Data Analytics Data Process Non-relational Database 
U-Next
MARCH 1, 2023
Being a hybrid role, Data Engineer requires technical as well as business skills. They build scalable data processing pipelines and provide analytical insights to business users. A Data Engineer also designs, builds, integrates, and manages large-scale data processing systems.
Knowledge Hut
JANUARY 24, 2024
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-relational databases, data warehouses, big data, and on-cloud data.
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Cloud Academy
JANUARY 27, 2022
Regular expressions can be used in all data formats and platforms. For example, you can learn about how JSONs are integral to non-relational databases – especially data schemas, and how to write queries using JSON. This includes understanding the AWS data analysis services and how they interact with one another.
ProjectPro
JANUARY 31, 2023
In this blog, we'll dive into some of the most commonly asked big data interview questions and provide concise and informative answers to help you ace your next big data job interview. Get ready to expand your knowledge and take your big data career to the next level! “Data analytics is the future, and the future is NOW!
Knowledge Hut
SEPTEMBER 22, 2023
Microsoft Azure Data Engineer Certification Career Opportunities I have seen data engineers finding employment in businesses or on projects focusing on a variety of industries, including artificial intelligence ( AI ), software, data analytics , healthcare, IT, retail, marketing , government, transportation, and more.
Silectis
JANUARY 3, 2021
BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Cassandra A database built by the Apache Foundation.
ProjectPro
NOVEMBER 15, 2021
It incorporates caching, stream computing, message queuing, and other functionalities to decrease the complexity and expenses of development and operations, in addition to the 10x quicker time-series database. DataFrames are used by Spark SQL to accommodate structured and semi-structured data.
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