Big Data Tools Relational Database Scala SQL 
AltexSoft
JUNE 7, 2021
A powerful Big Data tool, Apache Hadoop alone is far from being almighty. MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Alternatively, you can opt for Apache Cassandra — one more noSQL database in the family. Cassandra excels at streaming data analysis.
Knowledge Hut
APRIL 25, 2024
This article will discuss big data analytics technologies, technologies used in big data, and new big data technologies. Check out the Big Data courses online to develop a strong skill set while working with the most powerful Big Data tools and technologies.
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Knowledge Hut
NOVEMBER 17, 2023
Here is a step-by-step guide on how to become an Azure Data Engineer: 1. Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. You ought to be able to create a data model that is performance- and scalability-optimized.
ProjectPro
FEBRUARY 8, 2023
In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. In 2023, more than 5140 businesses worldwide have started using AWS Glue as a big data tool.
Edureka
FEBRUARY 9, 2023
Data Ingestion and Transformation: Candidates should have experience with data ingestion techniques, such as bulk and incremental loading, as well as experience with data transformation using Azure Data Factory. SQL is also an essential skill for Azure Data Engineers.
Knowledge Hut
JUNE 23, 2023
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.
Knowledge Hut
SEPTEMBER 26, 2023
We as Azure Data Engineers should have extensive knowledge of data modelling and ETL (extract, transform, load) procedures in addition to extensive expertise in creating and managing data pipelines, data lakes, and data warehouses. is the responsibility of data engineers.
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