Smart Data Collective

JUNE 20, 2022

When data leaders move to the cloud, it’s easy to get caught up in the features and capabilities of various cloud services without thinking about the day-to-day workflow of data scientists and data engineers. Failing to make production data accessible in the cloud.

Data Engineering 145

Data Engineering Data Engineering Data Engineering Data Engineer 145

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Smart Data Collective

OCTOBER 27, 2021

Every organization needs data to make many decisions. The data is ever-increasing, and getting the deepest analytics about their business activities requires technical tools, analysts, and data scientists to explore and gain insight from large data sets. Google BigQuery.

Analytics 130

Analytics Analytics Data Warehouse Business Intelligence 130

AWS Machine Learning Blog

FEBRUARY 23, 2023

Amazon SageMaker Ground Truth is a fully managed data labeling service that provides flexibility to build and manage custom workflows. With Ground Truth, you can label image, video, and point cloud data for object detection, object tracking, and semantic segmentation tasks.

AWS 130

AWS ML ML Machine Learning 130

Smart Data Collective

OCTOBER 17, 2022

If you haven’t already, moving to the cloud can be a realistic alternative. Cloud data warehouses provide various advantages, including the ability to be more scalable and elastic than conventional warehouses. Can’t get to the data. Data pipeline maintenance. However, there are ways to get around this.

Big Data 119

Big Data Big Data Data Engineering Data Engineering 119

Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. Data engineers want to catalog data pipelines.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

ML 121

ML ML AWS Data Warehouse 121

Alation

APRIL 4, 2023

Integrating helpful metadata into user workflows gives all people, from data scientists to analysts , the context they need to use data more effectively. The Benefits and Challenges of the Modern Data Stack Why are such integrations needed? Before a data user leverages any data set, they need to be able to learn about it.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineering 52

Alation

JANUARY 17, 2023

These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Precisely

APRIL 3, 2023

Two decades ago, powerful business intelligence systems relied on on-premise data warehouses, usually fed by overnight batch ETL processes that took multiple hours to complete. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Analytics Analytics Database 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. Migrations from legacy on-prem systems to cloud data platforms like Snowflake and Redshift. This is where AI truly shines.

AI 52

AI AI Data Pipeline ML 52

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities. Savings may vary depending on configurations, workloads and vendor.

AI 74

AI AI Machine Learning Machine Learning 74

phData

JULY 18, 2024

However, creating a computer vision AI requires data scientists to train models for months before they can give results, right? AI can be trained to determine even the most subtle defects in products while being available 24 hours a day, seven days a week.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Data Engineering 52

Dataconomy

MAY 14, 2025

Simply put, AI-ready data is structured, high-quality information that can be easily used to train machine learning models and run AI applications with minimal engineering effort . Equally important, first-party behavioral data is a source of competitive advantage for brands building AI agents. in a query-ready form.

AI 70

AI AI Data Warehouse Data Pipeline 70

AWS Machine Learning Blog

APRIL 12, 2023

The PdMS includes AWS services to securely manage the lifecycle of edge compute devices and BHS assets, cloud data ingestion, storage, machine learning (ML) inference models, and business logic to power proactive equipment maintenance in the cloud. Outside of work, Fauzan enjoys spending time in nature.

AWS 117

AWS ML ML Machine Learning 117

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And so that’s where we got started as a cloud data warehouse.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And so that’s where we got started as a cloud data warehouse.

SQL 52

SQL ML ML Python 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Furthermore, a shared-data approach stems from this efficient combination. What will You Attain with Snowflake?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

FEBRUARY 7, 2024

The Snowflake Data Cloud is a leading cloud data platform that provides various features and services for data storage, processing, and analysis. A new feature that Snowflake offers is called Snowpark, which provides an intuitive library for querying and processing data at scale in Snowflake.

Python 52

Python ML ML SQL 52

phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Alation

APRIL 14, 2023

Last week, the Alation team had the privilege of joining IT professionals, business leaders, and data analysts and scientists for the Modern Data Stack Conference in San Francisco. In “The modern data stack is dead, long live the modern data stack!” Cloud costs are growing prohibitive. Let’s dive in!

Data Pipeline 52

Data Pipeline Data Warehouse Cloud Data ETL 52

phData

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52