Smart Data Collective

MAY 20, 2019

GDPR helped to spur the demand for prioritized data governance , and frankly, it happened so fast it left many companies scrambling to comply — even still some are fumbling with the idea. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. Cloud Computing and Related Mechanics.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Dataconomy

FEBRUARY 23, 2023

Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

IBM Journey to AI blog

JUNE 29, 2023

Data integration and automation To ensure seamless data integration, organizations need to invest in data integration and automation tools. These tools enable the extraction, transformation, and loading (ETL) of data from various sources.

Analytics 94

Analytics Analytics Business Intelligence Business Intelligence 94

Alation

JANUARY 17, 2023

Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? A Note on the Shift from ETL to ELT.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Precisely helps enterprises manage the integrity of their data.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59