Data Engineering Google Cloud Scalability Software Engineering 
Xebia
JANUARY 19, 2023
The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.
CIO
SEPTEMBER 2, 2022
If you’re an IT pro looking to break into the finance industry, or a finance IT leader wanting to know where hiring will be most competitive, here are the top 10 in-demand tech jobs in finance, according to data from Dice. Software engineer. Full-stack software engineer. Back-end software engineer.
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CIO
AUGUST 31, 2022
If you’re an IT pro looking to break into the finance industry, or a finance IT leader wanting to know where hiring will be most competitive, here are the top 10 in-demand tech jobs in finance, according to data from Dice. Software engineer. Full-stack software engineer. Back-end software engineer.
CIO
JANUARY 6, 2023
The role typically requires a bachelor’s degree in computer science or a related field and at least three years of experience in cloud computing. Keep an eye out for candidates with certifications such as AWS Certified Cloud Practitioner, Google Cloud Professional, and Microsoft Certified: Azure Fundamentals.
Mobilunity
AUGUST 3, 2021
Technologies that have expanded Big Data possibilities even further are cloud computing and graph databases. The cloud offers excellent scalability, while graph databases offer the ability to display incredible amounts of data in a way that makes analytics efficient and effective. Who is Big Data Engineer?
Altexsoft
JUNE 29, 2021
This article will focus on the role of a machine learning engineer, their skills and responsibilities, and how they contribute to an AI project’s success. The role of a machine learning engineer in the data science team. The focus here is on engineering, not on building ML algorithms. Who does what in a data science team.
Confluent
FEBRUARY 6, 2019
Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.
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