Svg Vector Icons : http://www.onlinewebfonts.com/icon More Trending Articles

Data Science vs. Data Engineering

data engineering - data engineering training

The business world has an insatiable appetite for Big Data. The IDC predicts that worldwide data creating will reach 163 zettabytes by 2025, 10 times the amount produced in 2017.

Career opportunities in Big Data continue to grow and evolve. New functions and specialties arise, and specialized positions emerge.

Both data science and data engineering pursue truth and insights through accurate data analysis. Their roles in achieving successful results separate them. Yet when examining data science vs. data engineering, it's important to understand the overlap as well.

Data Science vs. Data Engineering: Similarities and Differences

Data engineering sets the table for data science. A data engineer lays the groundwork so that data scientists can work their craft.

Data engineering still requires an expertise of database systems and data modeling, as well as data warehouse solutions.

DATA ENGINEERING RESPONSIBILITIES

A data engineer's skill set includes:

  • Architecture development, building, and maintenance
  • Data acquisition, including gathering, cleaning, and ingesting data
  • Implementing data pipelines via the ETL (Extract, Transform, and Load) model
  • Data wrangling and employing a variety of Big Data tools to improve data reliability and quality
  • Development and oversight of data modeling and data mining
  • Establishing the process for delivery of data to the data science team

As you can see, data engineering impacts the data earlier in the data analysis process. Without clean, accurate, and formatted data, the data science cannot begin.

Serving in a complementary capacity, a data warehouse architect may work along side a data engineer in architecture strategy. As is the case with many data careers, specifics are constantly evolving to keep pace with technology.

DATA SCIENCE RESPONSIBILITIES

A data scientist's skill set includes:

  • Organization of Big Data
  • Conducting industry-relevant research through large amounts of data
  • Analysis of data to find actionable insights
  • Building predictive models through AI and machine learning
  • Present findings through data visualization, often generated through BI tools

ROAD MAPS TO DATA CAREERS

The data science vs. data engineering paradigm takes root in the same place. The broad field of Computer Science unites all parties in question. From that jumping-off point, the paths diverge.

Data scientists' studies focus more on math and statistics, while data engineers -- as the name suggests -- are likely to have more experience in engineering, particularly computer engineering.

Data science includes the study of machine learning. In the case of data science vs. machine learning, it's widely agreed upon today that ML exists under the umbrella of data science.

Data engineer training will also include gaining knowledge of database systems. Data engineers will have more experience in data storage and are more likely to have data warehouse certifications.

SNOWFLAKE FOR DATA SCIENCE AND DATA ENGINEERING

Snowflake caters to data-driven enterprises with its zero-management data-warehouse-as-a-service (DWaaS) in the Data Cloud. Built for the cloud, Snowflake provides speed and flexibility that can't be found in traditional data warehouses.

Virtual Hands-On Labs

Get up to speed through this hands-on workshop that focuses on increasing your efficiency, scaling to your needs and analyzing your data thoroughly. Learn how to set up a data warehouse and reveal the insights your business needs.

Find a workshop near you or online.