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AWS CEO Selipsky: We Are Making Cloud Easier To Use

Adrian Bridgwater for Forbes

What businesses need from cloud computing is the power to work on their data without having to transport it around between different clouds, different databases and different repositories, different integrations to third-party applications, different data pipelines and different compute engines.

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Discovering the Role of Data Science in a Cloud World

Pickl AI

Summary: “Data Science in a Cloud World” highlights how cloud computing transforms Data Science by providing scalable, cost-effective solutions for big data, Machine Learning, and real-time analytics. Advancements in data processing, storage, and analysis technologies power this transformation.

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Becoming a Data Engineer: 7 Tips to Take Your Career to the Next Level

Data Science Connect

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

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Demystifying Time Series Database: A Comprehensive Guide

Pickl AI

Summary: Time series databases (TSDBs) are built for efficiently storing and analyzing data that changes over time. This data, often from sensors or IoT devices, is typically collected at regular intervals. Buckle up as we navigate the intricacies of storing and analysing this dynamic data.

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The 2021 Executive Guide To Data Science and AI

Applied Data Science

Automation Automating data pipelines and models ➡️ 6. The Data Engineer Not everyone working on a data science project is a data scientist. Data engineers are the glue that binds the products of data scientists into a coherent and robust data pipeline.

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Navigating the Cloud Modernization Journey: Insights from Precisely’s Partnership with AWS

Precisely

As a Technical Architect at Precisely, I’ve had the unique opportunity to lead the AWS Mainframe Modernization Data Replication for IBM i initiative, a project that not only challenged our technical capabilities but also enriched our understanding of cloud integration complexities.

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A Guide to Choose the Best Data Science Bootcamp

Data Science Dojo

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.