The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. 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.

ETL 59

ETL Data Pipeline ML ML 59

Precisely

JANUARY 9, 2025

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. Just like in the data warehouse journey, the quality and consistency of the data flowing through Hadoop became a massive barrier to adoption.

Data Warehouse 119

Data Warehouse Hadoop Data Lakes Data Governance 119

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

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 Engineer Data Engineering 91

phData

SEPTEMBER 28, 2023

Power BI Dataflows provide vital functionalities that effectively empower users to cleanse and reshape data from various sources. These Dataflows are crucial in fostering consistency and reducing the duplication of repetitive ETL (Extract, Transform, Load) steps, achieved by reusing transformations.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78