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Building Data Pipelines On-premises or In-the-cloud

(The University of Chicago - Alvin Wei-Cheng Wong)

- Overview

Data pipelines can be built in-house or in the cloud. In-house pipelines give you more control over every aspect of the pipeline. However, cloud pipelines are more flexible and elastic, which can help organizations. 

Here are some benefits of building a data pipeline in the cloud: 

  • Scalability: Cloud pipelines can automatically scale storage and compute.
  • Efficiency: Cloud pipelines can help companies build and manage workloads more efficiently.
  • Digital transformation: Cloud pipelines can help organizations rapidly move their data and analytics infrastructure to the cloud.

Building elastic cloud-native data pipelines can help organizations quickly move their data and analytics infrastructure to the cloud and accelerate digital transformation. Deploying data pipelines in the cloud can help companies build and manage workloads more efficiently.


- Modern Data Pipelines and ETL

Data pipelines and ETL (extract, transform, load) are both responsible for transferring data between sources and storage solutions. However, they work in different ways: 

  • Data pipelines: Work with ongoing data streams in real time.
  • ETL: Focuses more on individual “batches” of data for more specific purposes.

ETL pipelines involve: 

  • Extracting data from multiple sources like transaction databases, APIs, or other business systems.
  • Transforming it.
  • Loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

ETL pipelines are critical for data-driven organizations. They save data teams time and effort by eliminating errors, bottlenecks, and latency. There has been a shift from traditional ETL to ELT (extract, load, transform) in modern data pipelines. 


- ETL Tools

ETL tools that work with in-house data warehouses do as much prep work as possible, including transformation, prior to loading data into data warehouses. Today, however, cloud data warehouses like Amazon Redshift, Google BigQuery, Azure SQL Data Warehouse, and Snowflake can scale up and down in seconds or minutes, so developers can replicate raw data from disparate sources and define transformations in SQL and run them in the data warehouse after loading or at query time.



[More to come ...]

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