Altexsoft

JUNE 25, 2019

Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. 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.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Altexsoft

JANUARY 22, 2020

With the uprise of internet-of-things (IoT) devices, overall data volume increase, and engineering advancements in this field led to new ways of collecting, processing, and analysing data. As a result, it became possible to provide real-time analytics by processing streamed data. Oracle Stream Analytics.

Analytics 20

Analytics Data IoT Analysis 20

BPM

JANUARY 18, 2019

The current Artificial Intelligence (AI) fascination is unfortunately completely biased on Deep Neural Networks (DNN) and Machine Learning (ML) for everything. As we move into a world that is more and more dominated by technologies such as big data, IoT, and ML, more and more processes will be started by external events.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Existek

JANUARY 11, 2022

For example, they considerably revised the cloud strategy due to the need to transform the delivery model from PaaS to IaaS, thus renaming Windows Azure to Microsoft Azure in 2014. . Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT.

Azure 52

Azure AWS Cloud How To 52

Altexsoft

MARCH 31, 2020

These can be data science teams , data analysts, BI engineers, chief product officers , marketers, or any other specialists that rely on data in their work. The simplest illustration for a data pipeline. Data pipeline components. Data pipeline components. When do you need a data pipeline?

Data 76

Data Storage Analytics Data Center 76

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. How data engineering works in a nutshell. Machine learning.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

JUNE 7, 2021

No real-time data processing. MapReduce performs batch processing only and doesn’t fit time-sensitive data or real-time analytics jobs. Data engineers who previously worked only with relational database management systems and SQL queries need training to take advantage of Hadoop. Complex programming environment.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

DECEMBER 9, 2020

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. Analytics maturity model. Building data-centered culture.

Analytics 102

Analytics Technical Review Technology Applications 102

Hu's Place - HitachiVantara

JANUARY 15, 2019

When the formation of Hitachi Vantara was announced, it was clear that combining Hitachi’s broad expertise in OT (operational technology) with its proven IT product innovations and solutions, would give customers a powerful, collaborative partner, unlike any other company, to address the burgeoning IoT market.

Systems Review 72

Systems Review Storage System Software Review 72

Cloudera

MAY 18, 2021

Finally, IaaS deployments required substantial manual effort for configuration and ongoing management that, in a way, accentuated the complexities that clients faced deploying legacy Hadoop implementations in the data center. Machine Learning Prototypes. data streaming, data engineering, data warehousing etc.),

Cloud 85

Cloud Technical Review Storage Backup 85