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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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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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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

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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Data Migration Software: Which Solution Fits Your Project Best

Altexsoft

Three types of data migration tools. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. This makes sense when you move a relatively small amount of data and deal with simple requirements. Cloud-based tools. Phases of the data migration process.

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Cloud Certification Guide: How to Master & Showcase Your Expertise in AWS, Azure, & Google Cloud

ParkMyCloud

Individuals in an associate solutions architect role have 1+ years of experience designing available, fault-tolerant, scalable, and most importantly cost-efficient, distributed systems on AWS. Must prove knowledge of deploying, operating and managing highly available, scalable and fault-tolerant systems on AWS. GCP Certifications.

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The Good and the Bad of Apache Kafka Streaming Platform

Altexsoft

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. Still, it’s the number one choice for data-driven companies, and here’re some reasons why. Scalability.

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Data Mesh Architecture: Concept, Main Principles, and Implementation

Altexsoft

The infrastructural shift means going from a fragmented platform with separate operational and analytical planes to an integrated infrastructure for both operational and data systems. Data mesh can be utilized as an element of an enterprise data strategy and can be described through four interacting principles.