Últimas publicaciones en los blogs

10 Tips for a Successful Cloud Migration

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"Paradisiac cloud migration"

Migrating applications, data, and other workloads to the cloud is now commonplace among businesses of all sizes. A 2018 report on cloud computing trends revealed that 92 percent of companies now use public cloud services.
However, cloud migration remains a complex undertaking with several challenges, and getting it right is important. Read on to find out the challenges of cloud migration and ten tips for building a smart cloud migration plan..

Understanding Database Cloud Migration

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Cloud computing is the new normal. Every time you are using a service or an application through an internet connection, you are using the cloud. Since most companies nowadays have part or all their databases hosted on the cloud, you ask how is the best way to do it. Migrating your database to the cloud can help you manage your workload by making your data available and easily scalable. Read on to learn about database cloud migration and tips to do it right..

Using Machine Learning/AI to Boost the Supply Chain: 5 Use Cases

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Industry - AI to boost the Supply ChainThis article will discuss how supply chains are being improved through the use of innovative technologies before highlighting five uses of artificial intelligence and machine learning in supply chains.

When you finish reading, you’ll understand why many industry analysts have described A.I. technologies as disruptive innovations that have the potential to alter and improve operations across entire supply chains..

Open Source for Big Data: An Overview

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Software Open SourceThis article will describe the relevance of open source software and big data before describing five interesting and useful open source big data tools and projects.

Big data workloads are those that involve the processing, storage, and analysis of large amounts of unstructured data to derive business value from that data. Traditional computing approaches and data processing software weren’t powerful enough to cope with big data, which typically inundates organizational IT systems on a daily basis.

The widespread adoption of Big Data analytics workloads over the past few years has been driven, in part, by the open source model, which has made frameworks, database programs, and other tools available to use and modify for those who want to delve into these big data workloads..

Defining the Difference between Deep Learning, Machine Learning, and Artificial Intelligence

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Deep learning and Artificial IntelligenceThis article will discuss three currently trending technology topics, namely deep learning, machine learning, and artificial intelligence.

According to research, 80 percent of enterprises are actively investing in AI technologies. The machine learning market has a projected value of $8.81 billion by 2022, while the deep learning market size is expected to reach $10.2 billion by 2025...

What is Storage Tiering and How Can it Reduce Storage Costs?

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Tiered storageTiered storage is a way of managing data by assigning it to different types of storage devices/media depending on the current value that the underlying information provides. The efficient management of data recognizes that all information provides an intrinsic value from the time it’s created to the time it becomes obsolete and that this value changes over the information lifecycle.

The typical factor determining the value of information is how frequently you access it, however, policy-based rules can factor a number of other issues to determine information value. For example, old bank transactions, which might have a low value, could suddenly shift in value depending on special circumstances, such as a tax audit. This article discusses some pros, cons, and best practices for tiered storage..

Software Testing

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Software testing is the process to evaluate a software item to detect differences between a given input and expected output. It should be started during development process as you will reduce the possibilities of returning to development phase due to unexpected errors/missing functionalities.

Software testingIt meets two steps:

  • Verification: Software has the expected behavior.

  • Validation: Meets client requirements..

The Importance of Big Data Disaster Recovery

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Analytics information big data Disaster recovery is a set of processes, techniques, and tools used to swiftly and smoothly recover vital IT infrastructure and data when an unforeseen event causes an outage.

The statistics tell the best story about the importance of disaster recovery—98 percent of organizations reported that a single hour of downtime costs over $100,000, while 81 percent indicated that an hour of downtime costs their business over $300,000...