IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. ” Are foundation models trustworthy?

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AI AI Data Warehouse ML 88

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 203

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ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. in an enterprise data warehouse. What is a Datamart?

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Power BI Data Warehouse ETL Data Preparation 52

Smart Data Collective

NOVEMBER 18, 2020

This new approach has proven to be much more effective, so it is a skill set that people must master to become data scientists. For example, many cryptocurrency platforms like Safetrading use AI to review services that provide free trading signals , which makes accuracy and speed higher. Definition: Data Mining vs Data Science.

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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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Data Warehouse Data Pipeline Cloud Data ETL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. As an early adopter of large language model (LLM) technology, Zeta released Email Subject Line Generation in 2021.

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AWS Machine Learning Machine Learning ML 123

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Natively connect to trusted, unified customer data.

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Tableau Data Warehouse Data Pipeline Data Visualization 98

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Natively connect to trusted, unified customer data.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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phData

JUNE 6, 2024

These traditional CDPs are designed to gather and house their own data store—separate from the core data infrastructure. Because of this separation, data models are rigid, and the setup process is costly and lengthy. Data gets ingested, centralized, and deployed within your cloud data warehouse.

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Data Warehouse Cloud Data ETL Data Modeling 52

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.

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Data Engineering Data Engineer Data Engineering Data Engineering 52

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?

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Tableau

APRIL 18, 2022

Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Data preparation. Virtualization and discovery.

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Tableau Data Quality Analytics Analytics 102

Tableau

APRIL 18, 2022

Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Data preparation. Virtualization and discovery.

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Tableau Data Quality Analytics Analytics 98

phData

JULY 18, 2023

The good news is that there’s a concept called the Modern Data Stack that when utilized properly, consistently helps empower organizations to harness the full potential of their data. Throughout this journey, we’ve helped hundreds of clients achieve eye-opening results by moving to the Modern Data Stack.

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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.

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Data Warehouse Business Intelligence Business Intelligence Database 52

Women in Big Data

DECEMBER 9, 2024

Qlik Sense – Qlik Sense is a powerful business intelligence and data visualization tool designed to facilitate data exploration, visualization, and storytelling. Google Looker – Lookers user experience is generally considered more technical due to its reliance on LookML which is Lookers modeling language for data modeling.

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Big Data Big Data Power BI Tableau 52

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? Data is at the core of any ML project, so data infrastructure is a foundational concern. Why did something break? Who did what and when?

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ML ML Data Scientist AWS 145

Precisely

OCTOBER 23, 2023

As more organizations tap into the value of advanced analytics and AI, MDM has emerged as a vital element for trusted data and confident decisions. Very often, key business users conflate MDM with various tasks or components of data science and data management. Others regard it as a data modeling platform.

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Data Quality Data Warehouse Analytics Analytics 110

phData

OCTOBER 17, 2024

Using 3rd party tooling is essential if you’re a Snowflake AI Data Cloud customer. Getting your data into Snowflake, creating analytics applications from the data, and even ensuring your Snowflake account runs smoothly all require some sort of tool. But you still want to start building out the data model.

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AI AI SQL Data Quality 52

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.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 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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Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

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Business Intelligence Business Intelligence ETL Data Lakes 52

DagsHub

NOVEMBER 11, 2024

Many find themselves swamped by the volume and complexity of unstructured data. In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data?

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AI AI Data Lakes Database 52

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. This led to the theory and development of AI.

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Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

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Data Warehouse Data Quality ETL Business Intelligence 52

Precisely

OCTOBER 16, 2023

Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale.

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Analytics Analytics Big Data Analytics Big Data Analytics 72

Tableau

DECEMBER 7, 2022

Traditionally, organizations built complex data pipelines to replicate data. Those data architectures were brittle, complex, and time intensive to build and maintain, requiring data duplication and bloated data warehouse investments. Salesforce Data Cloud for Tableau solves those challenges.

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Tableau Data Warehouse Data Pipeline Data Visualization 55

Pickl AI

JUNE 28, 2023

Data cleaning, normalization, and reformatting to match the target schema is used. · Data Loading It is the final step where transformed data is loaded into a target system, such as a data warehouse or a data lake. It ensures that the integrated data is available for analysis and reporting.

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Data Mining Data Mining Data Mining ETL 52

Pickl AI

AUGUST 11, 2024

Retail Sales In a retail data warehouse , the sales fact table might include metrics such as sales revenue, units sold, discounts applied, and profit margins. Web Analytics In a web analytics data warehouse, the page views fact table might include metrics such as total page views, unique visitors, session duration, and bounce rate.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

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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Analytics Analytics Data Warehouse Database 40

phData

MAY 13, 2024

Difficulty in moving non-SAP data into SAP for analytics which encourages data silos and shadow IT practices as business users search for ways to extract the data (which has data governance implications). Additionally, change data markers are not available for many of these tables.

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Database Analytics Analytics Machine Learning 52

phData

JULY 10, 2024

Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. The platform’s integration with cloud data warehouses like Snowflake AI Data Cloud , Google BigQuery, and Amazon Redshift makes it a vital tool for organizations harnessing big data.

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SQL Data Warehouse Analytics Analytics 52

Pickl AI

MARCH 3, 2025

Ensuring data accuracy and consistency through cleansing and validation processes. Data Analysis and Modelling Applying statistical techniques and analytical tools to identify trends, patterns, and anomalies. Developing data models to support analysis and reporting. Ensuring data integrity and security.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

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phData

SEPTEMBER 25, 2024

In this blog, we will provide a comprehensive overview of ETL considerations, introduce key tools such as Fivetran, Salesforce, and Snowflake AI Data Cloud , and demonstrate how to set up a pipeline and ingest data between Salesforce and Snowflake using Fivetran. What is Fivetran?

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Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required.

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