Big Data Engineering VS Data Warehousing
- AI Big Data Cassandra DW Engineer ML MongoDB News Python SQL
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
- Deploying data mining and implementing various machine learning (ML) models
- Delivering data modeling and ETL practices
- Architecting the infrastructure for Big Data lakes
- Maintaining and optimizing the distributed infrastructure and solutions.
The main job functions involve such activities:
- Design, construction, tests, maintenance and optimization of cloud infrastructure for running large-scale data processing systems
- Deployment of various algorithms for normalization of disparate data to leverage various types of Big Data analytics
- Assistance to Data Scientists in implementing various ML models in order to allow the AI to process the data and discover hidden patterns and anomalies.
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
- Delivering data modeling and ETL practices
- Deploying and maintaining Business Intelligence (BI) systems
Below are the mainly expected job activities:
- Support for developers, data analysts and data scientists who need to interact with DW/BI systems
- Configuration and maintenance of the DW/BI infrastructure
- Data integrity checks
- System performance optimization
- DW storage capacity and reliability management
Data warehousing is an established practice of data storage and processing to enable the usage by BI 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?
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