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Software Outsourcing: Why CEOs Love It

Gorilla Logic

. • Monetize data with technologies such as artificial intelligence (AI), machine learning (ML), blockchain, advanced data analytics , and more. Create value from the Internet of Things (IoT) and connected enterprise. Some of the most common include cloud, IoT, big data, AI/ML, mobile, and more.

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Trends in Cloud Jobs In 2019

ParkMyCloud

Business Intelligence Analyst. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist. IoT Engineer. The main responsibility of IoT engineers is to help businesses keep up with IoT technology trends. The Future of Cloud Computing Jobs.

Trends 72
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Fundamentals of Data Engineering

Xebia

. – AltexSoft All the data processing is done in Big Data frameworks like MapReduce, Spark and Flink. – Jesse Anderson The data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering.

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Less is More: The Benefits of Streamlining Your Data Integration Workflow

Datavail

Big data presents challenges in terms of volume, velocity, and variety—but that doesn’t mean you have to suffer from a bloated IT ecosystem to address these challenges. In fact, many businesses can realize significant advantages from streamlining their data integration pipelines, trimming away unnecessary tools and services.

Data 40
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The Good and the Bad of Kubernetes Container Orchestration

Altexsoft

Deployment and scaling automation to boost CI/CD Kubernetes has become an important part of a DevOps toolchain. It enables DevOps and site reliability engineer (SRE) teams to automate deployments, updates, and rollbacks. But even a seasoned software developer or DevOps engineer can find Kubernetes intimidating in the beginning.

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Industry 4.0: What Could Possibly Go Wrong?

Kentik

IoT plays a key role in sensing all of the critical elements in the physical world and delivering a continuous feed of sensor data to a Big Data analytics cluster situated in the cloud. McKinsey offers a typical definition: “We define Industry 4.0 Industry 4.0 Industry 4.0 An Industry 4.0

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Analytics Maturity Model: Levels, Technologies, and Applications

Altexsoft

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Data warehouse architecture. Analytics maturity model.

Analytics 102