CIO

FEBRUARY 14, 2023

Organizations have balanced competing needs to make more efficient data-driven decisions and to build the technical infrastructure to support that goal. This can only be achieved if the underlying data infrastructure is unified, robust, and efficient. The storage for these features is referred to as a feature store.

Artificial Inteligence 270

Artificial Inteligence Strategy Data Machine Learning 270

CIO

SEPTEMBER 2, 2022

In-demand skills for the role include programming languages such as Scala, Python, open-source RDBMS, NoSQL, as well as skills involving machine learning, data engineering, distributed microservices, and full stack systems. Data engineer.

Software Engineering 246

Software Engineering Data Engineering DevOps AWS 246

CIO

AUGUST 31, 2022

In-demand skills for the role include programming languages such as Scala, Python, open-source RDBMS, NoSQL, as well as skills involving machine learning, data engineering, distributed microservices, and full stack systems. Data engineer.

Software Engineering 246

Software Engineering Data Engineering DevOps AWS 246

Altexsoft

FEBRUARY 11, 2023

In the article, we explore the role of a data architect, discuss the responsibilities and required skills, and share what kind of companies may need such a specialist. What is a data architect? Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company.

Data 87

Data Data Engineering Big Data Architecture 87

Mobilunity

MAY 8, 2023

An Enterprise Data Lake is a large, centralized repository that stores structured and unstructured data at scale. It is designed to store vast amounts of data from a variety of sources. Moreover an Enterprise Data Lake makes it accessible to data scientists, analysts, and other users across the enterprise.

Engineering 52

Engineering Data Storage AWS 52

Hu's Place - HitachiVantara

APRIL 11, 2019

Few if any data management frameworks are business focused, to not only promote efficient use of data and allocation of resources, but also to curate the data to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need.

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

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

Marketing 75

Marketing Storage Systems Review Enterprise 75

RapidValue

JULY 21, 2019

DataOps aids data practitioners to continuously deliver quality data to applications and business processes. The end-users of data, like the data analysts and data scientists, work closely with both data engineers and IT Ops in order to deliver continuous data movement. DataOps and Cloud.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Cloudera

FEBRUARY 1, 2024

The other half of the equation requires your team’s emphasis to shift to sustained excellence in managing and optimizing your data ecosystem — better known as Day 2 operations. In this blog, we’ll cover the highlights of our recently published Day 2 Operations Guide and why it matters to enterprises.

Backup 82

Backup Cloud Architecture Resources 82

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 85

Trends Data Machine Learning Artificial Inteligence 85

O'Reilly Media - Data

APRIL 3, 2018

Data Science and Machine Learning sessions will cover tools, techniques, and case studies. This year, we have many sessions on managing and deploying models to production, and applications of deep learning in enterprise applications. Unlocking popular data types: Text, temporal data, and graphs. Privacy and security.

Machine Learning 154

Machine Learning Artificial Inteligence Company Case Study 154

d2iq

FEBRUARY 19, 2021

Except in many enterprises. There is a lot more to machine learning in the enterprise than just the model, which is what many people think of when they hear artificial intelligence. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model. AI is everywhere.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. After all, machine learning with Python requires the use of algorithms that allow computer programs to constantly learn, but building that infrastructure is several levels higher in complexity. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

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. When it comes to Data and AI, the industry is increasingly committed to a hybrid cloud approach.

Telecommunications 60

Telecommunications Data Architecture Big Data 60

Cloudera

DECEMBER 10, 2020

CDW includes two highly scalable and performant Apache open-source engines – Impala and Hive LLAP – that have proven themselves in hundreds of enterprises for business-critical data warehousing. 150 for storage use = $15 / TB / month x 10 TB. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

AWS Machine Learning - AI

JANUARY 26, 2024

Refresh your understanding of the AWS Shared Responsibility Model as it applies to infrastructure, services, and data when you build LLM solutions. AWS is architected to be the most secure global cloud infrastructure on which to build, migrate, and manage applications and workloads.

Artificial Inteligence 116

Artificial Inteligence Generative AI Security Applications 116

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Cloud-based spending will reach 60% of all IT infrastructure and 60-70% of all software, services, and technology spending by 2020. They require highly modernized digital application and service environments running on true cloud platforms like Hitachi Enterprise Cloud.

Cloud 78

Cloud Google Cloud Azure AWS 78

Hu's Place - HitachiVantara

JANUARY 3, 2019

Data curation will be a focus to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need to power the organization.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Existek

JANUARY 11, 2022

The cloud computing market covers many areas like business processes, infrastructure, platform, security, management, analytics supported by cloud providers. They quickly met the full range of enterprise needs starting from SaaS to PaaS and IaaS. Data and analytics. Hybrid cloud and infrastructure. Internet of Things.

Azure 52

Azure AWS Cloud How To 52

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 82

Data Analytics Cloud Technical Review 82

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

ParkMyCloud

JULY 30, 2019

The Databricks platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers. To do this, Databricks offers a range of tools for building, managing and monitoring data pipelines.

Azure 40

Azure AWS Analytics Machine Learning 40

O'Reilly Media - Ideas

MARCH 31, 2020

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

Product Management 135

Product Management Artificial Inteligence Machine Learning Software Review 135

Altexsoft

JUNE 25, 2019

However, they often forget about the fundamental work – data literacy, collection, and infrastructure – that must be done prior to building intelligent data products. 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

TIBCO - Connected Intelligence

DECEMBER 11, 2020

We have been working with TIBCO for over 18 years, initially focused on enterprise application integration, service-oriented architecture, events, messaging, and other capabilities that optimize our client’s operations. I would say, in general, that Nordics organizations have moved quickly to adopt data-driven business transformation.

Business Transformation 52

Business Transformation Data Analytics Hotels 52

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

Altexsoft

AUGUST 19, 2022

Following the current market trend, Power BI puts an emphasis on self-service which means shifting access to information and insights from professional data analysts to end consumers, average employees and decision-makers alike. You get 10GB of cloud storage and can upload 1GB of data at a time. per user/month).

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

TechCrunch

NOVEMBER 4, 2022

Ashish Kakran , principal at Thomvest Ventures , is a product manager/engineer turned investor who enjoys supporting founders with a balance of technical know-how, customer insights, empathy with challenges and market knowledge. Here’s where MLOps is accelerating enterprise AI adoption. More posts by this contributor.

Data 218

Data Storage Analytics Data Engineering 218

Daniel Bryant

SEPTEMBER 13, 2019

In relation to this, the HashiCorp Cloud Operating Model was once again on full display, and I believe that this is a very useful tool to help understand and plan the evolution of applications and infrastructure. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

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? You need an efficient data pipeline. Destination.

Data 76

Data Storage Analytics Data Center 76

CIO

JUNE 14, 2023

Start with storage. Before you can even think about analyzing exabytes worth of data, ensure you have the infrastructure to store more than 1000 petabytes! Going from 250 PB to even a single exabyte means multiplying storage capabilities four times. So, what does it require for organizations to go from PB to EB scale?

Data 152

Data Scalability Storage Big Data 152

Cloudera

NOVEMBER 28, 2023

Cloudera Private Cloud Data Services is a comprehensive platform that empowers organizations to deliver trusted enterprise data at scale in order to deliver fast, actionable insights and trusted AI. This means you can expect simpler data management and drastically improved productivity for your business users.

Data 80

Data Scalability Analytics Quality Assurance 80

CIO

JULY 15, 2022

The forecasting systems DTN had acquired were developed by different companies, on different technology stacks, with different storage, alerting systems, and visualization layers. Working with his new colleagues, he quickly identified rebuilding those five systems around a single forecast engine as a top priority. The merger playbook.

Systems Review 266

Systems Review Fractional CTO System Development Team Review 266

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

Kentik

APRIL 11, 2016

It has since gone on to become a key technology for running many web-scale services and products, and has also landed in traditional enterprise and government IT organizations for solving big data problems in finance, demographics, intelligence, and more. For more on how we make it work, see Inside the Kentik Data Engine.).

Big Data 40

Big Data Analytics Network Architecture 40

Altexsoft

NOVEMBER 23, 2020

Otherwise, let’s start from the most basic question: What is data migration? What is data migration? In general terms, data migration is the transfer of the existing historical data to new storage, system, or file format. Usually, data migration comes as a part of a larger project such as. Storage migration.

Data 104

Data Transportation Backup Storage 104

Datavail

JUNE 15, 2023

“It unifies the business model across all the different types of analytics workloads, whether they’re SQL, machine learning – whatever job you want, you can use the same compute infrastructure. Data Factory : A data integration tool with 150+ connectors to cloud and on-premises data sources.

Azure 52

Azure Analytics Storage Generative AI 52