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Data Observability, Essential for your Modern Data Stack

insideBIGDATA

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.

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Sky’s the Limit: Learn how JetBlue uses Monte Carlo and Snowflake to build trust in data and improve model accuracy

KDnuggets

Join JetBlue on 12/8 10AM PT to learn how their data engineering team achieves end-to-end coverage in their Snowflake data warehouse with the power of Monte Carlo and data observability.

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10 Data Engineering Topics and Trends You Need to Know in 2024

ODSC - Open Data Science

Now that we’re in 2024, it’s important to remember that data engineering is a critical discipline for any organization that wants to make the most of its data. These data professionals are responsible for building and maintaining the infrastructure that allows organizations to collect, store, process, and analyze data.

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2025’s Game-Changers: The Future of Data Engineering Unveiled

Dataversity

To remain competitive, organizations must embrace cutting-edge technologies and trends that optimize how data is engineered, processed, and utilized.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

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!

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Data observability: The missing piece in your data integration puzzle

IBM Journey to AI blog

Historically, data engineers have often prioritized building data pipelines over comprehensive monitoring and alerting. Delivering projects on time and within budget often took precedence over long-term data health. Better data observability unveils the bigger picture.

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Data Observability Tools and Its Key Applications

Pickl AI

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.