DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. This is a very good thing.

Data Governance 52

Data Governance Data Quality Data Analyst Data Pipeline 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

IBM Journey to AI blog

JULY 1, 2024

Whenever anyone talks about data lineage and how to achieve it, the spotlight tends to shine on automation. This is expected, as automating the process of calculating and establishing lineage is crucial to understanding and maintaining a trustworthy system of data pipelines.

ETL 40

ETL Data Lakes Database Data Pipeline 40

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. Narrow the scope It’s tempting to mark huge swaths of data as critical.

Data Governance 52

Data Governance Data Engineering Data Engineering Data Engineer 52

Mlearning.ai

JULY 10, 2023

In case of complex data pipelines, a combination of Materialized Views, Stored Procedures, and Scheduled Queries could be a better choice than to solely rely on Scheduled Queries by itself. This allows you to use tools like BigQuery to query the data before it’s migrated to a native BigQuery table.

SQL 52

SQL Database Database Administration Data Lakes 52

Snorkel AI

MARCH 2, 2023

There are definitely compelling economic reasons for us to enter into this realm. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

There are definitely compelling economic reasons for us to enter into this realm. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

phData

SEPTEMBER 27, 2024

All this raw data goes into your persistent stage. Then, if you later refine your definition of what constitutes an “engaged” customer, having the raw data in persistent staging allows for easy reprocessing of historical data with the new logic. Your customer data game will never be the same.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52