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Scaling Beyond Postgres: How to Choose a Real-Time Analytical Database

Simon Späti

It’s the Swiss Army knife of databases, and for many applications, it’s more than sufficient. Therefore, you’ve probably come across terms like OLAP (Online Analytical Processing) systems, data warehouses, and, more recently, real-time analytical databases.

Database 130
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MSSQL vs MySQL: Comparing Powerhouses of Databases

Analytics Vidhya

Introduction In the bustling arena of database management systems, two heavyweight contenders emerge, each carrying its arsenal of features and capabilities. In one corner, we have the suave and sophisticated Microsoft SQL Server (MSSQL), donned in the elegance of enterprise-level prowess.

MySQL 235
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Surveying The Market Of Database Products

Data Engineering Podcast

Summary Databases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. What are the aspects of the database market that keep you interested as a VP of product?

Database 189
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Microsoft’s Drasi: An Open-Source Tool for Efficient Change Management Systems

Analytics Vidhya

Introduction Today, data systems evolve quickly, demanding efficient monitoring and response. Real-time change detection is essential to keeping systems stable, preventing failures, and ensuring business continuity.

Systems 175
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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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Inside Facebook’s video delivery system

Engineering at Meta

Were explaining the end-to-end systems the Facebook app leverages to deliver relevant content to people. At Facebooks scale, the systems built to support and overcome these challenges require extensive trade-off analyses, focused optimizations, and architecture built to allow our engineers to push for the same user and business outcomes.

Systems 68
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Redefining AIOps IT Workflows with Legacy System Visibility

Precisely

Modern IT environments require comprehensive data for successful AIOps, that includes incorporating data from legacy systems like IBM i and IBM Z into ITOps platforms. AIOps presents enormous promise, but many organizations face hurdles in its implementation: Complex ecosystems made of multiple, fragmented systems that lack interoperability.

Systems 59