Remove Analytics Remove Data Engineering Remove Scalability
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What is DataOps? Collaborative, cross-functional analytics

CIO

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 195
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Fundamentals of Data Engineering

Xebia

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.

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Predictive analytics helps Fresenius anticipate dialysis complications

CIO

To do so, the team had to overcome three major challenges: scalability, quality and proactive monitoring, and accuracy. The solution uses CloudWatch alerts to send notifications to the DataOps team when there are failures or errors, while Kinesis Data Analytics and Kinesis Data Streams are used to generate data quality alerts.

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Firebolt, a data warehouse startup, raises $100M at a $1.4B valuation for faster, cheaper analytics on large data sets

TechCrunch

Israeli startup Firebolt has been taking on Google’s BigQuery, Snowflake and others with a cloud data warehouse solution that it claims can run analytics on large datasets cheaper and faster than its competitors. Firebolt cites analysts that estimate the global cloud analytics market will be worth some $65 billion by 2025.

Analytics 218
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10 most in-demand enterprise IT skills

CIO

Its a common skill for cloud engineers, DevOps engineers, solutions architects, data engineers, cybersecurity analysts, software developers, network administrators, and many more IT roles. Its a skill common with data analysts, business intelligence professionals, and business analysts.

UI/UX 203
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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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What is data architecture? A framework to manage data

CIO

Real-time analytics. The goal of many modern data architectures is to deliver real-time analytics the ability to perform analytics on new data as it arrives in the environment. Modern data architectures must be designed to take advantage of technologies such as AI, automation, and internet of things (IoT).