Remove Data Transformation Remove Interactive Remove Metrics Remove Testing
article thumbnail

The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

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. 10) Data Quality Solutions: Key Attributes.

article thumbnail

Simplify Metrics on Apache Druid With Rill Data and Cloudera

Cloudera

Co-author: Mike Godwin, Head of Marketing, Rill Data. Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Cloudera Data Warehouse).

Metrics 84
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Navigating the Chaos of Unruly Data: Solutions for Data Teams

DataKitchen

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

article thumbnail

Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

Allows them to iteratively develop processing logic and test with as little overhead as possible. Plays nice with existing CI/CD processes to promote a data pipeline to production. Provides monitoring, alerting, and troubleshooting for production data pipelines.

Testing 81
article thumbnail

Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

article thumbnail

AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. They have different metrics for judging whether some content is interesting or not. This is one of the main diagnostic tests.

article thumbnail

Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

In this post, we discuss ways to modernize your legacy, on-premises, real-time analytics architecture to build serverless data analytics solutions on AWS using Amazon Managed Service for Apache Flink. Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities.