Precisely

JANUARY 19, 2023

Yet mainframes weren’t designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Smart Data Collective

JUNE 20, 2022

The recommendation is to bring a minimal amount of data, development environments, and automation tools to the initial cloud environment, then introduce users and iterate based on their needs. Failing to make production data accessible in the cloud.

Data Engineering 136

Data Engineering Data Engineering Data Engineer Data Engineering 136

Alation

JANUARY 17, 2023

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? The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Precisely

JANUARY 18, 2024

Yet mainframes weren’t initially designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Artificial Intelligence Artificial Intelligence Cloud Computing 52

phData

OCTOBER 12, 2023

When the data or pipeline configuration needs to be changed, tools like Fivetran and dbt reduce the time required to make the change, and increase the confidence your team can have around the change. These allow you to scale your pipelines quickly. Governance doesn’t have to be scary or preventative to your cloud data warehouse.

Data Warehouse 52

Data Warehouse Database Cloud Data Data Pipeline 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59