Remove Accessibility Remove Data Consolidation Remove Definition Remove Unstructured Data
article thumbnail

Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The transformed data is then placed into the destination data warehouse or data lake.

article thumbnail

ETL vs. ELT and the Evolution of Data Integration Techniques

Ascend.io

How ETL Became Outdated The ETL process (extract, transform, and load) is a data consolidation technique in which data is extracted from one source, transformed, and then loaded into a target destination. But in a world that favors the here and now, ETL processes lack in the area of providing analysts with new, fresh data.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Data Warehousing Guide: Fundamentals & Key Concepts

Monte Carlo

Finally, where and how the data pipeline broke isn’t always obvious. Monte Carlo solves these problems with our our data observability platform that uses machine learning to help detect, resolve and prevent bad data. Data can be loaded in batches or can be streamed in near real-time.

article thumbnail

Data Virtualization: Process, Components, Benefits, and Available Tools

AltexSoft

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69