Lyft Engineering

OCTOBER 3, 2023

High-quality data is necessary for the success of every data-driven company. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale.

Big Data 86

Big Data Metadata Data Warehouse Data 86

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. Table of Contents What Does an AI Data Quality Analyst Do?

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

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. Different perspectives can often shed light on elusive issues.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Databand.ai

JUNE 14, 2023

Ryan Yackel June 14, 2023 Understanding Data Quality Testing Data quality testing refers to the evaluation and validation of a dataset’s accuracy, consistency, completeness, and reliability. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

Amazon Web Services 40

Amazon Web Services Datasets High Quality Data ETL Tools 40

Monte Carlo

DECEMBER 19, 2022

Here are the 7 must-have checks to improve data quality and ensure reliability for your most critical assets. Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. million per year.

High Quality Data 52

High Quality Data Data SQL Bytes 52

Databand.ai

JULY 5, 2023

Improved Collaboration Among Teams Data engineering teams frequently collaborate with other departments, such as analysts or scientists, who depend on accurate datasets for their tasks. Boosting Operational Efficiency A well-monitored data pipeline can significantly increase an organization’s operational efficiency.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Monte Carlo

AUGUST 9, 2023

Too much data Too much data might not sound like a problem (it is called big data afterall), but when rows populate out of proportion, it can slow model performance and increase compute costs. Volume tests It’s important to identify data volume changes as quickly as possible.

Finance 52

Finance High Quality Data SQL Data 52