Dataversity

DECEMBER 7, 2020

Data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations who seek to empower more and better data-driven decisions and actions throughout their enterprises. These groups want to expand their user base for data discovery, BI, and analytics so that their business […].

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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

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

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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Dataversity

AUGUST 15, 2022

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.

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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 Profiling 59

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 Profiling 59

The Data Administration Newsletter

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.

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Data Quality Data Warehouse Data Governance 52

Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business? Let’s take a closer look.

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Dataversity

MAY 26, 2023

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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The Data Administration Newsletter

FEBRUARY 1, 2022

In this blog, we will discuss a common problem for data warehouses that are designed to maintain data quality and provide evidence of accuracy. Without verification, the data can’t be trusted. Enter the mundane, but necessary, task of data reconciliation. This is often a time-consuming and wasteful process.

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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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Dataversity

FEBRUARY 13, 2024

Are you drowning in data? Feeling shackled by rigid data warehouses that can’t keep pace with your ever-evolving business needs? Traditional data storage strategies are crumbling under the weight of diverse data sources, leaving you with limited analytics and frustrated decisions. You’re not alone.

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Dataversity

FEBRUARY 10, 2023

It’s no surprise that, in 2023, business enterprises want to become truly data-driven organizations. For many of these organizations, the path toward becoming more data-driven lies in the power of data lakehouses, which combine elements of data warehouse architecture with data lakes.

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phData

SEPTEMBER 19, 2023

A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a data warehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.

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Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Pickl AI

AUGUST 31, 2023

However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. Accordingly, the need for Data Profiling in ETL becomes important for ensuring higher data quality as per business requirements. Evaluate the accuracy and completeness of the 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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Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

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Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

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Data Science Dojo

SEPTEMBER 6, 2023

M aintaining the security and governance of data within a data warehouse is of utmost importance. Data Security: A Multi-layered Approach In data warehousing, data security is not a single barrier but a well-constructed series of layers, each contributing to protecting valuable information.

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Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

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Alation

AUGUST 30, 2022

These range from data sources , including SaaS applications like Salesforce; ELT like Fivetran; cloud data warehouses like Snowflake; and data science and BI tools like Tableau. This expansive map of tools constitutes today’s modern data stack. In 2022.3, In 2022.3,

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Data Governance Data Quality Tableau Data Analyst 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation. Microsoft Azure.

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Alation

FEBRUARY 7, 2023

It wouldn’t be until 2013 that the topic of data lineage would surface again – this time while working on a data warehouse project. Data warehouses obfuscate data’s origin In 2013, I was a Business Intelligence Engineer at a financial services company. What’s the right lineage level? It depends!

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Data Warehouse ETL Business Intelligence Business Intelligence 52

Dataconomy

SEPTEMBER 4, 2023

Understand what insights you need to gain from your data to drive business growth and strategy. Best practices in cloud analytics are essential to maintain data quality, security, and compliance ( Image credit ) Data governance: Establish robust data governance practices to ensure data quality, security, and compliance.

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Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming. Two problems arise.

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phData

JANUARY 24, 2024

Fivetran includes features like data movement, transformations, robust security, and compatibility with third-party tools like DBT, Airflow, Atlan, and more. Its seamless integration with popular cloud data warehouses like Snowflake can provide the scalability needed as your business grows.

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Alation

APRIL 27, 2023

Article reposted with permission from Eckerson ABSTRACT: Data mesh is giving many of us from the data warehouse generation a serious case of agita. But, my fellow old-school data tamers, it’s going to be ok. It’s a subject that’s giving many of us from the data warehouse generation a serious case of agita.

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Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

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phData

JULY 19, 2023

At its core, Fivetran is a data integration platform that enables you to effortlessly connect and centralize your data from various sources into your preferred data destination, such as a data warehouse or cloud storage. Fivetran’s benefits extend to businesses across industries and sizes.

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Data Warehouse Database Data Pipeline SQL 52

phData

JULY 19, 2023

At its core, Fivetran is a data integration platform that enables you to effortlessly connect and centralize your data from various sources into your preferred data destination, such as a data warehouse or cloud storage. Fivetran’s benefits extend to businesses across industries and sizes.

Data Warehouse 52

Data Warehouse Database Data Pipeline SQL 52

phData

JUNE 27, 2023

Data Ingestion with Fivetran Fivetran is used to move your source(s) into a centralized space for storage. Data Storage with Snowflake Snowflake is the main data warehouse, the foundation. Storing all the collected data sent from Fivetran Once in Snowflake, the data is ready to be accessed and analyzed.

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