Ascend.io

OCTOBER 28, 2024

It is important to note that normalization often overlaps with the data cleaning process, as it helps to ensure consistency in data formats, particularly when dealing with different sources or inconsistent units. Data Validation Data validation ensures that the data meets specific criteria before processing.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Data Engineering Weekly

FEBRUARY 2, 2025

Shifting left involves moving data processing upstream, closer to the source, enabling broader access to high-quality data through well-defined data products and contracts, thus reducing duplication, enhancing data integrity, and bridging the gap between operational and analytical data domains.

Data Engineering 57

Data Engineering Data Engineer Engineering Data Lake 57

Edureka

APRIL 22, 2025

Data Quality and Governance In 2025, there will also be more attention paid to data quality and control. Companies now know that bad data quality leads to bad analytics and, ultimately, bad business strategies. Companies all over the world will keep checking that they are following global data security rules like GDPR.

Data Engineering 52

Data Engineering Data Engineer Engineering Consulting 52

Data Engineering Podcast

MAY 26, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

Systems 130

Systems Data Lake High Quality Data Google Cloud 130

Precisely

APRIL 7, 2025

Transformations: Know if there are changes made to the data upstream (e.g., If you dont know what transformations have been made to the data, Id suggest you not use it. Data validation and verification: Regularly validate both input data and the appended/enriched data to identify and correct inaccuracies before they impact decisions.

Retail 75

Retail Datasets Data Portfolio 75

Databand.ai

AUGUST 30, 2023

By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes. DataOps tools can be categorized into several types, including data integration tools, data quality tools, data catalog tools, data orchestration tools, and data monitoring tools.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121