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Neo4j vs. Amazon Neptune: Graph Databases in Data Engineering

Analytics Vidhya

Traditional databases, while still valuable, often falter when it comes to handling highly connected data. Enter the unsung heroes of the data world: graph databases. This article discusses […] The post Neo4j vs. Amazon Neptune: Graph Databases in Data Engineering appeared first on Analytics Vidhya.

Database 213
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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 228
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Understanding the Basics of Database Normalization

Analytics Vidhya

Introduction Data normalization is the process of building a database according to what is known as a canonical form, where the final product is a relational database with no data redundancy. More specifically, normalization involves organizing data according to attributes assigned as part of a larger data model.

Database 221
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How to Normalize Relational Databases With SQL Code?

Analytics Vidhya

The database is the major element of a data science project. To generate actionable insights, the database must be centralized and organized efficiently. If a corrupted, unorganized, or redundant database is used, the results of the analysis may become inconsistent and highly misleading. appeared first on Analytics Vidhya.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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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?

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Designing A Non-Relational Database Engine

Data Engineering Podcast

Summary Databases come in a variety of formats for different use cases. The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. Can you describe what constitutes a NoSQL database? If you were to start from scratch today, what database would you build?

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Modern Data Architecture for Embedded Analytics

You’ll learn: 7 approaches to data architecture for embedded analytics—from a transactional database to a columnar or in memory database. Discover the pros and cons of each approach, plus how to choose the right architecture for your business priorities, timeline, and customers.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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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.