CIO

NOVEMBER 27, 2024

Good data governance has always involved dealing with errors and inconsistencies in datasets, as well as indexing and classifying that structured data by removing duplicates, correcting typos, standardizing and validating the format and type of data, and augmenting incomplete information or detecting unusual and impossible variations in the data.

Data 211

Data Enterprise Weak Development Team Software Review 211

CIO

MARCH 21, 2022

Finance: Data on accounts, credit and debit transactions, and similar financial data are vital to a functioning business. But for data scientists in the finance industry, security and compliance, including fraud detection, are also major concerns.

Analytics 205

Analytics Data Analysis Technical Review 205

InfoWorld

FEBRUARY 9, 2024

It is built around a data lake called OneLake, and brings together new and existing components from Microsoft Power BI, Azure Synapse, and Azure Data Factory into a single integrated environment. In many ways, Fabric is Microsoft’s answer to Google Cloud Dataplex. As of this writing, Fabric is in preview.

Big Data 78

Big Data Data Azure Google Cloud 78

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 125

Weak Development Team Data Technical Review Survey 125

Cloudera

FEBRUARY 8, 2021

Security & Governance – an integrated set of security, management and governance technologies across the entire data lifecycle. 1 The enterprise data lifecycle. Data Enrichment Challenge. Building a Pipeline Using Cloudera Data Engineering. 2 ECC data enrichment pipeline. Conclusion.

Data 118

Data Technical Review Data Engineering Analysis 118

Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Let’s take a common use-case for Business Intelligence reporting. Figure 2: Example BI reporting data pipeline.

Data 115

Data Open Source Analytics Government 115

O'Reilly Media - Ideas

JANUARY 29, 2019

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption? Data scientists and data engineers are in demand.

Sustainability 85

Sustainability Company Machine Learning Artificial Inteligence 85

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 ). Why integrate Apache Iceberg with Cloudera Data Platform?

Data 88

Data Analytics Machine Learning Artificial Inteligence 88

CIO

JUNE 6, 2023

On the other hand, a business that needs efficiency to scale may be better served by a central team that provides functions like data governance, platform engineering, architecture, and data engineering to all areas of the business. Heavily regulated industries tend to centralize.

Data 78

Data Analytics Government Culture 78

Cloudera

OCTOBER 23, 2024

That’s why Cloudera added support for the REST catalog : to make open metadata a priority for our customers and to ensure that data teams can truly leverage the best tool for each workload– whether it’s ingestion, reporting, data engineering, or building, training, and deploying AI models.

Data 84

Data Analytics Systems Review Architecture 84

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 78

ChatGPT Government Artificial Inteligence Business Intelligence 78

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 79

Architecture Data Artificial Inteligence Artificial Intelligence 79

Altexsoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data 87

Data Data Engineering Big Data Architecture 87

Cloudera

AUGUST 27, 2024

The first is to leverage open formats, including Apache Parquet at the file level and Apache Iceberg at the table level, to ensure that data is both transferable between clouds and interoperable with a wide range of tools for different use cases. It’s portable, meaning that if infrastructure requirements change, it’s easy to move.

Report 83

Report Cloud Architecture Survey 83

O'Reilly Media - Ideas

FEBRUARY 11, 2019

There are many articles that point to the explosion of data, but in order for that data that be useful for analytics and ML, it has to be collected, transported, cleaned, stored, and combined with other data sources. Data Platforms. Data Integration and Data Pipelines. Model lifecycle management.

Big Data 85

Big Data Technology Tools Cloud 85

Datavail

MARCH 10, 2022

There are many reasons for this failure, but poor (or a complete lack of) data governance strategies is most often to blame. This article discusses the importance of solid data governance implementation plans and why, despite its obvious benefits, many organizations find data governance implementation to be challenging.

Government 40

Government Strategy Weak Development Team Company 40

Altexsoft

OCTOBER 2, 2020

Data integration and interoperability: consolidating data into a single view. Specialist responsible for the area: data architect, data engineer, ETL developer. Data analytics and business intelligence: drawing insights from data. Data Governance includes Master Data Management.

Strategy 64

Strategy Database Administration Data Technical Review 64

ParkMyCloud

MAY 29, 2019

In addition, they also have a strong knowledge of cloud services such as AWS, Google or Azure, with experience on ITSM, I&O, governance, automation, and vendor management. Business Intelligence Analyst. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

KitelyTech

FEBRUARY 3, 2021

Big data and data science are important parts of a business opportunity. Developing business intelligence gives them a distinct advantage in any industry. How companies handle big data and data science is changing so they are beginning to rely on the services of specialized companies.

Big Data 52

Big Data Data Development Business Intelligence 52

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

Invid Group

AUGUST 28, 2024

External metrics can be implemented using Business Intelligence (BI) tools and shared with the clients to measure performance. Addressing these challenges requires implementing best-practices approaches to data management, model development, and deployment.

Technical Review 52

Technical Review Artificial Inteligence Performance Azure 52

InnovationM

MAY 14, 2023

These tools streamline data flow, enable real-time data ingestion, and ensure data quality and metadata management. Data Governance and Metadata Management: Effective data governance is essential for managing data lakes successfully.

Data 52

Data Storage Construction Business Intelligence 52

Datavail

JUNE 15, 2023

The Microsoft Fabric platform includes: Power BI : The Microsoft business intelligence tool that’s a mainstay for many organizations, infused with a generative AI copilot for business analysts and business users. Data Factory : A data integration tool with 150+ connectors to cloud and on-premises data sources.

Azure 52

Azure Analytics Storage Generative AI 52

Cloudera

DECEMBER 7, 2017

That data may be stored multiple times in different pools, each multiplying storage resource costs. That data may be used in ways that don’t comply with appropriate security and governance policies. That data may be hard to discover for other users and other applications.

Data 40

Data Artificial Inteligence Machine Learning Analytics 40

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

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. You have a centralized data catalog and schema registry.

Data 98

Data Technical Review Storage Systems Review 98

Apiumhub

DECEMBER 9, 2024

Integrated Data Lake Synapse Analytics is closely integrated with Azure Data Lake Storage (ADLS), which provides a scalable storage layer for raw and structured data, enabling both batch and interactive analytics. When Should You Use Azure Synapse Analytics? finance, healthcare).

Azure 89

Azure Analytics Storage Machine Learning 89

Cloudera

NOVEMBER 2, 2020

On the technical side, it is cheaper and easier than ever to instrument everything and send that data in real-time through a messaging system. On the business side, companies and governments are digitizing and automating as many of their operations as possible so decision making and asset management can be more effective.

Data 97

Data Analytics Storage Big Data 97

Altexsoft

JULY 19, 2022

The HR team will manage all of this data and generate datasets to be consumed by other users in the company like the marketing team. They also own the governance of their domain. It’s easiest to understand the concept of a data mesh by looking at the core principles behind it which we’re going to uncover more extensively later on.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

OCTOBER 3, 2019

Deloitte reports that The Ministry of Energy of the Government of Mexico uses a predictive workforce planning and analytics model to learn about the current shortage of skilled workers in the oil and gas industry and predict one over a 10-year horizon. Data engineer builds interfaces and infrastructure to enable access to data.

Analytics 16

Analytics Company Off-The-Shelf How To 16

Hu's Place - HitachiVantara

JUNE 5, 2019

” In the last half of last year we applied DataOps to consolidate our enterprise reporting systems into one data lake. 32 data domain owners from governance, pricing, services, procurement, partners, sales, marketing, supply planning and IT were involved. We know it works because we use DataOps in our own operations.”

Technical Review 40

Technical Review Analytics Data Engineering Scalability 40

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Storage Machine Learning 52

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.

Data 82

Data Analytics Cloud Technical Review 82

Lacework

NOVEMBER 1, 2023

In many cases we see that customers prefer to have their data stored and managed locally in their home region, both for reasons of regulatory compliance and also business preference. This prevents role crossover and enforces “need to know” in terms of data access. A mart is a group of aggregated tables (e.g.,

System Design 52

System Design Systems Review System Data 52

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Many of us are all too familiar with the traditional way enterprises operate when it comes to on-premises data warehousing and data marts: the enterprise data warehouse (EDW) is often the center of the universe. Clouds (source: Pexels ).

Cloud 43

Cloud Data Storage Analytics 43

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.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a part of the data governance process which, in turn, is an element of the overall data management strategy. Today, such modern data management frameworks as DataOps strongly rely on effective metadata capture and management to bring order into the chaotic data flows. data cataloging).

Tools 59

Tools Technical Review Software Review Systems Review 59