Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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Dataversity

JANUARY 25, 2023

The emergence of advanced data storage technologies, such as cloud computing, data hubs, and data lakes, makes us question the role of traditional data warehouses in modern data architecture. Data warehouses were first introduced in the […] The post Are Data Warehouses Still Relevant?

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Dataversity

OCTOBER 28, 2022

In the past, designing and developing a robust data warehouse that satisfied the need for timely and effective business intelligence (BI) was an overwhelmingly difficult task, as it required significant time, capital, and risk. The post Developing Agile Data Warehouse Architecture Using Automation appeared first on DATAVERSITY.

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Simplilearn

JULY 18, 2024

In the era of big data, businesses and organizations continuously seek innovative ways to handle and leverage their vast amounts of data efficiently. This quest for data optimization has led to the emergence and evolution of data lakes and data warehouses, two pivotal structures in the data management landscape.

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Smart Data Collective

OCTOBER 27, 2021

Interactive analytics applications make it easy to get and build reports from large unstructured data sets fast and at scale. In this article, we’re going to look at the top 5. Firebolt makes engineering a sub-second analytics experience possible by delivering production-grade data applications & analytics. Google BigQuery.

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ElegantJ BI

APRIL 28, 2021

Without a clear understanding of the various categories and iterations of data management options, the business may make the wrong choice or become so mired in the review process that it will give up its quest. This article is the first of two on the topic of Data Management.

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ElegantJ BI

APRIL 28, 2021

Without a clear understanding of the various categories and iterations of data management options, the business may make the wrong choice or become so mired in the review process that it will give up its quest. This article is the first of two on the topic of Data Management. Data Warehouse.

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Simplilearn

MAY 9, 2021

A data warehouse allows us to manage the collected data, which can, in turn, helps in providing significant business insights. It is an essential Business Intelligence (BI) field, and this makes Data Warehouse Analysis one of the most sought-after career options today.

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Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Each business often uses one or more data […].

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Dataversity

FEBRUARY 1, 2021

Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […]. The post Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 3 appeared first on DATAVERSITY.

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Data Warehouse Business Intelligence Business Intelligence Data Quality 147

Dataversity

JANUARY 11, 2021

Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their user base for […]. The post Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 2 appeared first on DATAVERSITY.

Data Warehouse 147

Data Warehouse Business Intelligence Business Intelligence Data Quality 147

Dataversity

MARCH 10, 2023

Typically, enterprises cannot harness the power of predictive analytics because they don’t have a fully mature data strategy. To […] The post A Powerful Pair: Modern Data Warehouses and Machine Learning appeared first on DATAVERSITY.

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Dataversity

AUGUST 9, 2021

As I’ve been working to challenge the status quo on Data Governance – I get a lot of questions about how it will “really” work. The Business Dislikes Our Data Warehouse appeared first on DATAVERSITY. I’ll be sharing these questions and answers via this DATAVERSITY® series. Last year I wrote […]. The post Dear Laura: Help!

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Dataversity

AUGUST 9, 2021

As I’ve been working to challenge the status quo on Data Governance – I get a lot of questions about how it will “really” work. The Business Dislikes Our Data Warehouse appeared first on DATAVERSITY. I’ll be sharing these questions and answers via this DATAVERSITY® series. Last year I wrote […]. The post Dear Laura: Help!

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Data Warehouse Data Governance Government Data Quality 130

Dataversity

APRIL 21, 2022

SaaS apps are data-intensive, generating and accessing massive volumes of data in real time. Because of that, most organizations build SaaS apps on data warehouses instead of HTAP databases. For one, since SaaS apps operate on larger volumes of data, data warehouses […].

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Dataversity

DECEMBER 15, 2021

The abilities of an organization towards capturing, storing, and analyzing data; searching, sharing, transferring, visualizing, querying, and updating data; and meeting compliance and regulations are mandatory for any sustainable organization. For example, most data warehouses […].

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Data Warehouse Visualization Data Architecture Business Intelligence 246

Domo

DECEMBER 9, 2020

Data federation makes it simple to seamlessly integrate Domo into your existing infrastructure without a lot of implementation time, expense, or hassle. This allows you to optimize your data warehouse investments without having to recreate anything from scratch. With data federation, you can: Avoid data duplication.

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Data Warehouse IBM cost IBM Maintenance Data Modelling 98

Dataversity

MAY 6, 2022

An underlying architectural pattern is the leveraging of an open data lakehouse. That is no surprise – open data lakehouses can easily handle digital-era data types that traditional data warehouses were not designed for. Data warehouses are great at both analyzing and storing […].

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Digital Data Warehouse Customer Experience Customer Experience 246

Dataversity

DECEMBER 11, 2023

Organizations learned a valuable lesson in 2023: It isn’t sufficient to rely on securing data once it has landed in a cloud data warehouse or analytical store. As a result, data owners are highly motivated to explore technologies in 2024 that can protect data from the moment it begins its journey in the source systems.

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Dataversity

SEPTEMBER 20, 2024

However, the sheer volume, variety, and velocity of data can overwhelm traditional data management solutions. Enter the data lake – a centralized repository designed to store all types of data, whether structured, semi-structured, or unstructured.

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Smart Data Collective

DECEMBER 13, 2022

Five Best Practices for Data Analytics. Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Select a Storage Platform.

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Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

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Smart Data Collective

MARCH 30, 2022

If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. In this article, I will walk you through the process of migrating your data to data lakes.

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BizAcuity

FEBRUARY 1, 2019

And behold, today’s article is one such interesting discovery. The solution here is to consolidate all of this data, gathered from different points at different times along the course of the event and store it in one consolidated form in a Data Warehouse. Integrating Business Intelligence with Data Warehouses.

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Smart Data Collective

NOVEMBER 18, 2020

Data Mining is an important research process. It includes the analysis of hidden data models according to various translation options into useful information that is collected and generated in data warehouses to facilitate business decisions designed to reduce costs and increase income. Practical experience.

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InfoQ Articles

APRIL 22, 2021

In this article, we will define a new reference architecture for cloud-native companies that are looking for a simplified access management solution for their cloud resources, from SSH hosts, databases, data warehouses, to message pipelines and cloud storage endpoints. By Manav Mital.

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Smart Data Collective

APRIL 15, 2021

Business Intelligence uses methods and tools like machine learning to take massive, unstructured swaths of data and turn them into easy-to-use reports. This article aims to outline the process. Set Up Data Integration. But how exactly to implement BI into a company? What kinds of BI tools are available ? Pitch to Key Players.

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Dataversity

SEPTEMBER 5, 2024

… and your data warehouse / data lake / data lakehouse. Maybe an executive at your company read that article, and now you have a mandate to “modernize analytics.” A few months ago, I talked about how nearly all of our analytics architectures are stuck in the 1990s.

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ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business.

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ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business. Data Warehouse.

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Data Management Data Warehouse Government Data Governance 130

ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business. Data Warehouse.

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Smart Data Collective

JUNE 7, 2019

Data, for instance, has to be processed fast so that the companies can keep up to the changing business and market conditions in real time. This is where real-time stream processing enters the picture, and it may probably change everything you know about big data. What is Big Data?

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Dataversity

DECEMBER 16, 2020

According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].

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The Data Administration Newsletter

SEPTEMBER 14, 2021

Data lake is a newer IT term created for a new category of data store. But just what is a data lake? According to IBM, “a data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.” That makes sense. I think the […].

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Analysts Corner

JUNE 18, 2023

Data integration has become a crucial aspect of managing this information, and two popular approaches have emerged to address these needs: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). This process ensures that data is clean, accurate, and ready for analysis and reporting. ETL and ELT: Understanding the Basics 1.1

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BI Insight

AUGUST 28, 2014

In this post I’ll explain how to create a simple date dimension to use it in your data warehouses and your BI solutions.

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The Data Administration Newsletter

APRIL 6, 2021

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.

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