AltexSoft

FEBRUARY 11, 2023

If KPI goals are not met, a data architect recommends solutions (including new technologies) to improve the existing framework. Besides, it’s up to this specialist to guarantee compliance with laws, regulations, and standards related to data.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Towards Data Science

JUNE 8, 2023

Specialists or generalists? We examine which team structures are the best suited for efficiently improving data quality. Sure, data quality is everyones’ problem. Data governance How Contentsquare approaches data quality as part of their data governance initiative. Engineer or analyst?

Data Governance 78

Data Governance Government Generalist Data Engineering 78

Knowledge Hut

SEPTEMBER 6, 2023

Problem-Solving : Strong problem-solving abilities are crucial for solving complex data challenges and efficient data processing. Data Security and Privacy : Considering the sensitivity of data handled in Big Data projects , it is crucial to have awareness of data security and privacy concerns.

Big Data 52

Big Data Certification Hadoop Scala 52

Grouparoo

OCTOBER 6, 2021

90 What to Do When You Don’t Get Any Credit To get credit, talk in terms of business value, not technology 91 When Our Data Science Team Didn’t Produce Value Balance long-term solutions with short-term needs 92 When to Avoid the Naive Approach Storage format and schema are good to get right from the beginning. 97 Your Data Tests Failed!

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Cloudera

JULY 26, 2021

Authorization: Define what users of internal / external organizations can access and do with the data in a fine-grained manner that ensures compliance with e.g., data obfuscation requirements introduced by industry and country specific standards for certain types of data assets such as PII.

Generalist 114

Generalist Telecommunication Healthcare Data 114

U-Next

AUGUST 17, 2022

Data Engineer roles and responsibilities have certain important components, such as: Refining the software development process using industry standards. Identifying and fixing data security flaws to shield the company from intrusions. Employing data integration technologies to get data from a single domain.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52