CIO

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Data Engineering 338

Data Engineering Analytics Engineering Data 338

CIO

MARCH 27, 2024

For Huawei, digitally transforming manufacturing through advanced ICT including 5G technologies, cloud computing, big data and AI, is the key to reshaping industries for the future. With plans to expand, the company needed to upgrade its IT capabilities especially for storage and processing.

Automotive 130

Automotive Storage Systems Review Technical Review 130

CTOvision

JULY 9, 2014

The term “Big Data” has become synonymous with this evolution. But still, many of our customers continue to ask, “What is Big Data?”, “What are its use cases?”, What is Big Data? The data is too big, moves too fast, or does not fit the structures of your database architectures.

Big Data 114

Big Data Data CTO Fractional CTO 114

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Storage provisioning.

Big Data 63

Big Data Data Storage Microservices 63

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

Architecture 52

Architecture Data Storage Machine Learning 52

TechCrunch

SEPTEMBER 27, 2021

As more enterprises migrate to cloud-based architectures, they are also taking on more applications (because they can) and, as a result of that, more complex workloads and storage needs. Firebolt raises $127M more for its new approach to cheaper and more efficient Big Data analytics.

Artificial Inteligence 246

Artificial Inteligence Applications Cloud Artificial Intelligence 246

Agile Engine

FEBRUARY 20, 2019

2018 was the second consecutive year when Gartner published an obituary of Big Data. No one, including Gartner, thinks Big Data is dead. Au contraire, Big Data has grown so ubiquitous it became “just data”, argue the authors of the obituaries. Trend 1: From Big Data to “Just Data”.

Big Data 71

Big Data Google Cloud Data Azure 71

KitelyTech

FEBRUARY 3, 2021

Big data and data science are important parts of a business opportunity. How companies handle big data and data science is changing so they are beginning to rely on the services of specialized companies. User data collection is data about a user who is collected for market research purposes.

Big Data 52

Big Data Data Development Business Intelligence 52

Dell EMC

MAY 28, 2020

About 20 years ago, I started my journey into data warehousing and business analytics. Over all these years, it’s been interesting to see the evolution of big data and data warehousing, driven by the rise of artificial intelligence and widespread adoption of Hadoop. READ MORE.

Big Data 72

Big Data Data Artificial Intelligence Artificial Inteligence 72

CIO

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer vs. data architect.

Data Engineering 326

Data Engineering Analytics Engineering Data 326

CTOvision

FEBRUARY 18, 2015

On Tuesday, January 27, 2015 CTOvision publisher and Cognitio Corp co-founder Bob Gourley hosted an event for federal big data professionals. The breakfast event focused on security for big data designs and featured the highly regarded security architect Eddie Garcia. By Katie Kennedy. Learn More about Cloudera here.

Big Data 119

Big Data Data Hardware Authentication 119

CTOvision

DECEMBER 16, 2014

It is no secret that today’s data intensive analytics are stressing traditional storage systems. SSD) to bolster the performance of traditional storage platforms and support the ever-increasing IOPS and bandwidth requirements of their applications.

Storage 104

Storage Analytics System Data 104

Cloudera

JANUARY 12, 2018

I mentioned in an earlier blog titled, “Staffing your big data team, ” that data engineers are critical to a successful data journey. And the longer it takes to put a team in place, the likelier it is that your big data project will stall. Then this information must be executed against by the data engineers.

Data Engineering 55

Data Engineering Big Data Engineering Data 55

AWS Machine Learning - AI

FEBRUARY 26, 2024

Our proposed architecture provides a scalable and customizable solution for online LLM monitoring, enabling teams to tailor your monitoring solution to your specific use cases and requirements. A modular architecture, where each module can intake model inference data and produce its own metrics, is necessary.

Artificial Inteligence 105

Artificial Inteligence AWS Lambda Metrics 105

Data Virtualization

AUGUST 26, 2020

Recently, we have seen the rise of new technologies like big data, the Internet of things (IoT), and data lakes. But we have not seen many developments in the way that data gets delivered. Modernizing the data infrastructure is the.

Architecture 59

Architecture Data Big Data IoT 59

RapidValue

OCTOBER 12, 2020

In an effort to avoid the pitfalls that come with monolithic applications, Microservices aim to break your architecture into loosely-coupled components (or, services) that are easier to update independently, improve, scale and manage. Key Features of Microservices Architecture. Each service is known to have its own view on data models.

Microservices 53

Microservices AWS Architecture Applications 53

CIO

JUNE 7, 2023

This requires a high-performance database that can bring ML to the data. You’ve created the right architecture to collect and store your data and the best way to keep costs low is to leverage what you have. The solution to a storage cost problem is not adding more storage; it’s finding ways to process your data in place.

Organization 130

Organization Artificial Inteligence Open Source Architecture 130

Openxcell

JANUARY 6, 2023

Just a few of the existing cloud services include servers, storage, databases, networking, software, analytics, and business intelligence. Cloud storage functions by allowing users to access and download data on any selected device, such as a laptop, tablet, or smartphone, via an Internet service connection. Public cloud.

Architecture 52

Architecture Cloud Storage Google Cloud 52

Hu's Place - HitachiVantara

SEPTEMBER 4, 2018

Enterprise Storage Forum recently published their 2018 Storage Trends survey which made some interesting observations. The biggest challenges for IT and business leaders with operating their current storage infrastructure were aging equipment and lack of storage capacity. EB shipped in 1Q 2018. EB shipped in 1Q 2018.

Storage 53

Storage Performance IoT Machine Learning 53

Xebia

JANUARY 19, 2023

The following quotes date back to those years: Data Engineers set up and operate the organization’s data infrastructure, preparing it for further analysis by data analysts and scientist. – AltexSoft All the data processing is done in Big Data frameworks like MapReduce, Spark and Flink.

Data Engineering 130

Data Engineering Engineering Data Technical Review 130

CIO

MARCH 24, 2023

A framework for managing data 10 master data management certifications that will pay off Big Data, Data and Information Security, Data Integration, Data Management, Data Mining, Data Science, IT Governance, IT Governance Frameworks, Master Data Management

Government 357

Government Data Technical Review Policies 357

CTOvision

AUGUST 25, 2014

Current architectures, unfortunately, segment these efforts into distinct, separate systems, requiring costly duplication to provide these capabilities. Leverage Analytical Partners – Why an EDH is the best way to connect your existing applications and tools to big data. Rethink Analytics. Register at: [link].

Analysis 98

Analysis Architecture Data Big Data 98

Altexsoft

FEBRUARY 11, 2023

To get a better understanding of a data architect’s role, let’s clear up what data architecture is. Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. Sample of a high-level data architecture blueprint for Azure BI programs.

Data 87

Data Data Engineering Big Data Architecture 87

Falcon Chronicles

OCTOBER 24, 2018

It has essentially allowed businesses and institutions of all types to gain access to on-demand computing, storage and more. It also made collaboration more seamless as it provided the opportunity for shared data sources and easier sharing of results. The post Genomics Big Data Security: Cloud Today, Blockchain Tomorrow?

Blockchain 40

Blockchain Big Data Cloud Data 40

Perficient

MARCH 19, 2024

If you have built or are building a Data Lake on the Google Cloud Platform (GCP) and BigQuery you already know that BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence.

Data 111

Data Google Cloud Analytics Serverless 111

Cloudera

FEBRUARY 7, 2019

With the right technology now in place, ATB Financial is landing and curating more data than ever to bring data-driven insights to the business and its customers. Implementing a Modern Data Architecture. ATB Financial is also the first to use SAS Viya to interface between SAS tools and HDP. Check out our customer stories.

Big Data 65

Big Data Analytics Google Cloud Cloud 65

Dzone - DevOps

JANUARY 20, 2023

It has been a norm to perceive that distributed databases use the method of adding cheap PC(s) to achieve scalability (storage and computing) and attempt to store data once and for all on demand. Having a distributed and scalable graph database system is highly sought after in many enterprise scenarios.

Scalability 73

Scalability Fashion Architecture Hardware 73

CIO

MAY 16, 2023

Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data. Its algorithms and architecture make it resistant to tampering and, thus, ideal for storing financial records, medical data, or other sensitive information.

Innovation 249

Innovation Blockchain Artificial Intelligence Artificial Inteligence 249

Apps Associates

MAY 18, 2021

Performing analytics on the data was possible but took a long time and was mostly done in batch (using map reduce routines written in Java). Although on-premises Hadoop clusters ushered in the era of Big Data and supported data use cases that previously were not possible, many of these systems proved to be costly and cumbersome to maintain.

Cloud 98

Cloud Data Azure Analytics 98

Altexsoft

MAY 27, 2020

Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements. Reasonably, with the access to data, anyone with a computer can train a machine learning model today.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

OTS Solutions

JUNE 2, 2023

has been transforming the manufacturing sector through the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data analytics. and Big Data Analytics in Predictive Maintenance Industry 4.0 is also enabling the use of big data in predictive maintenance.

Mobile 130

Mobile Artificial Inteligence Industry IoT 130

Cloudera

SEPTEMBER 24, 2021

One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. The Dawn of Telco Big Data: 2007-2012. Suddenly, it was possible to build a data model of the network and create both a historical and predictive view of its behaviour.

Analytics 95

Analytics Network Innovation IoT 95

Cloudera

JULY 27, 2021

While billing used to be one of two critical things for any successful telco (the other being the network), today’s digital service providers prioritise channels, ecosystems, payments and cloud service architectures in enterprise architecture. Big Data has long been a growth area in telecom,’ he told me.

Telecommunications 61

Telecommunications Data Architecture Big Data 61

Cloudera

JANUARY 26, 2022

Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. The hybrid cloud’s premise—two data architectures fused together—gives companies options to leverage those solutions and to address decision-making criteria, on a case-by-case basis. .

Cloud 87

Cloud Data Architecture Operating System 87

Infinidat

OCTOBER 4, 2023

Infinidat is very aware of the global cybersecurity threat and we help our enterprise customers by including core cyber resilience technology in all our enterprise storage solutions. One of the reasons we see with large enterprises is that cybersecurity and cyber storage resilience are usually treated separately. Be vigilant! #3

Storage 72

Storage Development Team Review Malware Systems Review 72

Dzone - DevOps

FEBRUARY 16, 2023

Also, we got more and more emails from our colleagues asking if we could make data analysis easier for them. That's when the company assembled a big data team to tackle the beast. The first thing we did was to revolutionize our data processing architecture. We wanted support for point queries of high concurrency.

Data 71

Data ChatGPT Storage Architecture 71

Cloudera

JUNE 13, 2023

Data and analytics are the essential engines in this strategy which entails upgrading the bank’s core banking technology to deliver reliable, efficient, and secure services for their customers while safeguarding critical data.

Analytics 79

Analytics Data Banking Big Data 79