Companies today count on big data to improve their operations, create personalised marketing campaigns and provide better customer service. Big data has incredible volume, diversity, variability and velocity of data. Handling it requires someone who has a thorough understanding of what it is and how to use it. And that someone is a big data engineer.
The great thing about data is that it never runs out. As long as there is data, there will be a need for a data scientist or a big data engineer to cater to the needs of businesses regarding data.
This article takes a closer look at what is a big data engineer, how they differ from a data engineer, what their skills are and how to become one. Let's dive in.
What Is a Big Data Engineer?
Companies today collect enormous amounts of data called big data. This data is used for several purposes that benefit the company. A big data engineer is responsible for developing, maintaining, testing, analyzing and evaluating the inflow of all big data a company has. A big data engineer makes sense of the huge amount of data by collecting, maintaining and extracting data from it. By doing this, the company can improve its efficiency, scalability and profitability. Big data engineers play a major role in contributing to the company's growth.
Difference Between Data Engineer and Big Data Engineer
Data and big data differ from each other only in terms of their volume. Big data is about vast sets of data. Similarly, a data engineer and a big data engineer are different because they deal with data in different volumes. However, it is not just the volume that matters. There is a major upgrade in terms of complexity as well.
A big data engineer will have to learn multiple big data frameworks and NoSQL databases to create, design, and manage the processing systems.
- A data engineer's responsibilities are limited to data ingestion, taking data from different sources and then ingesting it into the data lake, from which data is extracted using batch and real-time extraction. Other skills include incremental load, loading the data parallelly.
- A data engineer also has to perform data transformation. It can be simple or complex, depending on the source, format, and required output.
- Performance optimization is when a data engineer improves the performance of an individual data pipeline and optimises the overall system.
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In-Demand Big Data Engineers Skills
1. Multi-Cloud computing
A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. They would need to know their way around IaaS, PaaS and SaaS implementation
2. Visualization
Data engineers need to process and visualise datasets. They also need to be skilled in exploratory data analysis or EDA to ensure ETL/ELT work and skilled in working with tools like SSRS, Excel, PowerBI, Google Looker, PowerBI etc.
3. Machine Learning and AI
A big data engineer should be familiar with Python’s libraries SciPy, NumPy, sci-kit learn, pandas, etc. They should also be familiar with the terminology and algorithms.
4. NoSQL
A data engineer should know how to work with key-value pairs and object formats like Avro, JSON, or Parquet in the open-source Apache-based or MongoDB and Cassandra.
5. Data Pipelines
Data engineers must operate with real-time streams, data warehouse queries, JSON, CSV and raw data.
6. Hyper Automation
Hyper automation focuses on improving the quality of work, increasing decision-making agility, and accelerating business processes. They require skills to run value-added tasks.
7. Programming
A decent knowledge of programming languages is required, especially Python, Go, Ruby, Rust, and Scala with Apache Spark data store. Online cloud implementations like DataBricks and Amazon Glue are also relevant.
8. DevOps
A data engineer should be knowledgeable in Software Development Life Cycle (SDLC) Continuous Development (CD), and Continuous Integration (CI) techniques. They should also know tools like Git, GitLab, and Jenkins.
9. SQL
Data engineers use SQL for performing ETL tasks within a relational database. SQL is ideal for use when the destination and data source are the same types of database.
10. Scripting
Data engineers should be able to script in multiple languages. Along with Python. NET, R, Shell Scripting, and Perl are other popular programming languages. Because they enable you to work with MapReduce, a crucial Hadoop component, Java and Scala are essential.
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Big Data Engineer Road Map
Becoming a big data engineer is not difficult. With determination and hard work, these steps can take you to your destination quickly.
Degree
Most of the above skills require years of education to acquire. A bachelor's degree in computer science, statistics or business data analysis, along with a master's degree in coding, statistics and data, are necessary to be a skilled data engineer. Most companies require a bachelor's degree for entry-level positions.
Experience
Experience is something nothing can replace. It is the most valuable asset for a big data engineer. Freelancing, interning and practising independently will give you practice. The more traditional way of getting it is to work as an entry-level data engineer. It directly affectsyour chances of getting a better position.
Certifications
There are highly valued certifications that add to the skillsets of a prospective data engineer. Make sure you look out for the best certifications available on the market. One such course you can try is the Simplilearn's Big Data Engineer Masters Program.
Conclusion
Big data engineering is a suitable position for someone passionate about data, computer science, numbers and programming. It is not a cakewalk, but the work is worth it. You get to be a direct part of a company's growth journey. It is also incredible to watch meaning form out of vast amounts of data.
Simplilearn offers the Caltech Post Graduate Program In Data Science, in collaboration with Caltech and IBM, which is a great platform to get started on the journey to becoming a data scientist. You can also check out ourProfessional Certificate Program In Data Engineering to get started as a Data Engineer.
Frequently Asked Questions
1. What are data engineering skills?
Knowing big data frameworks and databases, creating data infrastructure, containers, and other topics are expected of big data engineers. Additionally, they must gain practical experience with software like Hadoop, Scala, HPCC, Storm, Rapidminer, Cloudera, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and others.
2. What do big data engineers do?
A big data engineer is IT role who key responsibilities include designing, building, testing, and maintaining complex data processing systems. They work with large data sets.
3. Do big data engineers code?
Most positions in data engineering require coding, which is a highly valued skill. Most employers desire applicants to have at least a fundamental understanding of programming languages like Python.
4. Does big data need programming?
Yes, big data requires programming skills. You need the basic knowledge of Java to learn Hadoop and spark. It’s also possible to learn spark coding in scala or python to acquire a better set of skills
5. Do data engineers use C++?
One of the fundamental programming languages that Data Engineers can use is C++. Large data sets and processing 1 GB of data per second can be done using C++. Data engineers can retrain the data this way and keep records consistent.
