Cloudera

DECEMBER 20, 2022

Going into more technological aspects, the data platforms that must support this decision-making must be able to operate in a hybrid environment in which there is integration with the applications that reside in the company’s own data centers, as well as the possibility of working in public cloud environments. .

Energy 83

Energy Analytics Data Artificial Inteligence 83

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Private clouds are not simply existing data centers running virtualized, legacy workloads. Public cloud also introduces new challenges in governance, financial management and integration. 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

Altexsoft

DECEMBER 9, 2020

Sometimes, a data or business analyst is employed to interpret available data, or a part-time data engineer is involved to manage the data architecture and customize the purchased software. At this stage, data is siloed, not accessible for most employees, and decisions are mostly not data-driven.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

DECEMBER 4, 2020

Three types of data migration tools. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. This makes sense when you move a relatively small amount of data and deal with simple requirements. Phases of the data migration process. Data sources and destinations.

Software Review 69

Software Review Software Data Technical Review 69

O'Reilly Media - Ideas

JUNE 4, 2019

Understanding Data Science Algorithms in R: Regression , July 12. Cleaning Data at Scale , July 15. Scalable Data Science with Apache Hadoop and Spark , July 16. Effective Data Center Design Techniques: Data Center Topologies and Control Planes , July 19. First Steps in Data Analysis , July 22.

Course 66

Course Training Artificial Inteligence Software Review 66

O'Reilly Media - Ideas

JUNE 5, 2019

Understanding Data Science Algorithms in R: Regression , July 12. Cleaning Data at Scale , July 15. Scalable Data Science with Apache Hadoop and Spark , July 16. Effective Data Center Design Techniques: Data Center Topologies and Control Planes , July 19. First Steps in Data Analysis , July 22.

Course 49

Course Training Artificial Inteligence Software Review 49

O'Reilly Media - Ideas

MARCH 1, 2023

Companies can no longer protect all of their IT systems behind a firewall; many of the servers are running in a data center somewhere, and IT staff has no idea where they are or even if they exist as physical entities. Data engineering was the dominant topic by far, growing 35% year over year. That’s changing, but slowly.

Trends 135

Trends Technical Review Technology Software Review 135

BPM

JANUARY 18, 2019

They’ll have to invest in the last mile of their solution - business rules, organizational change, decision governance and infrastructure to let the business continually refine and improve the way these technologies are being applied. AI-enabled data engines will provide insight about what processes can be redesigned and/or automated.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Existek

JANUARY 11, 2022

Governments . Governments. Data and analytics. Security and governance. AWS delivered a significant contribution to cloud computing through the power of data analytics, AI, and other innovative technologies. Development Operations Engineer $122 000. Senior Sofware Engineer $130 000.

Azure 52

Azure AWS Cloud How To 52

Cloudera

SEPTEMBER 14, 2020

Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Fundamental principles to be successful with Cloud data management.

Cloud 111

Cloud Data Compliance Government 111

Altexsoft

APRIL 26, 2022

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. This demand gave birth to cloud data warehouses that offer flexibility, scalability, and high performance.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Cloudera

AUGUST 30, 2022

Data is the fuel that drives government, enables transparency, and powers citizen services. Data quality issues deter trust and hinder accurate analytics. Citizens who have negative experiences with government services are less likely to use those services in the future. Modern data architectures.

Architecture 81

Architecture Data Artificial Inteligence Artificial Intelligence 81

Altexsoft

OCTOBER 23, 2019

The government, fleet managers, automakers, and press can pass free of charge. Data Center Solution Annual Summit 2020. The annual event tracks activity in the development of the Data Center Industry. Discussions are usually held by experts from the IT and Real Estate Communities and CBRE’s Data Center.

Security 13

Security Artificial Inteligence Conference Data Center 13

CIO

JUNE 14, 2023

Before you can even think about analyzing exabytes worth of data, ensure you have the infrastructure to store more than 1000 petabytes! This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data. Merely adding more data nodes is insufficient.

Data 157

Data Scalability Storage Big Data 157

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.

Cloud 82

Cloud Data Government Innovation 82

DataRobot

SEPTEMBER 14, 2021

AI Cloud brings together any type of data, from any source, giving you a unique, global view of insights that drive your business. All of this is part of a unified, integrated platform spanning data engineering, machine learning, decision intelligence, and continuous AI – the entire AI lifecycle. Augmented Intelligence.

Cloud 52

Cloud Data Center Artificial Inteligence Machine Learning 52

Cloudera

APRIL 21, 2022

MHS Genesis has to tackle an almost impossible job in moving and processing petabytes of data, securely and accurately. The DoD is the federal government’s largest agency and one of the most complex organizations in the world. With more than 5,000 locations worldwide, 2.3 million retirees, 1.3

Security 89

Security Data Insurance System 89

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads.

Strategy 99

Strategy Data Technical Review Weak Development Team 99

Cloudera

MAY 22, 2018

End users, data stewards, governance groups, and security groups alike can easily get overwhelmed with multiple access points, inconsistent user interfaces, and overall complexity. Meanwhile, IT hasn’t abandoned the enterprise requirements for security and governance of data. With Cloudera Enterprise 6.0,

Enterprise 40

Enterprise Artificial Inteligence Machine Learning Analytics 40

Cloudera

OCTOBER 7, 2021

The concept of the data mesh architecture is not entirely new; Its conceptual origins are rooted in the microservices architecture, its design principles (i.e., need to integrate multiple “point solutions” used in a data ecosystem) and organization reasons (e.g., difficulty to achieve cross-organizational governance model).

Architecture 126

Architecture Data Security Technical Review 126

Cloudera

FEBRUARY 14, 2022

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. It’s no longer driven by data volumes, but containerization, separation of storage and compute, and democratization of analytics.

Data Engineering 116

Data Engineering Engineering Data Storage 116

Cloudera

JANUARY 11, 2021

LoB users who run these projects often lack expertise in security and governance requirements. How self-service data warehousing frees IT resources. The CDP Shared Data Experience (SDX) service underlying Cloudera Data Warehouse helps Central IT provide security and governance. Centralized security and governance.

Data 64

Data Pharmaceuticals Open Source Artificial Inteligence 64

Cloudera

OCTOBER 13, 2021

Turning the data center into a private cloud would bring all the agility and flexibility of public cloud to the control of an on-premises infrastructure. Move to more Data Services. Next stop: hybrid data cloud. What use cases and value will you unlock by turning your data center into a true private cloud?

Data Center 100

Data Center Cloud Data How To 100

Cloudera

MARCH 22, 2022

That technical debt includes silo-ed data warehousing appliances, homegrown tools for data processing, or point solutions used for dedicated workloads such as machine learning. To address that need, the data and analytics platform needs to provide pre-integrated, interoperable processing capabilities across the data lifecycle (e.g.,

Technical Review 82

Technical Review Strategy Data Analytics 82

DataRobot

FEBRUARY 2, 2022

DataRobot enables entire teams — from data scientists to data engineers and from IT to business users — to collaborate on a unified platform. By partnering with major cloud companies like AWS, Google Cloud, Azure, and VMware, DataRobot helps customers harness the power of their data wherever it lives.

Cloud 52

Cloud Innovation Google Cloud Azure 52