datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.
Octopai
MAY 15, 2023
How can you save your organizational data management and hosting cost using automated data lineage. Do you think you did everything already to save organizational data management costs? What kind of costs organization has that data lineage can help with? Well, you probably haven’t done this yet!
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erwin
MARCH 25, 2021
erwin recently hosted the second in its six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, the second webinar focused on “ The Value of Data Governance & How to Quantify It.”.
DataKitchen
FEBRUARY 21, 2023
Overview of Gartner’s data engineering enhancements article To set the stage for Gartner’s recommendations, let’s give an example of a new Data Engineering Manager, Marcus, who faces a whole host of challenges to succeed in his new role: Marcus has a problem. Summary: 10x your data engineering game.
FineReport
APRIL 26, 2023
MongoDB MongoDB, which gained popularity in 2010, is a NoSQL document-oriented database platform that is free and open-source. It uses collections and documents to store a high volume of data, with documents consisting of key-value pairs as the basic unit. It supports master-slave replication for data backup.
Domino Data Lab
OCTOBER 1, 2020
Finally, if the data scientist was not allowed to see certain columns, rows, or cells within the CSV file, there would be no way to give access to the file. The identity of the current user in Domino Data Lab is automatically and transparently propagated to Okera, with all the requisite fine-grained access control policies applied.
O'Reilly on Data
DECEMBER 12, 2019
Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].
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