Data Observability, Data Quality and Data Warehouse

Data Observability

Data Quality

Data Warehouse

Data Integrity vs. Data Quality: How Are They Different?

Precisely

JULY 12, 2024

When companies work with data that is untrustworthy for any reason, it can result in incorrect insights, skewed analysis, and reckless recommendations to become data integrity vs data quality. Two terms can be used to describe the condition of data: data integrity and data quality.

Data Quality

Data Quality Data Governance Database Analytics

Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality

Data Quality Data Governance ETL Data Observability

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How to Achieve High-Accuracy Results When Using LLMs

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Data Trustability: The Bridge Between Data Quality and Data Observability

Dataversity

AUGUST 15, 2022

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.

Data Observability

Data Observability Data Quality Data Lakes Data Warehouse

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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

DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

Data Quality

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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. IBM Databand underpins this set of capabilities with data observability for pipeline monitoring and issue remediation.

Data Silos

Data Silos Data Pipeline DataOps Business Intelligence

Testing and Monitoring Data Pipelines: Part One

Dataversity

MAY 26, 2023

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

Data Pipeline

Data Pipeline Data Warehouse Data Quality Data Observability

AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

JULY 3, 2024

This includes integration with your data warehouse engines, which now must balance real-time data processing and decision-making with cost-effective object storage, open source technologies and a shared metadata layer to share data seamlessly with your data lakehouse.

AI AI Data Quality Database

Five benefits of a data catalog

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Data Quality

Data Quality Data Governance Data Scientist Data Wrangling

Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Data Acquisition: Extracting data from source systems and making it accessible. as well as calculating business keys.

SQL

SQL Data Observability Data Quality Data Pipeline

Top ETL Tools: Unveiling the Best Solutions for Data Integration

Pickl AI

JUNE 7, 2024

Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Introduction In today’s business landscape, data integration is vital. Also Read: Top 10 Data Science tools for 2024. What is ETL?

ETL

ETL Data Pipeline Data Quality Data Warehouse

Modern Data Management Essentials: Exploring Data Fabric

Precisely

JULY 18, 2024

Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

Data Lakes

Data Lakes Data Warehouse Data Governance Machine Learning

Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

JULY 8, 2024

NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data. Data Warehouses : Centralised repositories optimised for analytics and reporting. Data Lakes : Scalable storage for raw and processed data, supporting diverse data types.

Data Pipeline

Data Pipeline Data Quality Database Apache Kafka

Mainframe Data: Empowering Democratized Cloud Analytics

Precisely

OCTOBER 16, 2023

The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and data warehouses have been replaced by modern data lakes and cloud analytics solutions. Secure data exchange takes on much greater importance.

Analytics

Analytics Analytics Big Data Analytics Big Data Analytics

Best Data Engineering Tools Every Engineer Should Know

Pickl AI

MARCH 19, 2025

It helps data engineers collect, store, and process streams of records in a fault-tolerant way, making it crucial for building reliable data pipelines. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that enables fast query execution for large datasets.

Data Engineering

Data Engineering Data Engineering Data Engineer Data Engineering

Data Science Current

Data Integrity vs. Data Quality: How Are They Different?

Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Webinars

Trending Sources

Data Trustability: The Bridge Between Data Quality and Data Observability

Webinars

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

Supercharge your data strategy: Integrate and innovate today leveraging data integration

Testing and Monitoring Data Pipelines: Part One

AI that’s ready for business starts with data that’s ready for AI

Five benefits of a data catalog

Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

Top ETL Tools: Unveiling the Best Solutions for Data Integration

Modern Data Management Essentials: Exploring Data Fabric

Build Data Pipelines: Comprehensive Step-by-Step Guide

Mainframe Data: Empowering Democratized Cloud Analytics

Best Data Engineering Tools Every Engineer Should Know

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