This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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-relationaldatabase.
Supports numerous data sources It connects to and fetches data from a variety of data sources using Tableau and supports a wide range of data sources, including local files, spreadsheets, relational and non-relationaldatabases, data warehouses, big data, and on-cloud data.
You should be thorough with technicalities related to relational and non-relationaldatabases, Data security, ETL (extract, transform, and load) systems, Data storage, automation and scripting, big data tools, and machine learning.
Relational vs non-relationaldatabases As we mentioned above, relational or SQL databases are designed for structured or tabular data. Non-relationaldatabases , on the other hand, work for data forms and structures other than tables. and its value (male, red, $100, etc.).
Data quality control — to ensure that all information is correct by applying data validation logic. Data security and governance — to provide different security levels to admins, developers, and consumer groups as well as define clear datagovernance rules, removing barriers for information sharing. ?onsuming
NoSQL Databases NoSQL databases are non-relationaldatabases (that do not store data in rows or columns) more effective than conventional relationaldatabases (databases that store information in a tabular format) in handling unstructured and semi-structured data.
The platform’s main capabilities comprise data integration, data quality assurance, and datagovernance. The software includes InfoSphere DataStage Designer — a tool with an easy-to-use web-based graphical interface that is suitable for both tech-savvy specialists and non-programmers. Source: G2. Ease of use.
DataFrames are used by Spark SQL to accommodate structured and semi-structured data. You can also access data through non-relationaldatabases such as Apache Cassandra, Apache HBase, Apache Hive, and others like the Hadoop Distributed File System.
We organize all of the trending information in your field so you don't have to. Join 37,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content