insideBIGDATA

AUGUST 22, 2023

In this contributed article, Mayank Mehra, head of product management at Modak, shares the importance of incorporating effective data observability practices to equip data and analytics leaders with essential insights into the health of their data stacks.

Data Observability 370

Data Observability Analytics Analytics Data Engineering 370

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Data Science Dojo

NOVEMBER 15, 2023

Here are nine of the top AI conferences happening in North America in 2023 and 2024 that you must attend: Top AI events and conferences in North America attend in 2023 Big Data and AI TORONTO 2023: Big Data and AI Toronto is the premier event for data professionals in Canada.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Data Science Dojo

OCTOBER 10, 2023

AI and Big Data Expo – North America (May 17-18, 2023): This technology event is for enterprise technology professionals interested in the latest AI and big data advances and tactics. However, in previous iterations of the summit, speakers have included prominent voices in data engineering and analytics.

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AI AI Data Observability Artificial Intelligence 243

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

IBM Journey to AI blog

DECEMBER 16, 2022

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134