Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 72

Data Warehouse Cost-Benefit Metadata Management 72

erwin

JULY 2, 2020

Users discuss how they are putting erwin’s data modeling, enterprise architecture, business process modeling, and data intelligences solutions to work. IT Central Station members using erwin solutions are realizing the benefits of enterprise modeling and data intelligence. This is live and dynamic.”.

Enterprise 122

Enterprise Modeling Metadata Data Governance 122

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 88

Machine Learning Modeling Metadata Recreation/Entertainment 88

Data Virtualization

FEBRUARY 24, 2022

We say that data storage, like matter, adheres to the Law of Increments, which states that, according. The post Introducing the Liquid Data Model appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Modeling 59

Modeling Data Integration Software Management 59

IBM Big Data Hub

APRIL 25, 2023

To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Now, let’s chat about why data warehouse optimization is a key value of a data lakehouse strategy. To effectively use raw data, it often needs to be curated within a data warehouse.

Optimization 56

Optimization Strategy Data Warehouse Cost-Benefit 56

O'Reilly on Data

AUGUST 18, 2020

That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows. The Two Cultures of Data Tooling. Lessons Learned from Data Warehouse and Data Engineering Platforms.

Data Science 355

Data Science Machine Learning Data Warehouse Deep Learning 355

CIO Business Intelligence

APRIL 10, 2019

BI analysts, with an average salary of $66,791 per year according to PayScale, provide application analysis and data modeling design for centralized data warehouses and extract data from databases and data warehouses for reporting, among other tasks. To read this article in full, please click here

Business Intelligence 111

Business Intelligence Data Warehouse Data-driven Software 111

Sisense

JULY 8, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. In recent years we’ve seen data become vastly more available to businesses. This has allowed companies to become more and more data driven in all areas of their business.

Data Warehouse 90

Data Warehouse Data Lake Data-driven Cost-Benefit 90

Analytics Vidhya

SEPTEMBER 1, 2021

This article was published as a part of the Data Science Blogathon A data scientist’s ability to extract value from data is closely related to how well-developed a company’s data storage and processing infrastructure is.

Data Science 237

Data Science Publishing Modeling Analytics 237

Sisense

APRIL 17, 2020

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. Why use a materialized view?

Data Warehouse 90

Data Warehouse Visualization Statistics Machine Learning 90

Cloudera

OCTOBER 11, 2022

A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. The article goes on to share insights from experts at Gartner, PwC, John Deere, and Cloudera that shine a light on the critical role that data plays in scaling AI. . Data science needs analytics.

Snapshot 98

Snapshot Data Science Digital Transformation Metadata 98

TDAN

MAY 16, 2023

Data quality management (DQM) has advanced considerably over the years. The full extent of the problem was first recognized during the data warehouse movement in the 1980s.

Data Quality 52

Data Quality Data Warehouse Management Modeling 52

CIO Business Intelligence

APRIL 12, 2023

Most current data architectures were designed for batch processing with analytics and machine learning models running on data warehouses and data lakes. What level of alerting, observability, and profiling can be counted on to ensure trust in the data by the business?

Machine Learning 145

Machine Learning Cost-Benefit Data-driven Strategy 145

TDAN

APRIL 19, 2022

Introduction We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively.

Data Warehouse 72

Data Warehouse Enterprise IT Metadata 72

Octopai

MARCH 22, 2023

Data migration can be a daunting task, especially when dealing with large volumes of data. Snowflake is one of the leading cloud-based data warehouse that provides scalability, flexibility, and ease of use. Snowflake data warehouse platform has been designed to leverage the power of modern-day cloud computing technology.

Cost-Benefit 78

Cost-Benefit Data Warehouse Unstructured Data Optimization 78

Cloudera

SEPTEMBER 29, 2022

They are being bombarded with literature about seemingly independent new trends like data mesh and data fabric while dealing with the reality of having to work with hybrid architectures. Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem.

Data Architecture 78

Data Architecture Metadata Data Warehouse Sales 78

Sisense

JUNE 8, 2020

Datasets are on the rise and most of that data is on the cloud. The recent rise of cloud data warehouses like Snowflake means businesses can better leverage all their data using Sisense seamlessly with products like the Snowflake Cloud Data Platform to strengthen their businesses.

Data Warehouse 89

Data Warehouse Data-driven Dashboards Sales 89

CIO Business Intelligence

FEBRUARY 14, 2023

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions.

Data Strategy 116

Data Strategy Strategy Machine Learning Data-driven 116

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Cloudera

FEBRUARY 16, 2021

You can use COD with: Cloudera DataFlow to ingest and aggregate data from various sources. Cloudera Data Engineering to ingest bulk data and data from mainframes. Cloudera Data Warehouse to perform ETL operations. Cloudera Machine learning to train and serve machine learning and AI models.

Machine Learning 86

Machine Learning Data Lake Enterprise Data Warehouse 86

Jet Global

MAY 1, 2020

The mechanical solution is to build a data warehouse. This will be the place where everybody has come together and agreed upon the data that you are going to use to manage your business, and how that data is going to be combined with which methods you’re going to use to calculate things like gross profit and net income.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

FineReport

JULY 28, 2021

If you are curious about the difference and similarities between them, this article will unveil the mystery of business intelligence vs. data science vs. data analytics. Definition: BI vs Data Science vs Data Analytics. On the other hand, data science is typically more extensive and complex.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

Smart Data Collective

NOVEMBER 18, 2020

Data Mining Techniques and Data Visualization. Data Mining is an important research process. After you’ve learned everything you need to analyze data and try your hand at open competitions, start looking for a job. Data Science vs Data Mining: Concluding Thoughts. Practical experience.

Data mining 143

Data mining Data Science Informatics Statistics 143

Peter James Thomas

AUGUST 18, 2018

No this article has not escaped from my Maths & Science section , it is actually about data matters. The image at the start of this article is of an Ichthyosaur (top) and Dolphin. That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. An example is probably simpler to understand.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Alation

JULY 13, 2021

Yet increasing complexity of data makes the old “lift-and-shift” model not just unrealistic, but risky. Businesses with complex data environments need a migration method that takes that complexity into account. The Data Race to the Cloud. This recent cloud migration applies to all who use data. Fern Halper, Ph.D.

Strategy 52

Strategy Data mining Structured Data Data Governance 52

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Data Virtualization

APRIL 11, 2024

Traditional data management approaches just won’t cut it, as they rely on physically replicating data from multiple systems into. The post Modern Data Solutions for the Supply Chain appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Integration 52

Data Integration Management IT Data Warehouse 52

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The many data warehouse systems designed in the last 30 years present significant difficulties in that respect.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Data Virtualization

MARCH 7, 2024

One thing is clear; if data-centric organizations want to succeed in. The post Data Management Predictions for 2024: Five Trends appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 59

Management Data Integration Strategy Data Lake 59

Smarten

DECEMBER 3, 2023

In this article, we provide an overview of the factors and considerations to include in your project plan. Perhaps you want to shift from a data warehouse to cloud storage. .’ If you are planning to implement an augmented analytics solution, you must have a comprehensive plan.

Analytics 40

Analytics Data Warehouse Metrics Interactive 40

Data Virtualization

JANUARY 25, 2024

One thing is clear; if data-centric organizations want to succeed in 2024, The post Data Management Predictions for 2024: Five Trends appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 59

Management Data Integration Strategy Data Lake 59

datapine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

datapine

SEPTEMBER 29, 2022

Engineered to be the “Swiss Army Knife” of data development, these processes prepare your organization to face the challenges of digital age data, wherever and whenever they appear. Data quality refers to the assessment of the information you have, relative to its purpose and its ability to serve that purpose.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Jet Global

NOVEMBER 2, 2020

Interestingly, you can address many of them very effectively with a data warehouse. The Data Warehouse Solution. Now consider an alternative that does not occur to most ERP system managers: A data warehouse with data from your old ERP system that provides all the information you need for historical reference.

Data Warehouse 83

Data Warehouse Cost-Benefit Reporting Recreation/Entertainment 83

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

FineReport

JULY 22, 2021

Business Intelligence(BI) is defined as the concept of using modern data warehouse technology, online analysis and processing technology, data mining and data display technology for data analysis to achieve business value. The examples in this article are real applying cases using the application FineReport.

Business Intelligence 52

Business Intelligence Sales Marketing Forecasting 52

Cloudera

APRIL 9, 2021

Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them. ETL jobs and staging of data often often require large amounts of resources.

Data Warehouse 60

Data Warehouse Cost-Benefit Statistics Data-driven 60