Dataversity

NOVEMBER 2, 2023

Among these advancements is modern data warehousing, a comprehensive approach that provides access to vast and disparate datasets. The concept of data warehousing emerged as organizations began to […] The post The Data Warehouse Development Lifecycle Explained appeared first on DATAVERSITY.

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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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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.

Data Warehouse 147

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.

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Data Warehouse Business Intelligence Business Intelligence Data Quality 147

Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

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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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Data Warehouse Artificial Intelligence Predictive Analytics Data Architecture 130

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!

Data Warehouse 130

Data Warehouse Data Governance Government Data Quality 130

ElegantJ BI

APRIL 28, 2021

In the second of these two articles entitled, ‘Factors and Considerations Involved in Choosing a Data Management Solution’, we discuss the various factors and considerations that a business should include when it is ready to choose a data management solution. Think of a Data Mart as a ‘subject’ or ‘concept’ oriented data repository.

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Data Warehouse Data Mining Data Management Data Governance 130

ElegantJ BI

APRIL 28, 2021

In the second of these two articles entitled, ‘Factors and Considerations Involved in Choosing a Data Management Solution’, we discuss the various factors and considerations that a business should include when it is ready to choose a data management solution. Data Warehouse. Data Lake.

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Data Warehouse Data Mining Data Management Data Governance 130

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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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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Smart Data Collective

JUNE 15, 2021

It’s also possible to employ extra caching or materialized views in the data warehouse in addition to caching in Looker (depending on the capability of your data warehouse). One added tip is to aggregate your data before loading it into Looker or in the data warehouse to reduce the amount of data loaded onto the platform.

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Smart Data Collective

OCTOBER 27, 2021

Snowflake provides the right balance between the cloud and data warehousing, especially when data warehouses like Teradata and Oracle are becoming too expensive for their users. It is also easy to get started with Snowflake as the typical complexity of data warehouses like Teradata and Oracle are hidden from the users. .

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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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Analysts Corner

NOVEMBER 29, 2023

Introduction to Snowflake Schema in Data Warehouse Modeling Continue reading on Analyst’s corner »

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Data Warehouse 130

Analysts Corner

DECEMBER 4, 2023

Introduction to Star Schema in Data Warehouse Modeling Continue reading on Analyst’s corner »

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Data Warehouse 130

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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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

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

Smart Data Collective

MAY 31, 2022

Data activation is a new and exciting way that businesses can think of their data. It’s more than just data that provides the information necessary to make wise, data-driven decisions. It’s more than just allowing access to data warehouses that were becoming dangerously close to data silos.

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Data Warehouse Big Data Customer Experience Customer Experience 355

Insight Software

APRIL 19, 2021

This puts tremendous stress on the teams managing data warehouses, and they struggle to keep up with the demand for increasingly advanced analytic requests. To gather and clean data from all internal systems and gain the business insights needed to make smarter decisions, businesses need to invest in data warehouse automation.

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Data Warehouse Documentation Business Intelligence Business Intelligence 98

Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

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Simplilearn

APRIL 12, 2021

In today’s rapidly changing corporate environment, organizations are turning to cloud-based technologies for convenient data collection, reporting, and analysis. This is where Data Warehousing comes in as a core component of business intelligence that enables businesses to enhance their performance. Read More.

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Data Warehouse Business Intelligence Business Intelligence 189

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

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IBM Support IBM cost Data Warehouse Business Intelligence 316

Simplilearn

OCTOBER 6, 2021

That’s where data warehousing comes in. Data warehouses are central repo. Access to relevant customer and industry information is the primary competitive advantage businesses have over their direct and indirect competitors today. Read More.

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Data Warehouse 290

Smart Data Collective

MAY 16, 2022

These include, but are not limited to, database management systems, data mining software, decision support systems, knowledge management systems, data warehousing, and enterprise data warehouses. Some data management strategies are in-house and others are outsourced.

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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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Simplilearn

JUNE 13, 2024

While data warehouses and server farms were once considered to be the ultimate choice for computing speed, the focus has quickly shifted to the concept of cloud or “offsite storag. Do you remember the first ever huge and bulky sets of computers? Read More.

Data Warehouse 246

Data Warehouse 246

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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Data Warehouse Data Management Data Architecture 243

Simplilearn

DECEMBER 27, 2022

Enterprises utilize data to optimize practically all business operations today. Traditional databases, on the other hand, do not meet the shifting demands of data analysis, which need access to big data for visualization and reporting. Dated databases enable improved performance while managing little transactional data at speed.

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Simplilearn

DECEMBER 27, 2022

One of the many variations of the star schema is the snowflake schema. The centralized fact table is linked to multiple dimensions in this case. Dimensions are present in a normalized form in multiple related tables in the snowflake schema. When the dimensions of a star schema are detailed and highly structured, with several levels of relationsh.

Data Warehouse 147

Data Warehouse 147

BizAcuity

NOVEMBER 22, 2022

This typically requires a data warehouse for analytics needs that is able to ingest and handle real time data of huge volumes. Snowflake is a cloud-native platform that eliminates the need for separate data warehouses, data lakes, and data marts allowing secure data sharing across the organization.

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Simplilearn

DECEMBER 27, 2022

In the modern day, data platforms are an essential component of business success. Organizations that make greater use of their data have a significant edge over their competition. Making use of data is easier said than done. Read More.

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