CIO

FEBRUARY 14, 2023

Titanium Intelligent Solutions, a global SaaS IoT organization, even saved one customer over 15% in energy costs across 50 distribution centers , thanks in large part to AI. DataStax Real-time data and decisioning First, a few quick definitions. Real-time data involves a continuous flow of data in motion.

Artificial Inteligence 272

Artificial Inteligence Strategy Data Machine Learning 272

CIO

JULY 18, 2023

As technology continues to advance at a rapid pace, the demand for qualified individuals has soared,” Chan says. CIO.com’s 2023 State of the CIO survey recently zeroed in on the technology roles that IT leaders find the most difficult to fill, with cybersecurity, data science and analytics, and AI topping the list.

Technical Advisors 226

Technical Advisors Artificial Inteligence Generative AI How To 226

Capgemini

MARCH 16, 2022

New technologies make it possible to leverage the wealth of data locked in production equipment and improve its reliability, performance, and flexibility. makes it possible to consider obstacles as key elements to unlock scalability and initiate the Factory of the Future. Industry 4.0 But the question how to start remains.

Industry 98

Industry IoT Technical Review Automotive 98

Trigent

AUGUST 26, 2022

Expert assistance and agile technology solutions make your work easier. The company holds decades of technical experience, immense knowledge of different domains, and technology expertise to deliver transformation solutions for small, medium, and large-level enterprises.

Software Review 52

Software Review Software Technical Review Artificial Intelligence 52

Kentik

JANUARY 3, 2018

Looking into Network Monitoring in an IoT enabled network. This allows businesses to take advantage of the many technologies that now enable greater speed and agility, and ultimately promise more revenue. As part of the movement, organizations are also looking to benefit from the Internet of Things (IoT).

IoT 40

IoT Network Tools Big Data 40

Confluent

FEBRUARY 6, 2019

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.

Machine Learning 106

Machine Learning Artificial Inteligence Scalability Data Engineering 106

Apiumhub

SEPTEMBER 20, 2023

From software architecture to artificial intelligence and machine learning, ​​these conferences offer unparalleled insights, networking opportunities, and a glimpse into the future of technology. This event is set to be one of the largest technology gatherings in Central Europe for the upcoming autumn season.

Conference 96

Conference Artificial Inteligence UI/UX Machine Learning 96

Agile Engine

DECEMBER 27, 2023

Case study: leveraging AgileEngine as a data solutions vendor 11. Key takeaways Any organization that operates online and collects data can benefit from a data analytics consultancy, from blockchain and IoT, to healthcare and financial services The market for data analytics globally was valued at $112.8

Big Data 52

Big Data Analytics Data Analysis 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

Cloudera

MAY 18, 2021

The intent of this article is to articulate and quantify the value proposition of CDP Public Cloud versus legacy IaaS deployments and illustrate why Cloudera technology is the ideal cloud platform to migrate big data workloads off of IaaS deployments. Technology and infrastructure costs . Deployment Type. Cost (in USD).

Cloud 85

Cloud Technical Review Storage Backup 85

Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability. Data availability.

Tools 52

Tools Software Review Systems Review Testing 52

Altexsoft

DECEMBER 4, 2020

Businesses can no longer count on using the same technologies for decades. So data migration is an unavoidable challenge each company faces once in a while. Transferring data from one computer environment to another is a time-consuming, multi-step process involving such activities as planning, data profiling, testing, to name a few.

Software Review 69

Software Review Software Data Technical Review 69

Altexsoft

OCTOBER 21, 2022

Kafka can continue the list of brand names that became generic terms for the entire type of technology. Similar to Google in web browsing and Photoshop in image processing, it became a gold standard in data streaming, preferred by 70 percent of Fortune 500 companies. Scalability. Scalability is one of Kafka’s key selling points.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Hu's Place - HitachiVantara

MARCH 18, 2019

One on Smart 4 Technologies and another on Smart Energy. As a megacity Istanbul has turned to smart technologies to answer the challenges of urbanization, with more efficient delivery of city services and increasing the quality and accessibility of such services as transportation, energy, healthcare, and social services.

Healthcare 61

Healthcare Tourism Systems Review Energy 61

CTOvision

JUNE 15, 2015

They also launched a plan to train over a million data scientists and data engineers on Spark. As data and analytics are embedded into the fabric of business and society –from popular apps to the Internet of Things (IoT) –Spark brings essential advances to large-scale data processing.

Open Source 117

Open Source Big Data Machine Learning Artificial Inteligence 117

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

Datavail

SEPTEMBER 8, 2022

According to an IDG survey , companies now use an average of more than 400 different data sources for their business intelligence and analytics processes. What’s more, 20 percent of these companies are using 1,000 or more sources, far too many to be properly managed by human data engineers. Conclusion.

Data 40

Data Nonprofit Quality Assurance Education 40

Altexsoft

MARCH 16, 2021

Check our article on data engineering to get a detailed understanding of the data pipeline and its components. Big data infrastructure in a nutshell. To deliver valuable, applicable knowledge, data has to go through the following stages of a data pipeline: Big data sources continuously generate raw, real-world data.

Big Data 116

Big Data Healthcare Applications Data 116

DataRobot

MARCH 10, 2022

This “revolution” stems from breakthrough advancements in artificial intelligence, robotics, and the Internet of Things (IoT). As a result of these technological advancements, the manufacturing industry has set its sights on artificial intelligence and automation to enhance services through efficiency gains and lowering operational expenses.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Altexsoft

JULY 19, 2022

No matter how ridiculous it may sound, the famous quote is applicable to the technology world in many ways. In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. What data mesh is and is not.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

FEBRUARY 8, 2021

Manufacturing is typically characterized by producing a lot of various disparate data that is hard to organize and analyze, especially with the spread of Internet of Things (IoT) devices. Data engineers work with technologies, setting up and managing data pipelines to extract, store, and transform data for further usage.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

MARCH 14, 2023

In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support. This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Altexsoft

APRIL 13, 2023

Source: Supply Chain Dive Over the last few years, global supply chains have been so severely disrupted – but also enhanced with cutting-edge technologies. Leading executives focus on building resilient and intelligent supply chains that can withstand the turmoil due to data-based proactive decisions.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

OCTOBER 2, 2020

Key disciplines and roles in data management. Data architecture: aligning technologies with business goals. Specialist responsible for the area: data architect. Data architecture is a starting point for any data management model. Data integration and interoperability: consolidating data into a single view.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Agile Engine

DECEMBER 2, 2019

Rankings of this kind seem even more meaningful and important when they come from analytics firms focused on technology and software. In addition to covering the broader software development industry, the company also has lists that narrow down on specific domains like IoT, blockchain, and AI. About AgileEngine.

Software 19

Software Company Blockchain Software Development 19

Altexsoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes. Find sources of relevant data.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. Multiple BI tools require access to the same data sources.

Virtualization 69

Virtualization Tools Data Architecture 69

Existek

JANUARY 11, 2022

And companies that have completed it emphasize gained advantages like accessibility, scalability, cost-effectiveness, etc. . Along with meeting customer needs for computing and storage, they continued extending services by presenting products dealing with analytics, Big Data, and IoT. Read the article.

Azure 52

Azure AWS Cloud How To 52

Altexsoft

JUNE 7, 2021

scalability. 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. Data access options.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

NOVEMBER 10, 2021

Let’s elaborate on this and figure out how a data lakehouse is different from its ancestors and name inspirers in more detail. Data warehouse vs data lake vs data lakehouse: What’s the difference. Data warehouse. Moreover, they support real-time data, e.g., streams from IoT devices. websites, etc.

Architecture 52

Architecture Data Storage Machine Learning 52

Kentik

DECEMBER 15, 2016

In case you’re not familiar with the phrase “3rd Platform,” it’s part of IDC’s forward-looking vision for IT, in which 3rd Platform is defined as business Information Technology that drives innovation and continuous change and is built on the four technology pillars of cloud, big data, mobility, and social business.

UI/UX 40

UI/UX Innovation Big Data Network 40

Altexsoft

OCTOBER 24, 2019

With a data warehouse, an enterprise is able to manage huge data sets, without administering multiple databases. Such practice is a futureproof way of storing data for business intelligence (BI) , which is a set of methods/technologies of transforming raw data into actionable insights. Reflects the source data.

Architecture 50

Architecture Enterprise Data Technical Review 50

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. Stream processing tools.

Analytics 20

Analytics Data IoT Analysis 20

ParkMyCloud

JANUARY 17, 2020

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.

Google Cloud 9

Google Cloud Azure AWS Cloud 9