If you’ll observe todays market discourse you will find Data Engineering and Data Science mentioned in most of them. In our today’s topic is Data engineering so let’s start with the question what Data Engineering is.
Data engineering work in various streams of data science to provide usable information to data scientists from raw data. So primarily data engineers work on Raw information and then slice and dice it to make it usable by Data Scientist. Data engineering’s goal is to make data accessible to organisations so that organisations can use the data for their development and improvement. Data Engineering also find trends in Datasets.
Let’s find what type of roles we have in Data Engineering.
Generalist: –
Data engineering generalist take care for every step-in data processing. Generalists are mostly found in smaller teams or setups as small teams does not have to worry about engineering for scale. Generalists Data Engineers plays several roles for data collection, segregation, and data delivery for specific purpose. This kind of roles are perfect for people transitioning from Engineering to data Engineering roles.
Pipeline Centric: –
Pipeline-centric data engineers work alongside data scientists to help make use of the data they collect. Pipeline-centric data engineers requires in-depth knowledge of distributed data systems and computer science. These types of roles are found mostly in mid-size companies, as mid-size companies need to deploy data engineers on various sources of data to organize and them feed them into a proper system to make use of it.
Database Centric: –
Database-centric data engineers work with data warehouses across multiple databases and are responsible for developing table schemas.
These positions are highly required at organisations of large scale where data engineering teams required to maintain continuous flow of large data chunks, all the time. In these type of roles data base scientist focus on analytical databases.
What are some key responsibilities of Data engineering teams?
- Use large data sets to provide valuable insights for business issues.
- Prepare data-based information for predictive and prescriptive modelling.
- Develop, create, test, and maintain Data architectures.
- Align Data architecture with Organization’s needs.
- Acquisition of data.
- Create data-set processes.
- Identify ways to improve data reliability, efficiency, and quality.
- Conduct research for industry and business queries.
- Deploy sophisticated analytics programs, machine learning and statistical methods.
- Find hidden patterns using data.
- Use data to discover tasks that can be automated.
- Deliver updates to stakeholders based on analytics.
We hope you would have liked the information.
Follow us for more such information.