Data Modeling, Data Warehouse and Events

Data Modeling Fundamentals in Power BI

phData

JUNE 13, 2023

While the front-end report visuals are important and the most visible to end users, a lot goes on behind the scenes that contribute heavily to the end product, including data modeling. In this blog, we’ll describe data modeling and its significance in Power BI. What is Data Modeling?

Power BI

Power BI Data Modeling Data Models Data Warehouse

Data Version Control for Data Lakes: Handling the Changes in Large Scale

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.

Data Lakes

Data Lakes Data Warehouse Database Big Data

Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics

Analytics Analytics Big Data Analytics Big Data Analytics

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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling

Data Modeling Data Models Apache Kafka Data Lakes

Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

NOVEMBER 18, 2020

Complex mathematical algorithms are used to segment data and estimate the likelihood of subsequent events. Every Data Scientist needs to know Data Mining as well, but about this moment we will talk a bit later. Where to Use Data Science? Data Mining Techniques and Data Visualization.

Data Mining

Data Mining Data Mining Data Mining Data Science

The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

What is a Customer Data Platform (CDP)?

phData

MARCH 11, 2024

For years, marketing teams across industries have turned to implementing traditional Customer Data Platforms (CDPs) as separate systems purpose-built to unlock growth with first-party data. Event Tracking : Capturing behavioral events such as page views, add-to-cart, signup, purchase, subscription, etc.

Data Warehouse

Data Warehouse Cloud Data Data Modeling Data Models

Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse

Data Warehouse Business Intelligence Business Intelligence Database

Discover the Most Important Fundamentals of Data Engineering

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

How to use foundation models and trusted governance to manage AI workflow risk

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. Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data.

AI

AI AI Data Warehouse ML

Star Schema vs. Snowflake Schema: Comparing Dimensional Modeling Techniques

Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. It is commonly used in data warehouses for business analytics and reporting.

Data Warehouse

Data Warehouse Business Intelligence Business Intelligence Database

Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

SEPTEMBER 19, 2023

Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred. 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.

Data Science

Data Science Analytics Analytics Data Scientist

Best Practices for Fact Tables in Dimensional Models

Pickl AI

AUGUST 11, 2024

These tables are called “factless fact tables” or “junction tables” They are used for modelling many-to-many relationships or for capturing timestamps of events. This schema serves as the foundation of dimensional modeling. A star schema forms when a fact table combines with its dimension tables.

Data Quality

Data Quality Data Warehouse Data Governance Analytics

From zero to BI hero: Launching your business intelligence career

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst: A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence

Business Intelligence Business Intelligence Data Analysis Data Analysis

From zero to BI hero: Launching your business intelligence career

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst: A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence

Business Intelligence Business Intelligence Data Analysis Data Analysis

What is Data Management? A Complete Guide With Examples & Benefits

Pickl AI

MAY 11, 2023

The Data Steward is responsible for the same. Their prime focus is to keep a tab on data collection and ensure that the exchange and movement of data are as per the policies. Data Modeling These are simple diagrams of the system and the data stored in it. It helped in the verification of data.

Data Warehouse

Data Warehouse Data Governance Big Data Big Data

How Alation’s Data Team Uses the Modern Data Stack to Power Insights

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Data Analyst

Data Analyst Data Scientist Analytics Analytics

What Free Tools Pair Well With The Snowflake AI Data Cloud?

phData

OCTOBER 17, 2024

The DAGs can then be scheduled to run at specific intervals or triggered when an event occurs. By leveraging version control, testing, and documentation features, dbt Core enables teams to ensure data quality and consistency across their pipelines while integrating seamlessly with modern data warehouses.

AI

AI AI SQL Data Quality

Data Demystified: What Exactly is Data?- 4 Types of Analytics

Pickl AI

JULY 23, 2023

Proper data collection practices are critical to ensure accuracy and reliability. Data Storage After collection, the data needs a secure and accessible storage system. Organizations may use databases, data warehouses, or cloud-based storage solutions depending on the type and volume of data.

Analytics

Analytics Analytics Predictive Analytics Data Analysis

Comparing Tools For Data Processing Pipelines

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. It also involves data enriching – a crucial step for further data travel.

Data Pipeline

Data Pipeline ETL SQL Data Quality

Most Common Use Cases of Data Engineering in Healthcare

phData

AUGUST 11, 2023

In Australia, the government’s healthcare branch uses Data Integration Partnership for Australia (DIPA) to identify adverse events. Norway is also making use of big data analytics to keep track of national health trends. Through big data models, hospitals can identify trends that guide smart decision-making.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

dbt Labs’ Coalesce 2023 Recap

phData

NOVEMBER 13, 2023

Delphi Prerequisites and Compatibility: It requires a dbt Cloud Team or Enterprise account and supports popular data warehouses like Snowflake, BigQuery, Databricks, and Redshift. These jobs can be triggered via schedule or events, ensuring your data assets are always up-to-date.

Database

Database Business Intelligence Business Intelligence Data Silos

How to Integrate SAP Data With Snowflake

phData

MAY 13, 2024

Built for integration, scalability, governance, and industry-leading security, Snowflake optimizes how you can leverage your organization’s data, providing the following benefits: Built to Be a Source of Truth Snowflake is built to simplify data integration wherever it lives and whatever form it takes.

Database

Database Analytics Analytics Machine Learning

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Data Modeling Fundamentals in Power BI

Data Version Control for Data Lakes: Handling the Changes in Large Scale

Webinars

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Beyond data: Cloud analytics mastery for business brilliance

Webinars

The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

What is a Customer Data Platform (CDP)?

Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Discover the Most Important Fundamentals of Data Engineering

How to use foundation models and trusted governance to manage AI workflow risk

Star Schema vs. Snowflake Schema: Comparing Dimensional Modeling Techniques

Data science vs data analytics: Unpacking the differences

Best Practices for Fact Tables in Dimensional Models

From zero to BI hero: Launching your business intelligence career

From zero to BI hero: Launching your business intelligence career

What is Data Management? A Complete Guide With Examples & Benefits

How Alation’s Data Team Uses the Modern Data Stack to Power Insights

What Free Tools Pair Well With The Snowflake AI Data Cloud?

Data Demystified: What Exactly is Data?- 4 Types of Analytics

Comparing Tools For Data Processing Pipelines

Most Common Use Cases of Data Engineering in Healthcare

dbt Labs’ Coalesce 2023 Recap

How to Integrate SAP Data With Snowflake

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