Data Engineering Podcast
APRIL 14, 2024
The default association with the term "database" is relational engines, but non-relational engines are also used quite widely. In this episode Oren Eini, CEO and creator of RavenDB, explores the nuances of relational vs. non-relational engines, and the strategies for designing a non-relational database.
U-Next
MARCH 1, 2023
A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. A good Data Engineer will also have experience working with NoSQL solutions such as MongoDB or Cassandra, while knowledge of Hadoop or Spark would be beneficial.
Cloudera
NOVEMBER 23, 2021
What is Cloudera Operational Database (COD)? Operational Database is a relational and non-relational database built on Apache HBase and is designed to support OLTP applications, which use big data. The operational database in Cloudera Data Platform has the following components: .
Rockset
JUNE 7, 2022
Relational databases scale up well, but can be painful to scale out when a company has more data than a single database server can manage. On the other hand, non-relational databases (commonly referred to as NoSQL databases) are flexible databases for big data and real-time web applications.
Edureka
AUGUST 26, 2024
MongoDB is a NoSQL database where data are stored in a flexible way that is similar to JSON format. MongoDB is a NoSQL database used in web development. This stack is complete JavaScript, which means JavaScript is used for both the client-side (front end) and server-side as well as the (back end) of an application.
Edureka
AUGUST 18, 2024
Using queries to SQL language and back-end nodes that communicate with databases are essential aspects of this, which form the entire impetus. Two types of databases are used in the development process – Relational Databases: MySQL PostgreSQL Microsoft SQL Server Oracle Database Non-Relational Databases: MongoDB Cassandra 12.
Rockset
DECEMBER 31, 2022
Since DynamoDB is a NoSQL data model, it handles less structured data more efficiently than a relational data model, which is why it’s easier to address query volumes and offers high performance queries for item storage in inconsistent schemas. In turn, it can be harder to get to data and run large computations.
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