Data Process Data Warehouse Pipeline-centric 
Edureka
APRIL 22, 2025
Fabric is meant for organizations looking for a single pane of glass across their data estate with seamless integration and a low learning curve for Microsoft users. Snowflake is a cloud-native platform for data warehouses that prioritizes collaboration, scalability, and performance. Office 365, Power BI, Azure).
DataKitchen
NOVEMBER 4, 2021
The typical pharmaceutical organization faces many challenges which slow down the data team: Raw, barely integrated data sets require engineers to perform manual , repetitive, error-prone work to create analyst-ready data sets. Cloud computing has made it much easier to integrate data sets, but that’s only the beginning.
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Maxime Beauchemin
JANUARY 20, 2017
The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.
Confluent
SEPTEMBER 25, 2019
Of course, this is not to imply that companies will become only software (there are still plenty of people in even the most software-centric companies), just that the full scope of the business is captured in an integrated software defined process. Here, the bank loan business division has essentially become software.
Knowledge Hut
DECEMBER 26, 2023
Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Let us now understand the basic responsibilities of a Data engineer.
Dataquest
JANUARY 25, 2017
A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use.
Ascend.io
MAY 31, 2023
In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.
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