Introduction to Data Engineering
Data science can be generally defined as the process of making data useful, and data engineering is a key part of how and why. Data engineers make sure the data flow is running smoothly, monitor systems, anticipate problems, and repair the data pipeline whenever problems arise. They extract and gather data from multiple sources and load it into a single, easy-to-query database. In short, data engineers make data scientists’ lives easier. They are vital parts of any data science project and their demand in the industry is growing exponentially in the current data-rich environment.
Before a model is built, before the data is cleaned and made ready for exploration, even before the role of a data scientist begins – this is where data engineers come into the picture. Every data-driven business needs to have a framework in place for the data science pipeline, otherwise it’s a setup for failure.
This article puts together a list of things every aspiring data engineer needs to know. Initially we’ll see what a data engineer is and how the role differs from a data scientist. Then, we’ll move on to the core skills you should have in your skillset before being considered a good fit for the role. We have also mentioned some industry recognized certifications you should consider.
Right, let’s dive right into it.
So, what is a Data Engineer?
A data engineer is responsible for building and maintaining the data architecture of a data science project. These engineers have to ensure that there is uninterrupted flow of data between servers and applications. Some of the responsibilities of a data engineer include improving data foundational procedures, integrating new data management technologies and softwares into the existing system, building data collection pipelines, among various other things.
One of the most sought-after skills in data engineering is the ability to design and build data warehouses. This is where all the raw data is collected, stored and retrieved from. Without data warehouses, all the tasks that a data scientist does will become either too expensive or too large to scale.
ETL (Extract, Transform, and Load) are the steps which a data engineer follows to build the data pipelines. ETL is essentially a blueprint for how the collected raw data is processed and transformed into data ready for analysis.
Data engineers usually come from engineering backgrounds. Unlike data scientists, there is not much academic or scientific understanding required for this role. Developers or engineers who are interested in building large scale structures and architectures are ideally suited to thrive in this role.
The Difference between a Data Scientist and a Data Engineer
It is important to know the distinction between these 2 roles. Broadly speaking, a data scientist builds models using a combination of statistics, mathematics, machine learning and domain based knowledge. He/she has to code and build these models using the same tools/languages and framework that the organization supports.
A data engineer on the other hand has to build and maintain data structures and architectures for data ingestion, processing, and deployment for large-scale data-intensive applications. To build a pipeline for data collection and storage, to funnel the data to the data scientists, to put the model into production – these are just some of the tasks a data engineer has to perform.
For any large scale data science project to succeed, data scientists and data engineers need to work hand-in-hand. Otherwise things can go wrong very quickly!
To learn more about the difference between these 2 roles, head over to our detailed infographic here.
The Different Roles in Data Engineering
- Data Architect: A data architect lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. This role requires knowledge of tools like SQL, XML, Hive, Pig, Spark, etc.
- Database Administrator: As the name suggests, a person working in this role requires extensive knowledge of databases. Responsibilities entail ensuring the databases are available to all the required users, is maintained properly and functions without any hiccups when new features are added.
- Data Engineer: The master of the lot. A data engineer, as we’ve already seen, needs to have knowledge of database tools, languages like Python and Java, distributed systems like Hadoop, among other things. It’s a combination of tasks into one single role.
Core Data Engineering Skills and Resources to Learn Them
- Introduction to Data Engineering
- Basic Language Requirement: Python
- Solid Knowledge of Operating Systems
- Heavy, In-Depth Database Knowledge – SQL and NoSQL
- Data Warehousing – Hadoop, MapReduce, HIVE, PIG, Apache Spark, Kafka
- Basic Machine Learning Familiarity
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