Big Data Engineering VS Data Warehousing

Vladimir Fedak
Oct 5, 2017 · 3 min read

While to many businesses these components of Big Data operations seem interchangeable, if not fully the same, Big Data engineering actually differs quite a lot from data warehousing.

Simply put, these are like the adjuster and the tester employed in the assembly shop of a factory. Both need to produce the aggregates, both understand their structure, but the former’s task is to create a whole from disparate parts, while the latter must make this whole thing work as intended. We will go through the difference between them in more details below.

There are quite a few fundamental distinctions between these two components of the dataops. These distinctions can be best tracked through the skill requirements for varying Big Data Engineer or Data Warehouse Engineer positions on job boards like Indeed or Glassdoor.

Job requirements for a Big Data Engineer

Below are the main skills expected from a Big Data engineer:

  • Big Data processing using NoSQL databases

The main job functions involve such activities:

  • Design, construction, tests, maintenance and optimization of cloud infrastructure for running large-scale data processing systems

The aforementioned skills and responsibilities require working with Big Data tools, languages and databases like Hadoop, MongoDB, Redis, Cassandra, Spark, Python, R, and others. Big Data Engineer works with so-called data lakes, namely huge storages and incoming streams of unstructured data. The system architecture is flat, meaning all the images, texts and documents along with the other types of data are stored without any hierarchy to enable swift normalization.This ensures easy adaptation to any requirements of highly configurable machine learning algorithms.

Job requirements for a Data Warehouse Engineer

These are the knowledge and skills needed for a Data Warehouse (DW) Engineer:

  • Conducting data warehousing using SQL databases

Below are the mainly expected job activities:

  • Support for developers, data analysts and data scientists who need to interact with DW/BI systems

Data warehousing is an established practice of data storage and processing to enable the usage byBI systems. Said systems utilize hierarchical architecture of data, requiring a significant amount of effort to structure the data and limit the possible data sources. Their main end users are business executives that can discover important insights from massive arrays of processed data. This is a proven method for ensuring more feasible usage of available data.


While data warehousing is a widely adopted practice, it is really a niche-specific approach, limited to a certain type of data input. Despite boasting a mature security and established workflow, this trend of data analysis is stagnating and will most likely go out of usage in the future.

Quite oppositely, Big Data engineering using machine learning is the uptrend nowadays. As we have already described, AI becomes the wisest way to do business nowadays. More and more companies begin to deploy highly flexible machine learning algorithms in an effort to outperform their competition by providing highly personalized offers.

Therefore, in comparison of Big Data engineering vs data warehousing, we adhere to preferring the former over the latter, as we consider Big Data engineering to be the future for a dynamically evolving AI-first IT industry. What do you think?

Originally published at

Vladimir Fedak

Written by

CEO of IT Svit since 2005 and don't wanna stop | DevOps & Big Data specialist