François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. How do we build data products ? How can we interoperate between the data domains ? As you can see, this is in the code part where you are building your data pipelines, a misnomer because this is an over simplification.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Databand.ai

AUGUST 30, 2023

DataOps practices help organizations establish robust data governance policies and procedures, ensuring that data is consistently validated, cleansed, and transformed to meet the needs of various stakeholders. One key aspect of data orchestration is the automation of data pipeline tasks.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Knowledge Hut

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Meltano

FEBRUARY 22, 2023

Testing a data application is similar to testing any software application in many ways, just with a strong focus on testing data-related issues. But testing problems like failing data workflows, mismatches in data reconciliation after ETL, and data quality issues means that you’re not only testing the code but also the data itself.

Data 52

Data Transportation Data Workflow Data Lake 52

Cloudera

AUGUST 21, 2020

Security needs to be treated at a mission-critical level and data security also needs to be a core part of a business’s strategic approach. The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from.

Banking 60

Banking Kafka Cloud Storage Government 60

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40