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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

It allows data engineers to build, test, and maintain data pipelines in a version-controlled manner. dbt focuses on transforming raw data into analytics-ready tables using SQL-based transformations. Looker: Looker is a business intelligence and data visualization platform.

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. So why are organizations not able to scale governance? Meet Governance Requirements.

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Who is a BI Developer: Role, Responsibilities & Skills

Pickl AI

What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50

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Introduction to Power BI Datamarts

ODSC - Open Data Science

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Data warehousing is a vital constituent of any business intelligence operation.