Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. By employing these tools, teams can proactively detect issues before they become larger problems that affect business operations.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Monte Carlo

OCTOBER 5, 2023

Ensure data quality Even if there are no errors during the ETL process, you still have to make sure the data meets quality standards. High-quality data is crucial for accurate analysis and informed decision-making. Your data pipelines will thank you.

Data Cleanse 52

Data Cleanse ETL Tools Datasets Utilities 52

Monte Carlo

OCTOBER 10, 2024

As the use of AI becomes more ubiquitous across data organizations and beyond, data quality rises in importance right alongside it. After all, you can’t have high-quality AI models without high-quality data feeding them. Data Validation Tools : Great Expectations, Apache Griffin.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Monte Carlo

DECEMBER 19, 2022

When it comes to data engineering, quality issues are a fact of life. Like all software and data applications, ETL/ELT systems are prone to failure from time-to-time. Among other factors, data pipelines are reliable if: The data is current, accurate, and complete. These are your unknown unknowns.

High Quality Data 52

High Quality Data Data SQL Bytes 52

Monte Carlo

AUGUST 9, 2023

Bad data in—bad data products out. And that puts data quality at the top of every CTO’s priority list. In this post, we’ll look at 8 of the most common data quality issues affecting data pipelines, how they happen, and what you can do to find and resolve them. As in all things, it depends.

Finance 52

Finance SQL High Quality Data Data 52

Monte Carlo

AUGUST 8, 2023

By applying rules and checks, data validation testing verifies the data meets predefined standards and business requirements to help prevent data quality issues and data downtime. From this perspective, the data validation process looks a lot like any other DataOps process.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

ProjectPro

FEBRUARY 21, 2023

Google Cloud Certified Professional Data Engineer Certifications An individual is fit for taking the GCP Data Engineering certification exam if he/she- Has more than three years of prior data engineering experience, including at least one year of solution design and management using Google Cloud.

Certification 52

Certification Data Engineering Data Engineer Engineering 52