Question 1

How do data engineering services differ from data science services?

Accepted Answer

Data engineering services are responsible for the construction, optimization and management of data pipelines and their infrastructure. In contrast, data science services are focused on utilizing the outputs of these data pipelines to access data and derive insights using statistics or advanced machine learning algorithms. In short, data engineering involves the development and maintenance of the infrastructure that handles data, while data science is the process of extracting insights and knowledge from that data.

Question 2

How can data engineering improve business decision-making?

Accepted Answer

Data engineering elevates decision-making by enabling businesses to transform raw data into a valuable asset. For example, data engineering can help identify areas for operational improvements, thus optimizing processes and leading to increased efficiency and cost savings. Data engineering can also prepare your data streams to be safely used to train and execute tailored large language models. Another critical advantage of data engineering is its ability to assist businesses in streamlining their data processing workflows. By utilizing the right data engineering tools and techniques, companies can automate numerous tasks in processing and preparing data for analysis.

Question 3

How do data engineering services ensure data quality and reliability?

Accepted Answer

Data engineering elevates decision-making by enabling businesses to transform raw data into a valuable asset. For example, data engineering can help identify areas for operational improvements, thus optimizing processes and leading to increased efficiency and cost savings. Data engineering can also prepare your data streams to be safely used to train and execute tailored large language models. Another critical advantage of data engineering is its ability to assist businesses in streamlining their data processing workflows. By utilizing the right data engineering tools and techniques, companies can automate numerous tasks in processing and preparing data for analysis.

Question 4

What are common challenges in data engineering projects?

Accepted Answer

Data engineering projects often face challenges related to data processing, including stages such as data ingestion, integration, validation and storage. Several factors can pose challenges, from analyzing and categorizing incoming data to integrating existing and new data, as well as scaling data storage to accommodate increased volume (scalability is the key factor here) and ensure data accessibility. It's also important to note that data engineering projects must comply with data privacy regulations and implement robust security measures to safeguard sensitive information.

Question 5

What is relation between AI and data engineering?

Accepted Answer

- There is no AI without data engineering;
- Even pre-trained AIs like ChatGPT need to be fine tuned using your comapny data;
- AI needs to be trained on data;
- Data needs to be curated so sensitive info do not leak out thorug AI (Samsung case);