Remove Amazon Web Services Remove Data Ingestion Remove Data Storage Remove Unstructured Data
article thumbnail

Most important Data Engineering Concepts and Tools for Data Scientists

DareData

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

article thumbnail

Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Unstructured data sources.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Azure Data Engineer Resume

Edureka

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. As a certified Azure Data Engineer, you have the skills and expertise to design, implement and manage complex data storage and processing solutions on the Azure cloud platform.

article thumbnail

Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]

Knowledge Hut

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.

article thumbnail

Top 10 Big Data Companies of 2023

Knowledge Hut

Tech Mahindra Tech Mahindra is a service-based company with a data-driven focus. The complex data activities, such as data ingestion, unification, structuring, cleaning, validating, and transforming, are made simpler by its self-service. It also makes it easier to load the data into destination databases.

article thumbnail

Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play.

article thumbnail

What is a Data Platform? And How to Build An Awesome One

Monte Carlo

We’ll cover: What is a data platform? Below, we share what the “basic” data platform looks like and list some hot tools in each space (you’re likely using several of them): The modern data platform is composed of five critical foundation layers. Data Storage and Processing The first layer?