ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

Azure 52

Azure ML ML Data Modeling 52

Alation

FEBRUARY 20, 2020

When it was no longer a hard requirement that a physical data model be created upon the ingestion of data, there was a resulting drop in richness of the description and consistency of the data stored in Hadoop. You did not have to understand or prepare the data to get it into Hadoop, so people rarely did.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

ODSC - Open Data Science

MAY 8, 2023

To get the data, you will need to follow the instructions in the article: Create a Data Solution on Azure Synapse Analytics with Snapshot Serengeti — Part 1 — Microsoft Community Hub, where you will load data into Azure Data Lake via Azure Synapse. Lastly, upload the data from Azure Subscription.

Azure 52

Azure ML ML Data Modeling 52

ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

Deep Learning 52

Deep Learning Deep Learning AI AI 52

Data Science Dojo

JULY 6, 2023

It enables data engineers to define data models, manage dependencies, and perform automated testing, making it easier to ensure data quality and consistency. Fivetran: Fivetran is a cloud-based data integration platform that simplifies the process of loading data from various sources into a data warehouse or data lake.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

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 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. However, these tools have functional gaps for more advanced data workflows.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

AWS Machine Learning Blog

OCTOBER 20, 2023

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access. This framework considers multiple personas and services to govern the ML lifecycle at scale.

ML 126

ML ML AWS Data Lakes 126

ODSC - Open Data Science

MARCH 23, 2023

Real-time Analytics & Built-in Machine Learning Models with a Single Database Akmal Chaudhri, Senior Technical Evangelist at SingleStore, explores the importance of delivering real-time experiences in today’s big data industry and how data models and algorithms rely on powerful and versatile data infrastructure.

Data Science 52

Data Science Data Lakes Machine Learning Machine Learning 52

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. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

phData

SEPTEMBER 26, 2023

Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g. Data flows from the current data platform to the destination. Learn more about how a data model is chosen!

SQL 52

SQL Database ETL Data Modeling 52

Precisely

MARCH 27, 2023

Transforming Go-to-Market After years of acquiring and integrating smaller companies, a $37 billion multinational manufacturer of confectionery, pet food, and other food products was struggling with complex and largely disparate processes, systems, and data models that needed to be normalized.

Data Quality 52

Data Quality Data Lakes Data Governance Data Modeling 52

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 94

AWS Data Lakes Clustering Data Preparation 94

Tableau

MARCH 10, 2022

Within TCRM’s dashboard designer, you can use three object types to create visualizations: Data lake objects provide access to data ingested from various connected data sources. These allow you to build dashboards that profile, analyze, and monitor data coming into Salesforce CDP.

Tableau 98

Tableau Data Lakes Analytics Analytics 98

Tableau

MARCH 10, 2022

Within TCRM’s dashboard designer, you can use three object types to create visualizations: Data lake objects provide access to data ingested from various connected data sources. These allow you to build dashboards that profile, analyze, and monitor data coming into Salesforce CDP.

Tableau 98

Tableau Data Lakes Analytics Analytics 98

ODSC - Open Data Science

NOVEMBER 18, 2024

Dimensional Data Modeling in the Modern Era Dustin Dorsey |Principal Data Architect |Onix With the emergence of big data, cloud computing, and AI-driven analytics, many wonder if the traditional principles of dimensional modeling still hold value.

Deep Learning 40

Deep Learning Deep Learning Database Data Science 40

DagsHub

NOVEMBER 11, 2024

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? These processes are essential in AI-based big data analytics and decision-making.

AI 52

AI AI Data Lakes Database 52

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

Analytics Analytics Big Data Analytics Big Data Analytics 203

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

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. Data integration. Orchestration.

Tableau 101

Tableau Data Quality Analytics Analytics 101

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. Data integration. Orchestration.

Tableau 98

Tableau Data Quality Analytics Analytics 98

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

Data Science 98

Data Science Analytics Analytics Data Scientist 98

Tableau

OCTOBER 8, 2024

Tableau Einstein is the first BI platform with a workflow engine that spans the analytics journey from raw data, to a data lake, a semantic layer, a viz layer, and an action layer.

Tableau 52

Tableau Analytics Analytics Data Lakes 52

Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.

AWS 167

AWS ML ML Analytics 167

Alation

FEBRUARY 20, 2020

data, models…). reports, dashboards, charts, data…). In our industry, we tend to celebrate the hero data scientist or lone analyst, but what makes a data-driven organization successful are shared insights. It may be more surprising that Collaboration was a key theme for BI end users.

Data Lakes 52

Data Lakes Business Intelligence Business Intelligence Analytics 52

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.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

AWS Machine Learning Blog

NOVEMBER 6, 2024

For the past 20 years, he has been helping customers build enterprise data strategies, advising them on Generative AI, cloud implementations, migrations, reference architecture creation, data modeling best practices, and data lake/warehouse architectures.

AWS 117

AWS Machine Learning Machine Learning Python 117

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.

AI 97

AI AI Data Warehouse ML 97

Tableau

DECEMBER 7, 2022

Built-in connectors bring in data from every single channel. That includes live data streams, streaming data from web and mobile, and APIs integrated with MuleSoft to bring in external data from legacy systems or proprietary data lakes. . The power of the customer graph keeps going.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

Tableau

DECEMBER 7, 2022

Built-in connectors bring in data from every single channel. That includes live data streams, streaming data from web and mobile, and APIs integrated with MuleSoft to bring in external data from legacy systems or proprietary data lakes. . The power of the customer graph keeps going.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

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. Data Warehousing concepts and knowledge should be strong.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

OCTOBER 9, 2024

What should you be looking for?

AI 40

AI AI Apache Kafka Machine Learning 40

Alation

FEBRUARY 13, 2020

Just as you need data about finances for effective financial management, you need data about data (metadata) for effective data management. You can’t manage data without metadata. But data catalogs do much more. Figure 1 shows a logical data model that represents typical metadata content of a data catalog.

Data Lakes 52

Data Lakes Data Governance Data Science Data Analyst 52

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. A data catalog can assist directly with every step, but model development.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59