TIBCO - Connected Intelligence

MARCH 4, 2021

Here are just some of the reasons TIBCO continues to lead in the way: Sparks DSML Innovation: TIBCO continues to fuel innovation with cutting-edge DSML technology, such as dynamic learning on streaming data and IoT capabilities. Encourages Collaboration: Today, analytics + data science is a team sport spanning business and IT.

Machine Learning 72

Machine Learning Artificial Inteligence Software Analytics 72

Trigent

NOVEMBER 3, 2022

The future of the global supply chain market lies in IoT, integrated solutions, data, and mobility. Connected logistics devices generate a massive amount of data. The ultimate goal of any organization dealing with a pool of connected devices and sensors is to leverage this data by learning the trends and patterns.

Analytics 52

Analytics Data IoT Trends 52

Altexsoft

FEBRUARY 8, 2021

And planning, in turn, relies on understanding of current performance, past trends, existing risks, and possible future scenarios. There are two main approaches to demand planning: Traditional statistical methods make forecasts based on historical data and assume the continuation of existing trends. Cost control.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Apiumhub

JUNE 22, 2021

Data Innovation Summit topics. Same as last year, the event offers six workshops (crash-course) themes, each dedicated to a unique domain area: Data-driven Strategy, Analytics & Visualisation, Machine Learning, IoT Analytics & Data Management, Data Management and Data Engineering.

Innovation 83

Innovation Data Technical Review Artificial Inteligence 83

Altexsoft

FEBRUARY 22, 2022

Predictive maintenance (PdM) involves constant monitoring of your equipment condition and conducting repairs only when bad trends are detected – but before breakdowns occur. Reporting and analytics is essential to obtain a bird eye view of your fleet and make data-based decisions.

Software Review 52

Software Review Technical Review Software Technology 52

Altexsoft

DECEMBER 9, 2020

Often, no technologies are involved in data analysis. But decisions are mostly made based on intuition, experience, politics, market trends, or tradition. Usually, there’s no dedicated engineering expertise; instead, existing software engineers are engaged in data engineering tasks as side projects.

Analytics 102

Analytics Technical Review Technology Applications 102

Mobilunity

DECEMBER 28, 2022

Modeling is also a critical task for freelancer machine learning engineers. This involves creating models that can be used to predict outcomes or trends based on data. Some machine learning freelance engineers can also specialize in deep learning. Statistical data analytics. IoT development. ML modeling.

Machine Learning 52

Machine Learning Artificial Inteligence Engineering How To 52

Mobilunity

MAY 8, 2023

Key zones of an Enterprise Data Lake Architecture typically include ingestion zone, storage zone, processing zone, analytics zone, and governance zone. Ingestion zone is where data is collected from various sources and ingested into the data lake. Storage zone is where the raw data is stored in its original format.

Engineering 52

Engineering Data Storage AWS 52

Apiumhub

APRIL 5, 2022

M2- Data Engineering Stage: Technical track focusing on agile approaches to designing, implementing and maintaining a distributed data architecture to support a wide range of tools and frameworks in production. Presentations by some of the leading experts, researchers and practitioners in the area.

Innovation 52

Innovation Data Artificial Inteligence Case Study 52

Apiumhub

APRIL 8, 2021

With the Data Science industry continually evolving, there can be a lot to keep up with. New trends are coming up quite frequently, and if you want to do a good job and improve your skills, you must keep yourself up-to-date. As the director of Advertisement, he works to help data-driven businesses be more successful.

Artificial Inteligence 109

Artificial Inteligence Technical Advisors Data Machine Learning 109

TIBCO - Connected Intelligence

MARCH 5, 2020

In part two, I provide my list of 10 sure bet technology trends you can count on to continue. 10 Technology Trends You Can Count On In 2020. The business value of data continues to rise – Every business transformation today—be it greater customer intimacy, more optimized operations, faster innovation, and more—is fueled by data.

Technology 42

Technology Games Trends IoT 42

TIBCO - Connected Intelligence

DECEMBER 7, 2021

While data-driven organizations have more information to work with than ever before, this also means dealing with more data sources, siloed data , complexity in data integration and data access, and growing data compliance mandates.

Architecture 49

Architecture Data Government Agile 49

Cloudera

JANUARY 20, 2021

Managing the collection of all the data from all factories in the manufacturing process is a significant undertaking that presents a few challenges: Difficulty assessing the volume and variety of IoT data: Many factories utilize both modern and legacy manufacturing assets and devices from multiple vendors, with various protocols and data formats.

Data 103

Data Artificial Inteligence Analytics Machine Learning 103

Altexsoft

MARCH 16, 2021

In general, a data infrastructure is a system of hardware and software tools used to collect, store, transfer, prepare, analyze, and visualize data. Check our article on data engineering to get a detailed understanding of the data pipeline and its components. Big data infrastructure in a nutshell.

Big Data 116

Big Data Healthcare Applications Data 116

Altexsoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

DECEMBER 20, 2022

Across the energy supply chain from generation to consumer, we can see that the trend toward investing in renewable energy has picked up pace as demand has grown for energy companies to actively pursue investments in energies with little or no environmental impact in the quest for decarbonisation.

Energy 79

Energy Analytics Data Machine Learning 79

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

APRIL 13, 2023

Leading executives focus on building resilient and intelligent supply chains that can withstand the turmoil due to data-based proactive decisions. According to the PwC 2023 Digital Trends in Supply Chain Survey , 86 percent of respondents believe they should invest more in technology to identify, track, and measure supply chain risk.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

JULY 19, 2022

That’s exactly what every data-driven organization has been trying to find for years,” someone would come up with a new, better solution. Data mesh is another hot trend in the data industry claiming to be able to solve many issues of its predecessors. Decentralized data ownership by domain.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

JULY 23, 2020

As a logical reaction to this problem, a new trend — MLOps — has emerged. It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

Altexsoft

MAY 14, 2021

To understand Big Data, you need to get acquainted with its attributes known as the four V’s: Volume is what hides in the “big” part of Big Data. This relates to terabytes to petabytes of information coming from a range of sources such as IoT devices, social media, text files, business transactions, etc.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

OCTOBER 26, 2022

This can be anything from CRMs to AI-powered chatbots to IoT systems. Things like business knowledge, in-depth understanding of digital structures, experience in project management, and comprehensive know-how in areas such as big data, blockchain, AI, IoT, etc. Attract talent. are important for the CDO to do their job.

CTO 52

CTO Organization Technical Review Culture 52

Altexsoft

JUNE 26, 2023

Note that in many cases, the process of gathering information never ends since you always need fresh data to re-train and improve existing ML models, gain consumer insights, analyze current market trends, and so on. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

BPM

JANUARY 18, 2019

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. I can see two major trends in this development: Simplification. AI-enabled data engines will provide insight about what processes can be redesigned and/or automated.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Altexsoft

AUGUST 22, 2022

Gartner claims that there is such a solution capable of enabling companies to have advanced, flexible, and reusable data management across all environments. The solution is called a data fabric. to provide a unified view of all enterprise data. How to approach data fabric implementation.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 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

Existek

JANUARY 11, 2022

Being a market leader, AWS continues bringing new trends and approaches. Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. Want to find more on current market trends in cloud app development? Read the article.

Azure 52

Azure AWS Cloud How To 52

Altexsoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Data sources component in a modern data stack.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Cloudera

FEBRUARY 19, 2018

But when companies are looking towards new technologies such as data lakes, machine learning or predictive analytics, SAP alone is just not enough. To keep up with tech trends, businesses have to face the challenges of integrating SAP with non-SAP technologies and embark on a crusade against data silos. SAP as a must-have.

Data 46

Data Government Big Data Compliance 46

O'Reilly Media - Ideas

MARCH 6, 2019

Spotlight on Innovation: AI Trends with Roger Chen , March 13. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. IoT Fundamentals , April 4-5.

Course 10

Course Artificial Inteligence Training Microservices 10

O'Reilly Media - Ideas

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. The term “ML” is No.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8