Enhanced Security and Compliance Data Warehouses often store sensitive information, making security a paramount concern.

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Becoming a Data Engineer: 7 Tips to Take Your Career to the Next Level

Data Science Connect

In this blog post, we will be discussing 7 tips that will help you become a successful data engineer and take your career to the next level. Learn SQL: As a data engineer, you will be working with large amounts of data, and SQL is the most commonly used language for interacting with databases.