CIO

NOVEMBER 9, 2022

But experienced data analysts and data scientists can be expensive and difficult to find and retain. Self-service analytics typically involves tools that are easy to use and have basic data analytics capabilities. Understand the importance of timeliness and quality of the data on which important decisions are being made.

Analytics 342

Analytics Metrics Government Business Intelligence 342

Altexsoft

FEBRUARY 11, 2023

This specialist works closely with people on both business and IT sides of a company to understand the current needs of the stakeholders and help them unlock the full potential of data. To get a better understanding of a data architect’s role, let’s clear up what data architecture is. Feel free to enjoy it.

Data 87

Data Data Engineering Big Data Architecture 87

CIO

FEBRUARY 13, 2023

Fifty-two percent of organizations plan to increase or maintain their IT spending this year, according to Enterprise Strategy Group. This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%).

Budget 363

Budget Artificial Inteligence Technical Review VR 363

TechCrunch

FEBRUARY 2, 2022

But, as RudderStack CEO Soumyadeb Mitra argued when I talked to him ahead of today’s announcement, most of the existing customer data pipeline solutions were built for selling to marketing teams, using architectures that make it harder to build the advanced applications that businesses are now looking for.

Data 204

Data Machine Learning Artificial Inteligence Architecture 204

CIO

MARCH 27, 2024

“Le azioni successive per il miglioramento della data quality possono essere sia di processo che applicative e includono la definizione di un modello organizzativo intorno alla data governance , assegnando ruoli e compiti chiari alle varie figure coinvolte (data scientist, data engineering, data owner, data steward, eccetera)”.

ChatGPT 130

ChatGPT Government Artificial Inteligence Business Intelligence 130

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data 94

Data Analytics Open Source Architecture 94

Cloudera

OCTOBER 4, 2023

Ability to handle complex analytic queries — especially when we’re using real-time analytics to augment existing business dashboards and reports with large, complex, long-running business intelligence queries typical for those use cases, and not having the real-time dimension slow these down in any way. Kudu has this covered.

Analytics 90

Analytics Machine Learning Artificial Inteligence Telecommunications 90

O'Reilly Media - Data

APRIL 3, 2018

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

Machine Learning 155

Machine Learning Artificial Inteligence Company Case Study 155

Lacework

NOVEMBER 1, 2023

This article addresses privacy in the context of hosting data and considers how privacy by design can be incorporated into the data architecture. In the first article in this series, the topic of privacy by design is covered in some detail, particularly regarding the design of products for enterprise security and data hosting.

System Design 52

System Design Systems Review System Data 52

TIBCO - Connected Intelligence

SEPTEMBER 22, 2020

For today’s most mature enterprise cloud architectures, ultra-quick querying capabilities on petabytes of data is what the modern pace of business demands. And for enterprises running AWS, Amazon Redshift is most certainly a part of the data warehousing picture given its size, flexibility, and scale.

AWS 48

AWS Analytics Cloud Analysis 48

CIO

APRIL 4, 2023

In order to move AI forward, we need to first build and fortify the foundational layer: data architecture. This architecture is important because, to reap the full benefits of AI, it must be built to scale across an enterprise versus individual AI applications. Constructing the right data architecture cannot be bypassed.

Architecture 130

Architecture Data Analysis Construction 130

O'Reilly Media - Ideas

FEBRUARY 11, 2019

The resource examples I’ll cite will be drawn from the upcoming Strata Data conference in San Francisco , where leading companies and speakers will share their learnings on the topics covered in this post. AI and machine learning in the enterprise. AI and machine learning in the enterprise. AI and Data technologies in the cloud.

Big Data 87

Big Data Technology Tools Cloud 87

Hu's Place - HitachiVantara

JUNE 5, 2019

“ It unleashes data’s ultimate potential by automating the processes that enable the right data to get to the right place at the right time – all while ensuring it remains secure and accessible only to authorized employees. Business owners reported immediate benefits from this implementation. Fatima Hamad, Sr.

Technical Review 40

Technical Review Analytics Data Engineering Scalability 40

Altexsoft

FEBRUARY 8, 2021

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Business Problem & Background.

Data 85

Data Analytics Cloud Technical Review 85

Altexsoft

APRIL 13, 2023

Supply chain control tower architecture: main components, integrations, and data sources As we mentioned, an SCCT is typically comprised of multiple components that handle various aspects of supply chain management. Let’s look closer at what’s there under the hood and list the main components, integrations, and data sources.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Cloudera

AUGUST 30, 2022

Modern data architectures. To eliminate or integrate these silos, the public sector needs to adopt robust data management solutions that support modern data architectures (MDAs). Towards Data Science ). Deploying modern data architectures. Forrester ).

Architecture 80

Architecture Data Artificial Inteligence Artificial Intelligence 80

Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. Watch this video to get an overview of CDW. .

Data 98

Data Technical Review Storage Systems Review 98

Apiumhub

AUGUST 12, 2021

His current technical expertise focuses on integration platform implementations, Azure DevOps, and Cloud Solution Architectures. Evgenii Vinogradov – Director, Analytical Solutions Department @YooMoneyon Evgenii is the Head of Data Engineering and Data Science team at YooMoney, the leading payment service provider on the CIS Market.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

JULY 19, 2022

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. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two. What data mesh IS.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

OCTOBER 24, 2019

In this article, we will discuss what an enterprise data warehouse is, its types and functions, and how it’s used in data processing. We will define how enterprise warehouses are different from the usual ones, what types of data warehouses exist, and how they work. What is an Enterprise Data Warehouse?

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

SEPTEMBER 9, 2022

Today, such modern data management frameworks as DataOps strongly rely on effective metadata capture and management to bring order into the chaotic data flows. Plus, a data fabric architecture design approach is also based on metadata as one of the main building blocks. It provides data consumers with.

Tools 59

Tools Technical Review Software Review Systems Review 59

CIO

AUGUST 18, 2022

Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. The result is an emerging paradigm shift in how enterprises surface insights, one that sees them leaning on a new category of technology architected to help organizations maximize the value of their data.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Altexsoft

JANUARY 22, 2020

Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview. A complete guide to business intelligence and analytics. The role of business intelligence developer.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Data lake architecture example.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). But the current data lakehouse architectural pattern is not enough.

Data 89

Data Analytics Machine Learning Artificial Inteligence 89

Strategy Driven

AUGUST 27, 2015

But getting access to these Big Data technologies means either buying it and building it into your organization’s current IT enterprise structure, or accessing the tools and data storage through Cloud-based offerings (or a combination of both). Copyright 2007-2015 by StrategyDriven Enterprises, LLC. All rights reserved.

Big Data 33

Big Data Data Storage Journal 33

Datavail

JUNE 8, 2023

Data Summit 2023 was filled with thought-provoking sessions and presentations that explored the ever-evolving world of data. From the technical possibilities and challenges of new and emerging technologies to using Big Data for business intelligence, analytics, and other business strategies, this event had something for everyone.

Database Administration 52

Database Administration Data Artificial Inteligence Analytics 52

O'Reilly Media - Ideas

FEBRUARY 11, 2020

Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.

Weak Development Team 129

Weak Development Team Data Technical Review Survey 129

Altexsoft

JANUARY 14, 2021

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. A data warehouse is often abbreviated as DW or DWH.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

APRIL 16, 2021

As the topic is closely related to business intelligence (BI) and data warehousing (DW), we suggest you to get familiar with general terms first: A guide to business intelligence. An overview of data warehouse types. What is data pipeline. Online Analytical Processing Architecture.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

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. Data warehousing in a nutshell. CRMs, OLAP/OLTP databases, enterprise applications, etc.)

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

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 lakes are mostly used by data scientists for machine learning projects.

Data 76

Data Storage Analytics Data Center 76

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

AUGUST 21, 2019

Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data. Using specific tools and practices, businesses implement these methods to generate valuable insights. Data engineer. Data scientists.

Development 44

Development Software Engineering Data Engineering Architecture 44

Altexsoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Key differences between data marts, data warehouses, and data lakes.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

MARCH 18, 2021

The primary difference resides in WHERE and WHEN data transformation and loading happens. With ETL, data is transformed in a temporary staging area before it gets to a target repository (e.g As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. ELT comes to the rescue.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109