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How to Build a Performant Data Warehouse in Redshift

Sisense

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. This results in less joins between the metric data in fact tables, and the dimensions. So let’s dive in! OLTP vs OLAP. Sort & Dist Keys.

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Microsoft Azure Data Fundamentals: DP-900 Exam Preparation Guide

Whizlabs

Describing the components of a modern data warehouse. Describing data processing and data ingestion over Azure. Describing data visualization over Microsoft Power BI. How to Prepare for the Microsoft Azure Data Fundamentals DP-900 Exam? Step 2: Check the Microsoft Documentations.

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Where Dark Data Lives and How to Mine It for Business Gain

Domo

.” Here’s a shortlist of dark data and the common places it lives: Documents. The same goes for Word documents, forms, PDFs, and presentations. Data repositories. Lots of data—structured and unstructured—gets dumped into data warehouses, lakes, and non-relational databases.

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IDP in the Age of GenAI

Astera

Data Governance is a systematic approach to managing and utilizing an organizations data. It ensures data quality, security, and accessibility for informed decision-making. However, managing, analyzing, and governing the data is a complex process. However, managing, analyzing, and governing the data is a complex process.

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What is a database?

Astera

These databases are suitable for managing semi-structured or unstructured data. Types of NoSQL databases include document stores such as MongoDB, key-value stores such as Redis, and column-family stores such as Cassandra. These databases are ideal for big data applications, real-time web applications, and distributed systems.

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What is Metadata Management? Benefits, Framework, Tools, Use Cases, Best Practices

Astera

Process metadata: tracks data handling steps. It ensures data quality and reproducibility by documenting how the data was derived and transformed, including its origin. Examples include actions (such as data cleaning steps), tools used, tests performed, and lineage (data source).

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Top 8 Data Wrangling Tools for 2023

Astera

Scalability : The best part about data wrangling tools is their ability to handle large data volumes, allowing seamless scalability. These tools employ optimized algorithms and parallel processing techniques, enabling faster data processing and analysis.