Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Pipeline 103

Data Pipeline Data Engineering Data Engineer Engineering 103

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. We were data engineers! Data Engineering? Data science as a discipline was going through its adolescence of self-affirming and defining itself. We were pioneers.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Grouparoo

OCTOBER 6, 2021

It was a fun experience and I think we made a good choice by picking 97 Things Every Data Engineer Should Know. This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Christophe Blefari

APRIL 8, 2023

I did not care about data modeling for years. I was in the Hadoop world and all I was doing was denormalisation. The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. I still firmly believe that this is not the role of a data engineer. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

Christophe Blefari

APRIL 8, 2023

I did not care about data modeling for years. I was in the Hadoop world and all I was doing was denormalisation. The only normalisation I did was back at the engineering school while learning SQL with Normal Forms. I still firmly believe that this is not the role of a data engineer. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

Dataquest

JANUARY 25, 2017

Data scientists have become extremely sought after , and for good reason — a skilled data scientist can add incredible value to a business. But what about data engineers? A data scientist is only as good as the data they have access to. A data engineer transforms data into a useful format for analysis.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

ProjectPro

JUNE 1, 2017

News on Hadoop - May 2017 High-end backup kid Datos IO embraces relational, Hadoop data.theregister.co.uk , May 3 , 2017. Datos IO has extended its on-premise and public cloud data protection to RDBMS and Hadoop distributions. now provides hadoop support. Hadoop moving into the cloud.

Hadoop 52

Hadoop Medical Pipeline-centric Database-centric 52

Cloudera

APRIL 22, 2021

As a user/support engineer of Ozone, I may want to: . Look at details of volumes/buckets/keys/containers/pipelines/datanodes. Given a file, find out what nodes/pipeline is it part of. Find out whether the data distribution across Datanodes, and within disks in a Datanode is good.

Pipeline-centric 100

Pipeline-centric Data Lake Hadoop Metadata 100

AltexSoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Big Data 52

Big Data Data Process Process Hadoop 52

Monte Carlo

NOVEMBER 14, 2023

This is the world that data orchestration tools aim to create. Data orchestration tools minimize manual intervention by automating the movement of data within data pipelines. According to one Redditor on r/dataengineering, “Seems like 99/100 data engineering jobs mention Airflow.”

Pipeline-centric 40

Pipeline-centric Google Cloud Data Workflow Python 40

Knowledge Hut

SEPTEMBER 26, 2023

Key Features of Azure Synapse Here are some of the key features of Azure Synapse: Cloud Data Service: Azure Synapse operates as a cloud-native service, residing within the Microsoft Azure cloud ecosystem. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Databand.ai

DECEMBER 20, 2022

He is also an open-source developer at The Apache Software Foundation and the author of Hysterical , a popular blog on tech careers and topics like data, coding, and engineering. On LinkedIn, he posts frequently about data engineering, data architecture, interview preparation, and career advice.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52