Data engineering is one of the fastest growing job categories right now and hence you might wonder what is it all about? With the vast amounts of data generated by organizations per day its necessary to have people to process and channel that data to data analysts and machine learning engineers.
So what is data engineering?
Data engineering is the practice of collecting, designing, storing and analyzing data from various resources at scale.
The data engineering ecosystem consist of:
1. Data — work with different data types, formats and sources of data
2. Data stores and repositories — Relational and non-relational databases, data warehouses, data lakes and big data stores that store and process the data
3. Data pipelines — collect/gather data from various sources, clean, process and transform it to data that can be used for analysis
4. Analytics and data driven decision making — make well processed data for use in business analytics, visualization and data driven decision making.
Data engineers are the people responsible for ensuring data is in its highly usable state by the time it reaches data scientist and business analyst to interpret. This ensures that data is accessible for organizations to use to evaluate and optimize their performance.
Some common tasks you might perform when working with data are:
- Acquire datasets that align with business needs
- Develop algorithms to transform data into useful, actionable information
- Build, test, and maintain database pipeline architectures
- Collaborate with management to understand company objectives
- Create new data validation methods and data analysis tools
- Ensure compliance with data governance and security policies
You might be wondering what relationship do data engineers have with data scientist and data analyst
So data scientists and data analysts analyze data sets to get insights and knowledge while data engineers build systems for collecting, validating and preparing high quality data for use by data scientists to make better decisions in the business.
Skills required to become a data engineer.
For starters data engineers are required to possess cloud computing skills, coding skills and database design skills.
1. Coding — data engineers are at least required to have coding skills in the common programming languages used in data engineering i.e. SQL, NoSQL, python, java, R and Scala
2. Relational and non-relational databases — data engineers should be familiar with databases both relational and non-relational and how they work
3. Data storage — data types are not all stored in the same way hence data engineers will need to know which data type is suitable when
4. Automation and scripting: When working with Big data, automation becomes necessary because organizations are able to collect much information therefore as data engineer you should be able to write scripts to automate repetitive tasks
5. Cloud computing: As many companies are moving to cloud computing Data Engineers will need to understand cloud computing and cloud storage.
6. ETL (extract, transform, and load) systems: data engineers should be able to move data from databases and other sources into a single repository, like a data warehouse.
Data engineering is quite a big field but for entry level the above skills will set you up on a good path. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.