Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

DataRobot

MARCH 10, 2022

DataRobot and Snowflake Jointly Unleash Human and Machine Intelligence Across the Industrial Enterprise Landscape. This “revolution” stems from breakthrough advancements in artificial intelligence, robotics, and the Internet of Things (IoT). IoT Empowered Assembly Lines: Predictive Maintenance.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Hu's Place - HitachiVantara

JANUARY 3, 2019

In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Altexsoft

AUGUST 25, 2020

Big data fosters the development of new tools for transporting, storing, and analyzing vast amounts of unstructured data. Prominent enterprises in numerous sectors including sales, marketing, research, and healthcare are actively collecting big data. Data engineering vs big data engineering.

Big Data 75

Big Data Data Engineering Engineering Data 75

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. Cost control. Quality management. Shipment tracking and vehicle maintenance.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

JULY 23, 2020

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. MLOps lies at the confluence of ML, data engineering, and DevOps.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

Altexsoft

APRIL 13, 2023

Due to extensive usage of connected IoT devices and advanced processing technologies, SCCTs not only gather data and build operational reports but also create predictions, define the impact of various macro- and microeconomic factors on the supply chain, and run “what-if” scenarios to find the best course of action.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

O'Reilly Media - Ideas

MAY 1, 2019

Spotlight on Data: Caching Big Data for Machine Learning at Uber with Zhenxiao Luo , June 17. Introduction to Distributed Ledger Technology for Enterprise , July 10. Spotlight on Data: Data as an Asset with Friederike Schüür and Jen van der Meer , May 20. Data science and data tools.

Course 46

Course Training Artificial Inteligence Machine Learning 46

Altexsoft

JUNE 25, 2019

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. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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. Sales region: Northern California.

Analytics 20

Analytics Data IoT Analysis 20

Existek

JANUARY 11, 2022

It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. . They quickly met the full range of enterprise needs starting from SaaS to PaaS and IaaS. Both small businesses and enterprises shift computing tasks to the cloud. Business apps.

Azure 52

Azure AWS Cloud How To 52

Cloudera

OCTOBER 29, 2020

With the massive explosion of data across the enterprise — both structured and unstructured from existing sources and new innovations such as streaming and IoT — businesses have needed to find creative ways of managing their increasingly complex data lifecycle to speed time to insight.

Data 111

Data Machine Learning Artificial Inteligence Analytics 111

ParkMyCloud

MAY 29, 2019

As more and more enterprises drive value from container platforms, infrastructure-as-code solutions, software-defined networking, storage, continuous integration/delivery, and AI, they need people and skills on board with ever more niche expertise and deep technological understanding. IoT Engineer. Cloud Consultants.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

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

ParkMyCloud

JANUARY 17, 2020

For individuals who perform complex Big Data analyses and have at least two years of experience using AWS. Implement core AWS Big Data services according to basic architecture best practices. Design and maintain Big Data. Leverage tools to automate data analysis. Azure Data Engineer Associate.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

Kentik

MAY 31, 2023

Reliability is a massive burden for network operators While DevOps teams and their SREs focus on the reliability of the application environment, an enterprise’s network concerns often extend well beyond the uptime of a suite of customer-facing web and mobile apps.

Network 52

Network Cloud Load Balancer Firewall 52

Altexsoft

OCTOBER 24, 2019

While our brain is both the processor and the storage, companies need multiple tools to work with data. And one of the most important ones is a data warehouse. In this article, we will discuss what an enterprise data warehouse is, its types and functions, and how it’s used in data processing.

Architecture 50

Architecture Enterprise Data Technical Review 50

O'Reilly Media - Ideas

MARCH 6, 2019

Product Management for Enterprise Software , April 17. 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. What You Need to Know About Data Science , April 1.

Course 10

Course Artificial Inteligence Training Microservices 10

Hu's Place - HitachiVantara

MARCH 18, 2019

City of Istanbul Governorship: Safe, Smart Campus The challenge was to secure the governorship campus and include multiple existing video and IoT systems. This enables real time alerts and analysis from facial recognition, traffic and parking, queue detection, people counting and license plate recognition.

Healthcare 61

Healthcare Tourism Systems Review Energy 61

Altexsoft

OCTOBER 26, 2022

This is how Deloitte defines this role, “The CDO is a catalyst and driver of change, bringing a new culture and mindset, and new ways of working to the enterprise, that would otherwise not have happened or would have happened too slowly.”. This can be anything from CRMs to AI-powered chatbots to IoT systems. CIO vs CDO.

CTO 52

CTO Organization Technical Review Culture 52

Altexsoft

JUNE 26, 2023

While all three are about data acquisition, they have distinct differences. Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. Data integration , on the other hand, happens later in the data management flow.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

BPM

JANUARY 18, 2019

Process mining will be incorporated into larger suites of professional business process analysis tools. This will aid the faster creation of baseline models, enabling analysis and process improvement. Enterprises must consider where and when to apply RPA, to achieve the expected value returns. Steven Stanton FCB Partners [link].

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Altexsoft

JULY 19, 2022

Through all these shifts, data mesh is called to solve the problems of centralized data platforms by giving more flexibility and independence, agility and scalability, cost-effectiveness, and cross-functionality. Data mesh principles and architecture. Decentralized data ownership by domain.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

OCTOBER 2, 2020

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. Fitting into wider enterprise architecture, it outlines how data is collected, integrated, transformed, stored, and used.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Virtualization 69

Virtualization Tools Data Architecture 69

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO Cloud Integration was identified as a “Leader” in four analyst reports, including the 2021 Gartner ® Magic Quadrant for EiPaaS , the Forrester Wave Enterprise iPaaS, Q4, 2021 , the 2021 Nucleus Research iPaaS Value Matrix , and the 2021 Aragon Research Globe Digital Business Business Platform Report.

Innovation 26

Innovation Analytics Virtualization Cloud 26

Altexsoft

AUGUST 22, 2022

The landscape of enterprise data is fragmented. Organizations have data stored in public and private clouds, as well as in various on-premises data repositories. Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52

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

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Typically used for data analysis and reporting, data warehouses rely on ETL mechanisms to extract , transform , and load data into a destination.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. process data in real time and run streaming analytics. In other words, Kafka can serve as a messaging system, commit log, data integration tool, and stream processing platform. Red Hat , acquired by IBM.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

DECEMBER 9, 2020

In the era of global digital transformation , the role of data analysis in decision-making increases greatly. Data is collected to provide a better understanding of the reality, and in most cases, the only reports available are the ones reflecting financial results. Often, no technologies are involved in data analysis.

Analytics 102

Analytics Technical Review Technology Applications 102

TIBCO - Connected Intelligence

FEBRUARY 18, 2020

To democratize, collaborate, and operationalize data science and machine learning (ML) across your entire organization, you need to make data science a team sport. TIBCO can help empower everyone from data scientists and citizen data scientists to data engineers to business users to developers with flexible and extensible tools.

Artificial Inteligence 42

Artificial Inteligence Machine Learning Data Sport 42

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

That is accomplished by delivering most technical use cases through a primarily container-based CDP services (CDP services offer a distinct environment for separate technical use cases e.g., data streaming, data engineering, data warehousing etc.) data streaming, data engineering, data warehousing etc.),

Cloud 85

Cloud Technical Review Storage Backup 85

Altexsoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59