Data Engineering is primarily concerned with Information Engineering as to how the data will be organized, collected, and used.
Data Engineers build the foundation required to channel data into a format useful according to its intended purposes. They function behind the scenes to enable Data Scientists and researchers to probe for insights, apply Machine Learning, algorithms, etc.
Data Engineers hold the key responsibilities of Information Engineering to design and build the pipelines to transform and transport the data.
It is not an easy job, as most of the Data Engineering and Data Science Projects fail due to a lack of reliable systems and resilient cloud data infrastructures.
Data Engineering is difficult and a leading profession in the tech industry.
If you want to learn Data Engineering, you must learn the basics of software engineering, including design, development, testing, and the maintenance of applications.
Unlike software engineers who function at different levels, data engineers build efficient systems from designing, creating, building, and maintaining data pipelines for collating data, processing, integrating, performance optimization and more.
The rise of big data has placed more responsibility on data engineers to perform a plethora of tasks as data keeps abounding at higher frequency, creating therefore complex requirements to process raw data.
- There are always requirements for handling large-scale data processing
- Unique purposes for consolidating and enrich numerous datasets
- Need for better plans to monitor and maintain systems
Big data skills are essential for all data engineering job roles. There is an ever-increasing demand for Data Engineers and it is one of the rising professions across the globe. Therefore, it would be in your best interests to pursue this goal.
Data Engineers are required to have a solid knowledge about big data frameworks, database architectures, cloud computing, and more.
If you want to learn Data Engineering, understand the basics of working with all kinds of data assets, starting from programming, data pipelines, data queries and data storage systems.
In this article, we list basic steps of learning the distinct intricacies of data engineering that one should apply to build a solid foundation for a rewarding career in big data and data science.
How to become a Data Engineer (Skills for Data Engineering)
Data Engineers are expected to possess sound skills in designing data models, building data warehouses, and data lakes.
It’s important to learn every important concept related to Cloud Data Warehouses, ETL, Data Pipelines and Big Data Engineering tools like Apache Airflow, Hadoop, Spark and more.
Data engineering is quite an advanced field and requires learning a lot of skills. This may sound intimidating, but it is reachable. In fact, as a beginner, keep only a few goals; learn to code, understand cloud computing concepts, and use a few big data tools with consistent practice.
You don't need to commit to a full-time degree program or wait years to get a job as a data engineer. It’s possible to develop the skills you need to get an entry-level role as a data engineer in a matter of months. But getting a job doesn’t mean your learning should stop. In data engineering, you’ll continue improving your skills.
You should build these skills to become a successful data engineer:
Some popular programing languages in big data engineering are Python, Java, Scala, and Go.
Learn the basics of Python or R programming – If you are a beginner, we very much recommend learning Python. It is flexible, and able to handle many data types.
Python is a great fit for Data Engineering, with useful packages/modules to build custom solutions to improve overall workflow.
Spend a lot of hours sharpening programming skills to build data pipelines and develop the ability and interest to work with massive datasets.
You must learn how data is stored programmatically and structured by the machines. It is critical to learn all the basic and different data types.
After you’ve built a foundation in programming, pick one skill and dig deeper. Build confidence with a skill you already have some proficiency in or tackle your serious weakness head-on.
#2 Relational and Non-relational databases
A strong understanding of NoSQL and SQL databases is essential for working in data warehousing and data modeling.
It’s very crucial to learn database architectures and having the working knowledge of both relational and non-relational-databases.
Build a solid understanding of SQL and gain knowledge of the relational databases, their functions, and the syntax.
Learn NoSQL databases and the components of data workflows. You must build a good knowledge of non-relational database systems, such as MongoDB, Apache Cassandra, and Redis.
#3 Learn regular expressions (RegEx)
You must learn to perform advanced data cleaning with regular expressions (RegEx) on datasets.
- Learn regular expressions to perform powerful string manipulation.
- Learn regex components like character classes and quantifiers
- Learn Regular Expressions with the ‘re’ module and pandas in Python
- Learn character classes, quantifiers, positional anchors, capture groups and more.
Data Engineers are first responsible for working with the real-world data, and it is important to think like a data scientist as you explore a dataset to build data pipelines to make the data available to other people.
You don’t have to abandon everything and study full time to make progress toward. It will surprise you by how much you can accomplish with as little as 15-20 minutes a day.
#4 Learn ETL (extract, transform, and load) systems
ETL stands for "extract, transform, and load" and these are three unique processes.
It is the process employed by data engineers to move data from databases and other sources into a single repository, like a data warehouse.
ETL allows data engineers to unify data from multiple databases and other sources into a single repository with data that is formatted and qualified for analysis.
Learn popular ETL tools like Xplenty, Stitch, Alooma, and Talend. These tools automate the extraction, transforming, and loading processes for centralizing data from multiple data sources or databases.
Accuracy is absolutely critical, but while you’re learning, accept the fact that you will mess up. You will feel frustrated, but learn from those attempts and become better at ETL by working through them.
#5 – Automation
You don’t have to wait until you have a job as a data engineer to gain experience. As you’re learning programming and ETL, you can use tools for automation libraries/packages to crawl the web for interesting data.
Learning should be less about memorizing information and more about improving broader skill sets.
Take a do-it-yourself approach by designing your own automation projects using free, open-source data sets.
Learn to write scripts to automate repetitive tasks.
Automation improves the quality of work, accelerates productivity, and increases decision-making agility. It is a required part of working with big data simply because organizations can collect so much information.
Data engineers mostly dive into data and identify tasks for automation to eliminate manual participation.
#6 Data Storage
Data Engineers build data storage and processing systems. It takes data engineers to maintain data so that it is highly available and usable by other people to dig the actionable insights out of it.
You must be able to determine when to use data lake vs a data warehouse for designing data solutions.
There are complex challenges, as we should store not all types of data in the same way. The growing non-traditional data storage solutions to fulfill complex requirements are search engines, document stores, and columnar stores.
- Search engines transcend at text queries and offer higher query capabilities and better performance.
- Document stores provide better data schema adaptability than traditional databases.
- Columnar stores specialize in value aggregations and single column queries.
#7 Understand basics of machine learning (ML)
You’ll need to learn the basics of Machine Learning and algorithms as you go along. You don't need to work with ML models, but the data science and research teams rely heavily on the work of data engineering teams.
Data Scientists and ML Engineers work in close collaboration with Data Engineers. You will need to learn mathematics and ML algorithms to enable the work of multi-dimensional data in a dynamic environment.
#8 Big Data Engineering Tools and Frameworks
Data Engineers are highly responsible for managing and maintaining data. Big data tools are evolving and some popular tools which you need to master are:
Apache Spark: Learn about Apache Spark — an open-source analytics engine for data processing. Apache Spark performs mostly the same functions as Hadoop, but it is significantly faster and supports stream processing. Learn how to set up a cluster of machines that enables you to create a distributed computing engine to process large amounts of data. Spark fluency is one of the important data engineer skills.
Apache Hadoop: It is an open source framework used to store and process large datasets. Learn distributed processing of large datasets using simple programming models, its use for big data processing and machine learning. Its shortcoming include low processing speed and require a lot of coding.
Apache Kafka: It is an open-source distributed event store and stream-processing platform. Learn to build real-time streaming data pipelines and real-time streaming applications.
Apache Flink: It is a big data processing tool, a distributed processing engine and a scalable data analytics framework. Learn to process data streams at a large scale and also how to deliver real-time analytical insights about your processed data with your streaming application. Learn as well about Apache Flink APIs, PyFlink, and Table/SQL API.
Distributed File System: Distributed file systems are used to store data during processing and make it convenient to share data among users on a network in a controlled way. Learn to use HDFS, Amazon EMR and AWS S3. These specialized file systems can store a virtually unlimited amount of data and offer a wide variety of uses besides big data analytics, including storing data for web/mobile applications and IoT devices.
#9 Cloud Computing
Data Engineers have an excellent working knowledge and rich experience with cloud platforms like Amazon Web Services, Azure, GCP and DigitalOcean.
A Data Engineer needs to have a solid understanding of cloud computing and working knowledge of IaaS, PaaS, and SaaS implementations.
You’ll need to learn about cloud storage and cloud computing as companies increasingly use cloud platforms.
How to Learn The Skills for Data Engineering
Data engineers are skilled software engineers who understand software/application development process, data pipelines and database architecture.
Data engineering skills are also helpful for adjacent roles as well, such as Software Engineer, Cloud Engineer, ML Engineer and Data Scientist.
Data engineering is a new field, so there are no formal data engineer qualifications. Since it is rather hard to find a university program that teaches data engineering, a better option is learning yourself via an online learning program that specializes in data engineering.
We have provided a comprehensive overview of high-quality data engineering courses and learning programs from notable educators. You will learn popular programming languages and tools used by data engineers (Python, R, SQL) as well as data science, machine learning, building data pipelines, cloud computing and finding data warehousing solutions.
If you want to be a part of this groundbreaking science, devote a long-term commitment to learning and learn all the skills we listed in this article. They are the skills expected for data engineering job roles.
We hope you found our article on data engineering skills useful.
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