Data Science Connect

JANUARY 27, 2023

It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible. Learn about data modeling: Data modeling is the process of creating a conceptual representation of data.

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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

JUNE 24, 2022

Data models play an integral role in the development of effective data architecture for modern businesses. They are key to the conceptualization, planning, and building of an integrated data repository that drives advanced analytics and BI.

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Data Models Data Modeling Data Warehouse Analytics 52

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

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Data Lakes Data Models Data Modeling Data Warehouse 52

DataRobot

JANUARY 20, 2022

As health services consolidate and organizational boundaries creep, there is an urgent need to implement highly flexible and scalable data management systems to enable real-time data sharing and modelling across systems, partners, and third party organizations. Technology Alliance.

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Pickl AI

MARCH 19, 2025

Data engineering is all about collecting, organising, and moving data so businesses can make better decisions. Handling massive amounts of data would be a nightmare without the right tools. In this blog, well explore the best data engineering tools that make data work easier, faster, and more reliable.

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phData

OCTOBER 9, 2023

However, to harness the full potential of Snowflake’s performance capabilities, it is essential to adopt strategies tailored explicitly for data vault modeling. It is agile, scalable, no pre-modeling required, and well-suited for fluid designs. FAQs What’s the difference between a star schema and a data vault?

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phData

JULY 17, 2023

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data. Data modeling, data cleanup, etc.

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phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

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phData

JUNE 6, 2024

Marketing and business professionals must effectively manage and leverage their customer data to stay competitive. In this blog, we will explore how marketing professionals have approached the challenge of effectively using their vast amount of customer data using Composable CDPs. Why use Fivetran for Composable CDP?

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Data Warehouse Cloud Data ETL Data Modeling 52

Flipboard

MAY 30, 2025

For the preceding techniques, the foundation should provide scalable infrastructure for data storage and training, a mechanism to orchestrate tuning and training pipelines, a model registry to centrally register and govern the model, and infrastructure to host the model.

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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phData

AUGUST 16, 2023

To address these complexities, a powerful data warehousing solution like the Snowflake Data Cloud , coupled with an effective data modeling approach such as the Data Vault architecture, can be a winning combination. What is a Data Vault Architecture? Using dbt is one of the best choices.

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Data Warehouse Data Governance SQL Data Modeling 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. This blog post delves into the details of this MLOps platform, exploring how the integration of these tools facilitates a more efficient and scalable approach to managing ML projects.

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Pickl AI

JULY 25, 2024

Introduction Dimensional modelling is crucial for organising data to enhance query performance and reporting efficiency. Effective schema design is essential for optimising data retrieval and analysis in data warehousing. Must Read Blogs: Exploring the Power of Data Warehouse Functionality.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

js and Tableau Data science, data analytics and IBM Practicing data science isn’t without its challenges. There can be fragmented data, a short supply of data science skills and rigid IT standards for training and deployment. Watsonx comprises of three powerful components: the watsonx.ai

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phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

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phData

MARCH 11, 2024

This blog was co-written by Alex Roed, Luke Kline, Sam Hall, Alec Haase, and Christian Franklin. The depreciation of third-party cookies has only compounded this problem, leaving companies scrambling to figure out how to implement the systems needed to power growth with their first-party customer data. Modern marketing is changing.

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Pickl AI

AUGUST 9, 2024

Summary: This blog delves into hierarchies in dimensional modelling, highlighting their significance in data organisation and analysis. Real-world examples illustrate their application, while tools and technologies facilitate effective hierarchical data management in various industries.

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IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

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phData

MAY 25, 2023

One of the easiest ways for Snowflake to achieve this is to have analytics solutions query their data warehouse in real-time (also known as DirectQuery). If you’re interested in learning more, we highly recommend checking out our comprehensive blog that covers this in much more detail.

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Pickl AI

JANUARY 28, 2025

Introduction Business Intelligence (BI) architecture is a crucial framework that organizations use to collect, integrate, analyze, and present business data. This architecture serves as a blueprint for BI initiatives, ensuring that data-driven decision-making is efficient and effective. time, product) and facts (e.g.,

Business Intelligence 52

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Pickl AI

AUGUST 11, 2024

Summary: This blog discusses best practices for designing effective fact tables in dimensional models. Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

Alation

SEPTEMBER 7, 2021

This is the last of the 4-part blog series. In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. Subscribe to Alation's Blog.

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phData

JULY 10, 2024

These tools allow users to handle more advanced data tasks and analyses. In this blog, we’ll explain why custom SQL and CSVs are important, demonstrate how to use these features in Sigma Computing, and provide some best practices to help you get started. Choose your desired data source type (e.g.,

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SQL Data Warehouse Analytics Analytics 52

phData

FEBRUARY 8, 2024

Data Modeling, dbt has gradually emerged as a powerful tool that largely simplifies the process of building and handling data pipelines. dbt is an open-source command-line tool that allows data engineers to transform, test, and document the data into one single hub which follows the best practices of software engineering.

Data Models 52

Data Models Data Modeling Data Warehouse Database 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

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Pickl AI

JUNE 28, 2023

But, this data is often stored in disparate systems and formats. Here comes the role of Data Mining. Read this blog to know more about Data Integration in Data Mining, The process encompasses various techniques that help filter useful data from the resource. Wrapping It Up !!!

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IBM Journey to AI blog

SEPTEMBER 17, 2024

The solution is designed to manage enormous memory capacity, enabling you to build large and complex data models while maintaining smooth performance and usability. Many customers use models with hundreds of thousands or even millions of data points.

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phData

JUNE 27, 2023

All of which have a specific role used to collect, store, process, and analyze data. This blog will hone in on the new collaboration, how to implement it into your workbooks, and why Sigma users should be excited about the feature. Data Storage with Snowflake Snowflake is the main data warehouse, the foundation.

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Pickl AI

MARCH 3, 2025

Business Intelligence Analysts are the skilled artisans who transform this raw data into valuable insights, empowering organizations to make strategic decisions and stay ahead of the curve. Key Takeaways BI Analysts convert data into actionable insights for strategic business decisions. Ensuring data integrity and security.

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phData

AUGUST 10, 2023

In this blog, our focus will be on exploring the data lifecycle along with several Design Patterns, delving into their benefits and constraints. Data architects can leverage these patterns as starting points or reference models when designing and implementing data vault architectures.

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phData

JULY 20, 2023

In this blog post, we’ll explore how Sigma Computing offers a novel approach to solve not only complex scenarios like identifying specific items in retail orders, but also multiple levels of criteria like tracking customer behavior from retail orders. Sigma Computing offers a different solution with its flexible modeling abilities.

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phData

JULY 20, 2023

In this blog post, we’ll explore how Sigma Computing offers a novel approach to solve not only complex scenarios like identifying specific items in retail orders, but also multiple levels of criteria like tracking customer behavior from retail orders. Sigma Computing offers a different solution with its flexible modeling abilities.

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Data Warehouse Database Data Modeling Data Models 52

phData

JULY 12, 2023

Businesses today are grappling with vast amounts of data coming from diverse sources. To effectively manage and harness this data, many organizations are turning to a data vault—a flexible and scalable data modeling approach that supports agile data integration and analytics.

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Clustering Data Warehouse Data Quality Data Modeling 52

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

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Iguazio

JANUARY 22, 2024

But doing so requires significant engineering, quality data and overcoming risks. In this blog post, we show all the elements and practices you need to to take to productize LLMs and generative AI. You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here.

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