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

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 130

ML ML AWS Data Lakes 130

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

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 118

AWS Python Machine Learning Machine Learning 118

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 52

Data Modeling Data Models Apache Kafka Data Lakes 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 97

AWS Data Lakes Clustering Data Preparation 97

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

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

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

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

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

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

AWS Machine Learning Blog

FEBRUARY 21, 2025

Apache HBase was employed to offer real-time key-based access to data. Model training and scoring was performed either from Jupyter notebooks or through jobs scheduled by Apaches Oozie orchestration tool, which was part of the Hadoop implementation. HBase is employed to offer real-time key-based access to data.

Data Science 73

Data Science AWS Hadoop Data Scientist 73

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 102

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. Data integration. Orchestration.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. Architecture The architecture includes two types of SQL pools: Dedicated predictable workloads and serverless for on-demand querying Support for Apache Spark for big data processing.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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

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 Data Science AI 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 Models 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.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

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 94

Data Science Analytics Analytics Data Scientist 94

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 Models 52

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 Analytics Analytics Business Intelligence 52

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

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 Big Data 52

AWS Machine Learning Blog

SEPTEMBER 5, 2023

The portal combines these predictive alerts with other insights we derive from our AWS-based data lake in order to give our dealers more clarity into equipment health across their entire client base. Our next step is to integrate these insights into the upcoming release of Carrier’s Connected Dealer Portal.

AWS 121

AWS ML ML Machine Learning 121

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 88

AI AI Data Warehouse ML 88

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

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

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? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. Compute.

ML 145

ML ML Data Scientist AWS 145

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 Data Lakes AI 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