AltexSoft

FEBRUARY 11, 2023

The former uses data to generate insights and help businesses make better decisions, while the latter designs data frameworks, flows, standards, and policies that facilitate effective data analysis. But first, all candidates must be accredited by Arcitura as Big Data professionals.

Data Architect 87

Data Architect Certification Generalist Big Data 87

U-Next

AUGUST 17, 2022

These consist of: Generalist: Typically, general practitioners work in small teams or for small businesses. Being one of the few “data-focused” employees in an organization, Data Engineers juggle multiple responsibilities. Responsibilities of a Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Knowledge Hut

NOVEMBER 28, 2023

Data Visualization To successfully fulfill ETL or ELT-related work, you must be well-versed in exploratory data analysis (EDA). This forms an integral part of data visualization, which includes tools like Azure, Google Looker, Excel, SSRS, etc. Let's take a look at each of these groups.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

JUNE 20, 2023

Additionally, they create and test the systems necessary to gather and process data for predictive modelling. Data engineers play three important roles: Generalist: With a key focus, data engineers often serve in small teams to complete end-to-end data collection, intake, and processing.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 6, 2023

Programming Languages : Good command on programming languages like Python, Java, or Scala is important as it enables you to handle data and derive insights from it. Data Analysis : Strong data analysis skills will help you define ways and strategies to transform data and extract useful insights from the data set.

Big Data 52

Big Data Certification Hadoop Scala 52

Knowledge Hut

DECEMBER 26, 2023

If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured. These are as follows: 1.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98