Alation

MARCH 22, 2022

Accenture calls it the Intelligent Data Foundation (IDF), and it’s used by dozens of enterprises with very complex data landscapes and analytic requirements. Simply put, IDF standardizes data engineering processes. IDF works natively on cloud platforms like AWS. How the IDF Supports a Smarter Data Pipeline.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. What is an ETL data pipeline in ML?

ETL 59

ETL Data Pipeline ML ML 59

phData

FEBRUARY 14, 2023

Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.). Streamsets Data Collector StreamSets Data Collector Engine is an easy-to-use data pipeline engine for streaming, CDC, and batch ingestion from any source to any destination. The biggest reason is the ease of use.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Furthermore, a shared-data approach stems from this efficient combination. What will You Attain with Snowflake?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. He has a record of working with distributed teams across the globe within large enterprises.

ML 95

ML ML AWS AI 95

Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. Use with caution, and test before committing to using them.

Clustering 98

Clustering Database SQL Data Pipeline 98