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Top 12 Backend Developer Skills You Must Know in 2024

Knowledge Hut

From in-depth knowledge of programming languages to problem-solving skills, there are various qualities that a successful backend developer must possess. Backend Programming Languages Java, Python, PHP You need to know specific programming languages to have a career path that leads you to success.

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Data Engineering Learning Path: A Complete Roadmap

Knowledge Hut

You should have the expertise to collect data, conduct research, create models, and identify patterns. You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. You must develop predictive models to help industries and businesses make data-driven decisions.

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. RDBMS is a part of system software used to create and manage databases based on the relational model.

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Data Engineering Glossary

Silectis

MapReduce MapReduce is a component of the Hadoop framework that’s used to access big data stored within the Hadoop File System Metadata A set of data that describes and gives information about other data. MySQL An open-source relational databse management system with a client-server model.

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The Role of Database Applications in Modern Business Environments

Knowledge Hut

Database Software- Other NoSQL: NoSQL databases cover a variety of database software that differs from typical relational databases. Key-value stores, columnar stores, graph-based databases, and wide-column stores are common classifications for NoSQL databases. Spatial Database (e.g.-

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100+ Data Engineer Interview Questions and Answers for 2023

ProjectPro

Below are some big data interview questions for data engineers based on the fundamental concepts of big data, such as data modeling, data analysis , data migration, data processing architecture, data storage, big data analytics, etc. Hadoop is highly scalable.