Data Science Dojo

FEBRUARY 9, 2023

It allows users to connect to a variety of data sources, perform data preparation and transformations, create interactive visualizations, and share insights with others. The platform includes features such as data modeling, data discovery, data analysis, and interactive dashboards.

Power BI 221

Power BI Data Analyst Data Visualization Data Analysis 221

Cambridge Intelligence

NOVEMBER 20, 2023

This Azure Cosmos DB tutorial shows you how to integrate Microsoft’s multi-model database service with our graph and timeline visualization SDKs to build an interactive graph application. There’s support for MongoDB, PostgreSQL, Apache Cassandra, Apache Gremlin, and Tables, and our data visualization toolkits work with all of them.

Azure 64

Azure Database Data Modeling Data Models 64

Pickl AI

MARCH 4, 2024

Loading data into Power BI is a straightforward process. Using Power Query, users can connect to various data sources such as Excel files, SQL databases, or cloud services like Azure. Once connected, data can be transformed and loaded into Power BI for analysis. How do you handle real-time data streaming in Power BI?

Power BI 52

Power BI Data Analysis Data Analysis Data Modeling 52

ODSC - Open Data Science

MAY 8, 2023

Article on Azure ML by Bethany Jepchumba and Josh Ndemenge of Microsoft In this article, I will cover how you can train a model using Notebooks in Azure Machine Learning Studio. At the end of this article, you will learn how to use Pytorch pretrained DenseNet 201 model to classify different animals into 48 distinct categories.

Azure 52

Azure ML ML Data Modeling 52

Data Science Blog

SEPTEMBER 19, 2023

This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure. Enhanced Security and Compliance Data Warehouses often store sensitive information, making security a paramount concern. Of course, Terraform and the Azure CLI needs to be installed before.

Data Warehouse 259

Data Warehouse Azure SQL Database 259

Snorkel AI

OCTOBER 18, 2023

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results.

AI 64

AI AI Machine Learning Machine Learning 64

Smart Data Collective

OCTOBER 19, 2022

MongoDB is deployable anywhere, and the MongoDB Atlas database-as-a-service can be deployed on AWS, Azure, and Google Cloud Platform (GCP). What Are Their Ranges of Data Models? MongoDB has a wider range of datatypes than DynamoDB, even though both databases can store binary data.

Big Data 112

Big Data Big Data Database AWS 112

Snorkel AI

OCTOBER 18, 2023

Claims data is often noisy, unstructured, and multi-modal. Manually aligning and labeling this data is laborious and expensive, but—without high-quality representative training data—models are likely to make errors and produce inaccurate results.

AI 59

AI AI Machine Learning Machine Learning 59

Data Science Blog

NOVEMBER 15, 2023

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

Data Modeling 245

Data Modeling Data Models Business Intelligence Business Intelligence 245

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

Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Use Multiple Data Models With on-premise data warehouses, storing multiple copies of data can be too expensive.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

MAY 25, 2023

One big issue that contributes to this resistance is that although Snowflake is a great cloud data warehousing platform, Microsoft has a data warehousing tool of its own called Synapse. In a perfect world, Microsoft would have clients push even more storage and compute to its Azure Synapse platform.

Power BI 84

Power BI Analytics Analytics Azure 84

Revolutions

JANUARY 21, 2021

by Hong Ooi Last week , I announced AzureCosmosR, an R interface to Azure Cosmos DB , a fully-managed NoSQL database service in Azure. Explaining what Azure Cosmos DB is can be tricky, so here’s an excerpt from the official description : Azure Cosmos DB is a fully managed NoSQL database for modern app development.

Azure 40

Azure SQL Database Administration Database 40

Pickl AI

JULY 25, 2023

Skills and Tools of Data Engineers Data Engineering requires a unique set of skills, including: Database Management: SQL, NoSQL, NewSQL, etc. Data Warehousing: Amazon Redshift, Google BigQuery, etc. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Apache Hadoop 59

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science 105

Data Science Exploratory Data Analysis Machine Learning Machine Learning 105

ODSC - Open Data Science

JANUARY 25, 2023

The Azure ML team has long focused on bringing you a resilient product, and its latest features take one giant leap in that direction, as illustrated in the graph below (Figure 1). Continue reading to learn more about Azure ML’s latest announcements. This is the motivation behind several of Azure ML’s latest features.

Machine Learning 59

Machine Learning Machine Learning Azure ML 59

phData

DECEMBER 13, 2023

FAQs What are dbt models? A dbt model is how you want to create a table or view in your data model. You can use the SQL Select statement to write a model. A dbt python model is a model that uses the Python language and is defined using the.py Contact our dbt Experts Today! What is dbt Python?

SQL 52

SQL Python Azure Data Modeling 52

Smart Data Collective

NOVEMBER 24, 2020

Features: intuitive visualizations on-premise and cloud report sharing dashboard and report publishing to the web indicators of data patterns integration with third-party services (Salesforce, Google Analytics, Zendesk, Azure, Mailchimp, etc.). Unique feature: custom visualizations to fit your business needs better. SAP Lumira.

Business Intelligence 131

Business Intelligence Business Intelligence Power BI Augmented Analytics 131

Hacker News

NOVEMBER 14, 2023

The data from D10 was never actually transferred to D11, meaning the business is now using 2 systems instead of 1. D11 data model doesn’t really support the data in D10 either. Technology teams demanded that BackEnd be built in Microsoft Azure Pipelines, to comply with “Strategic Vision”.

Power BI 120

Power BI Database Algorithm Azure 120

Snorkel AI

JUNE 7, 2023

Fireside Chat: The Building Blocks of Modern Enterprise AI Aparna Lakshmi Ratan from Snorkel AI and Marco Casalaina from Azure Cognitive Services delved into the core elements of modern enterprise AI, exploring the convergence of data, models, and MLops platforms.

Data Scientist 52

Data Scientist AI AI Machine Learning 52

Snorkel AI

JUNE 7, 2023

Fireside Chat: The Building Blocks of Modern Enterprise AI Aparna Lakshmi Ratan from Snorkel AI and Marco Casalaina from Azure Cognitive Services delved into the core elements of modern enterprise AI, exploring the convergence of data, models, and MLops platforms.

Data Scientist 52

Data Scientist AI AI Machine Learning 52

phData

SEPTEMBER 28, 2023

Attach a Common Data Model Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common Data Model (CDM) format by another application. With the import option, users can create a new version of the Dataflow while the original Dataflow remains unchanged.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

The MLOps Blog

NOVEMBER 30, 2022

Now that we understand visibility shares vital details of the model, let us learn what the barriers to visibility are: Decoupled pieces: The data, code, configuration, and results are generated at different steps during the project. But it is not built with machine learning models in mind.

ML 52

ML ML Machine Learning Machine Learning 52

Cambridge Intelligence

OCTOBER 19, 2023

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores. Transactional, analytical, or both…?

Database 59

Database Azure SQL AWS 59

DagsHub

APRIL 21, 2024

Amazon SageMaker pricing is based on a pay-as-you-go model, with costs calculated based on factors such as instance type, storage usage, and training hours. Similar to SageMaker, Azure ML offers a range of tools and services for the entire machine learning lifecycle, from data preparation and model development to deployment and monitoring.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

JULY 11, 2023

Power BI Datamarts provides a low/no code experience directly within Power BI Service that allows developers to ingest data from disparate sources, perform ETL tasks with Power Query, and load data into a fully managed Azure SQL database. Blog: Data Modeling Fundamentals in Power BI. a.

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Hacker News

JULY 18, 2023

What if you could automatically shard your PostgreSQL database across any number of servers and get industry-leading performance at scale without any special data modelling steps? Schema-based sharding has almost no data modelling restrictions or special steps compared to unsharded PostgreSQL.

Database 123

Database SQL Data Modeling Data Models 123

The MLOps Blog

JANUARY 25, 2023

When training the models on this type of data, models can be biased towards some text while ignoring others. Solution To solve the potential bias in the training data, you can start with debiasing techniques. Solution There are several solutions for deploying a sentiment classification model.

Natural Language Processing 52

Natural Language Processing ML ML Deep Learning 52

ODSC - Open Data Science

APRIL 6, 2023

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling. Finally, cloud services.

SQL 98

SQL Data Scientist Database Data Analysis 98

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

Mlearning.ai

SEPTEMBER 4, 2023

Turbo model, this may be a better fit for some businesses.[2],[3] 2],[3] It’s important to note that even with a million-dollar investment, it may be challenging to match the general performance and latency of these commercial models.[4] 4] Private instances: Microsoft Azure provides a private instance of ChatGPT.

AI 52

AI AI Azure Machine Learning 52

The MLOps Blog

JUNE 27, 2023

SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale. Model versioning, lineage, and packaging : Can you version and reproduce models and experiments?

Machine Learning 134

Machine Learning Machine Learning ML ML 134

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? As a commercial product, Databricks provides a managed environment that combines data-centric notebooks with a proprietary production infrastructure.

ML 138

ML ML Data Scientist AWS 138

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

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AI AI Data Analysis Data Analysis 52

Data Science Blog

JULY 23, 2023

One of this aspect is the cloud architecture for the realization of Data Mesh. Data Mesh on Azure Cloud with Databricks and Delta Lake for Applications of Business Intelligence, Data Science and Process Mining. It offers robust IoT and edge computing capabilities, advanced data analytics, and AI services.

Data Science 246

Data Science Azure Power BI Business Intelligence 246

The MLOps Blog

OCTOBER 3, 2023

It’s almost like a specialized data processing and storage solution. For example, you can use BigQuery , AWS , or Azure. My advice to people would be that one, all the boring ML and data infrastructure and knowledge is more important than ever. And if you want to use different inference stores, you have that option.

ML 52

ML ML Data Scientist Machine Learning 52