Gorilla Logic

APRIL 27, 2022

Are you a data engineer or seeking to become one? This is the first entry of a series of articles about skills you’ll need in your everyday life as a data engineer. Data cleansing and enrichment processes need to combine, filter, aggregate, and select different sets to answer questions we have.

Data Engineering 59

Data Engineering Engineering Data Windows 59

CIO

JUNE 22, 2023

Technology has quickly become a top priority for businesses across every industry. Consulting In the consulting industry, technology has become an important tool for making decisions, designing solutions, improving processes, and providing insights on optimizing business strategy. Average salary: US$131,995 Increase since 2021: 0% 2.

Industry 286

Industry UI/UX Telecommunications Quality Assurance 286

Altexsoft

DECEMBER 9, 2020

Moreover, the MicroStrategy Global Analytics Study reports that access to data is extremely limited, taking 60 percent of employees hours or even days to get the information they need. Different technologies and methods are used and different specialists are involved. Often, no technologies are involved in data analysis.

Analytics 102

Analytics Technical Review Technology Applications 102

Hu's Place - HitachiVantara

JANUARY 3, 2019

Against this backdrop there are five trends for 2019 that I would like to call out. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

O'Reilly Media - Ideas

JANUARY 8, 2019

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. Cloud for data infrastructure.

Trends 87

Trends Data Machine Learning Artificial Inteligence 87

CIO

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems.

Analytics 314

Analytics Data Analysis Business Analytics 314

ParkMyCloud

MAY 29, 2019

Trends in cloud jobs can be overall indicators into trends in the cloud computing space. Here are some trends we’re seeing. Cloud Talent Demand Trends. A Cloud Consultant’s expert knowledge of cloud service providers and cloud technologies available is key to provide maximum value to an enterprise.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

CIO

JULY 6, 2023

OCI’s Supercluster includes OCI Compute Bare Metal, which provides an ultralow-latency remote direct access memory (RDMA) over a Converged Ethernet (RoCE) cluster for low-latency networking, and a choice of high-performance computing storage options.

Generative AI 216

Generative AI Artificial Inteligence Strategy Google Cloud 216

TIBCO - Connected Intelligence

MARCH 5, 2020

Over the past sixty years, technology has redefined how people live and work, how commerce occurs, how citizens and governments engage, and so much more. With digital transformation critical to every organization’s survival, it makes sense that organizations want to leverage and gain advantage from technology advancements.

Technology 42

Technology Games Trends IoT 42

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. Technological solutions to fleet maintenance. To support your preventive maintenance strategy, you have two technological options.

Software Review 52

Software Review Technical Review Software Technology 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

Erik Bernhardsson

DECEMBER 15, 2020

Decades ago, software engineering was hard because you had to build everything from scratch and solve all these foundational problems. You need storage to build something to serve 1M concurrent users? But the fact that non-engineers are building technology validates that there's demand for engineers.

Software Engineering 304

Software Engineering Engineering Tools Software 304

Hu's Place - HitachiVantara

MAY 24, 2019

The latest International Data Corporation ( IDC ) Worldwide Quarterly Enterprise Storage Systems Tracker , was published on March 4, 2019. It showed vendor revenue in the worldwide enterprise storage systems market is still increasing: 7.4% enterprise storage market in 4Q 2018. year over year to $14.5 This takes DataOps.

Marketing 75

Marketing Storage Systems Review Enterprise 75

Exadel

FEBRUARY 20, 2023

The technological landscape has evolved to include AI assistants, self-driving cars, and machine learning solutions that process data in a blink of an eye. AI is by far the technology that can drive innovation forward, and you should keep up. Cloud-based AI services make this possible. More was yet to come for AI in the cloud.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

Cloudera

OCTOBER 13, 2021

These companies have instead opted to leverage their existing data centre investment. In addition to the Cloudera Data Warehouse (CDW) and Cloudera Machine Learning (CML), CDP Private Cloud now also includes Cloudera Data Engineering (CDE). Breaking down the on-prem monolith.

Data Center 98

Data Center Cloud Data How To 98

Mentormate

OCTOBER 31, 2023

The allure of the latest machine-learning techniques is undeniable, but without a well-structured approach, you risk getting lost in the technological maze. In the world of AI, success hinges not only on technological prowess but also on aligning your solutions with the broader business strategy.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Altexsoft

MARCH 31, 2020

It means you must collect transactional data and move it from the database that supports transactions to another system that can handle large volumes of data. And, as is common, to transform it before loading to another storage system. But how do you move data? The simplest illustration for a data pipeline.

Data 76

Data Storage Analytics Data Center 76

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. This is resulting in advancements of what is provided by the technology, and a resulting shift in the art of the possible. Correlations across data domains, even if they are not traditionally stored together (e.g. One other example highlights this trend.

Data 97

Data Analytics Storage Big Data 97

O'Reilly Media - Data

JULY 18, 2018

Because large deep learning architectures are quite data hungry, the importance of data has grown even more. In this short talk, I describe some interesting trends in how data is valued, collected, and shared. Economic value of data. User expectations for what happens to data they generate has changed.

Machine Learning 142

Machine Learning Artificial Inteligence Data Marketing 142

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. No wonder only 0.5

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Kentik

MAY 10, 2023

A unified data repository (UDR) is a centralized storage system that consolidates, organizes, and manages data from different sources. It serves as a single point of access for various types of data, which could be a significant volume of very diverse data formats in an extensive network. Why do we need a UDR?

Analytics 59

Analytics Network Data Machine Learning 59

Hu's Place - HitachiVantara

MAY 10, 2019

In the past 10 years the incidents of fraud have escalated thanks to new technologies, like mobile, that have been adopted by banks to better serve their customers. This will require another product for data governance. Data Preparation : Data integrationthat is intuitive and powerful.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

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

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 81

Energy Analytics Data Machine Learning 81

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

Altexsoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. What is Big Data analytics? Data storage and processing.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

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

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Altexsoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. Virtually, Hadoop puts no limits on the storage capacity. What is Hadoop.

Big Data 98

Big Data Tools Data Storage 98

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Data 110

Data Artificial Inteligence Analytics Machine Learning 110

Existek

JANUARY 11, 2022

Being a market leader, AWS continues bringing new trends and approaches. In 2010, they launched Windows Azure, the PaaS, positioning it as an alternative to Google App Engine and Amazon EC2. They provided a few services like computing, Azure Bob storage, SQL Azure, and Azure Service Bus.

Azure 52

Azure AWS Cloud How To 52

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

Cloudera

AUGUST 24, 2020

Today we are continuing our discussion with Martin Mannion , EMEA Big Data Community lead at Deloitte and Paul Mackay, the EMEA Cloud Lead at Cloudera to look at why security and governance requirements must be tackled in the early stages of data-led use case development, thereby mitigating more work later on. Thanks for joining us.

Cloud 81

Cloud Data Government Innovation 81

Altexsoft

AUGUST 19, 2022

One of the important steps away from spreadsheets and towards developing your BI capabilities is choosing and implementing specialized technology to support your analytics endeavors. Power BI helps arrange disparate data from multiple sources and make it understandable for end users. What is Power used for? per user/month).

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

RapidValue

JULY 21, 2019

From DevOps to DataOps DataOps can be simply stated as “DevOps for data”. It is a set of practices and technologies that integrate the development and operation of data movement architectures into a continuous process. DataOps aids data practitioners to continuously deliver quality data to applications and business processes.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. It aims at making data assets understandable and discoverable for users. Metadata storage usually implies developing a specialized repository. Metadata storage.

Tools 59

Tools Technical Review Software Review Systems Review 59