Data Governance, Data Pipeline and Data Profiling

Data Profiling: What It Is and How to Perfect It

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

Data Profiling Data Quality Data Governance Data Pipeline

Unfolding the difference between Data Observability and Data Quality

Pickl AI

OCTOBER 10, 2023

In today’s fast-paced business environment, the significance of Data Observability cannot be overstated. Data Observability enables organizations to detect anomalies, troubleshoot issues, and maintain data pipelines effectively. Quality Data quality is about the reliability and accuracy of your data.

Data Observability

Data Observability Data Quality Data Governance Data Pipeline

Data Observability Tools and Its Key Applications

Pickl AI

OCTOBER 11, 2023

What is Data Observability? It is the practice of monitoring, tracking, and ensuring data quality, reliability, and performance as it moves through an organization’s data pipelines and systems. Data quality tools help maintain high data quality standards. Tools Used in Data Observability?

Data Observability

Data Observability Data Quality Data Pipeline Data Governance

Webinars

Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Marketing Operations in 2025: A New Framework for Success

MORE WEBINARS

What Orchestration Tools Help Data Engineers in Snowflake

phData

AUGUST 17, 2023

Data pipeline orchestration tools are designed to automate and manage the execution of data pipelines. These tools help streamline and schedule data movement and processing tasks, ensuring efficient and reliable data flow. This enhances the reliability and resilience of the data pipeline.

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

How data engineers tame Big Data?

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data

Big Data Big Data Data Engineering Data Engineer

Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Learn more about designing the right data architecture to elevate your data quality here.

Data Quality

Data Quality Data Profiling Data Governance Machine Learning

Data Quality Framework: What It Is, Components, and Implementation

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. If any of these is missing, the client data is considered incomplete.

Data Quality

Data Quality Data Governance Machine Learning Machine Learning

Alation & Bigeye: A Potent Partnership for Data Quality

Alation

DECEMBER 7, 2021

Data teams use Bigeye’s data observability platform to detect data quality issues and ensure reliable data pipelines. If there is an issue with the data or data pipeline, the data team is immediately alerted, enabling them to proactively address the issue.

Data Quality

Data Quality Data Observability Data Pipeline Data Profiling

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

What does a modern data architecture do for your business? A modern data architecture like Data Mesh and Data Fabric aims to easily connect new data sources and accelerate development of use case specific data pipelines across on-premises, hybrid and multicloud environments.

Data Quality

Data Quality Data Lakes Data Warehouse Business Intelligence

Data Quality in Machine Learning

Pickl AI

JULY 24, 2024

Key Components of Data Quality Assessment Ensuring data quality is a critical step in building robust and reliable Machine Learning models. It involves a comprehensive evaluation of data to identify potential issues and take corrective actions. Conduct thorough data quality assessments to identify and prioritise issues.

Data Quality

Data Quality Machine Learning Machine Learning Data Profiling

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. LakeFS facilitates data reproducibility, collaboration, and data governance within the data lake environment.

Machine Learning

Machine Learning Machine Learning ML ML

phData Toolkit June 2023 Update

phData

JUNE 26, 2023

By programmatically performing the translation, you can focus your efforts on defining information architecture, implementing more data governance, and deriving business value faster. If you were to try and translate thousands of SQL statements manually, it would be tedious, expensive, and error-prone.

SQL

SQL Data Profiling Data Pipeline Data Governance

phData Toolkit December 2022 Update

phData

DECEMBER 29, 2022

The phData Toolkit continues to have additions made to it as we work with customers to accelerate their migrations , build a data governance practice , and ensure quality data products are built. Some of the major improvements that have been made are within the data profiling and validation components of the Toolkit CLI.

SQL

SQL Database Database Administration Data Profiling

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning

Machine Learning Machine Learning ML ML

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning

Machine Learning Machine Learning ML ML

Data Science Current

Data Profiling: What It Is and How to Perfect It

Unfolding the difference between Data Observability and Data Quality

Webinars

Trending Sources

Data Observability Tools and Its Key Applications

Webinars

What Orchestration Tools Help Data Engineers in Snowflake

How data engineers tame Big Data?

Data integrity vs. data quality: Is there a difference?

Data Quality Framework: What It Is, Components, and Implementation

Alation & Bigeye: A Potent Partnership for Data Quality

Data architecture strategy for data quality

Data Quality in Machine Learning

MLOps Landscape in 2023: Top Tools and Platforms

phData Toolkit June 2023 Update

phData Toolkit December 2022 Update

Capital One’s data-centric solutions to banking business challenges

Capital One’s data-centric solutions to banking business challenges

Stay Connected