Since the last decade, as the majority of companies started adopting digital transformation, Data Scientists and Data Engineers have evolved into two separate roles, of course, with certain overlaps.
In this blog, we shall define the key concepts of Data Engineering, its tools, and trends:
Who is a Data Engineer?
Enterprise data is stored in various formats: databases, text files, or any other sources of storage. Data Engineers are the professionals who build pipelines to transform this data into the formats that are readable and usable for Data Scientists. They convert the data in such a way that it is suitable for analysis. This pipeline involves taking data from discrete sources and storing them in a single warehouse, wherein the data will be represented uniformly.
Check out this video to understand the key differences between a Data Analyst and a Data Engineer:
A Data Engineer can be critically termed as the first member of the Data Science team. He/she works with huge amounts of data to maintain the analytics infrastructure, making it suitable for Data Scientists to work on.
To execute all the above tasks, Data Engineers must be highly skilled in SQL, Data Engineering architecture, cloud technologies, frameworks such as Agile, Scrum, etc., and Data Engineering programs, such as Python and Julia.
Data Engineering Definition
Data Engineering meaning can be explained in this way: It is a terminology used for collecting and validating quality data so that it can be used by Data Scientists. It is an incredibly broad field, which comprises employing different modules and data steps, such as data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management.
Hence, it is not possible for a single Data Engineer to work across the whole spectrum of skills. In this blog, we shall outline the specific roles a Data Engineer performs as per the requirements from the employer.
Learn Data Engineering through our Data Science Tutorial designed for beginners for a better understanding of the concept.
Responsibilities of a Data Engineer
Data Engineers maintain the data infrastructure to support business applications. As part of their responsibilities, they fuel Artificial Intelligence analytics and the Machine Learning process.
Various positions held by a Data Engineer are listed below.
- Data Architects ingest, design, and manage the sources of data essential for business insights to build a Data Engineering architecture. With in-depth knowledge of SQL and XML, they can integrate and organize certain parts of the data management system.
- Data Engineers are the ones who need to be proficient in programming languages such as Python and Julia. They design, integrate, and prepare the data infrastructure, adhering to all data management norms.
- Database Administrators (DBAs) design and maintain database systems to ensure that users can access all functions seamlessly. They also optimize the speed of databases and work against workflow interference.
Roles of a Data Engineer
A Data Engineering career has a long yet worthy path to its success. It develops through various roles as explained below:
- A Generalist Data Engineer is someone who works in a small team. He/she is typically a data-focused person and works on ingesting data to process it for further analysis.
- Pipeline-centric Data Engineers work for mid-sized companies, where they have to deal with a little more complex data needs. They have to work according to the Data Engineering methods in collaboration with Data Scientists to transform the data. Knowledge of computer science and distributed systems are essential for these professionals to execute such analyses.
- A Database-centric Data Engineer is someone who sets up and populates analytics databases. He/she works with the pipeline and tuning for quick analysis and designing schemas. These Data Engineers usually work for larger organizations where the data is distributed across several databases.
Data Engineering Trends
A Data Engineer specializes in data modeling, data transforming, data storage, and data maintenance to deliver Data Analytics across various systems.
Gartner predicts that, by 2022, a minimum of 80 percent of all projects will include an AI-driven virtual developer within their team.
In the case of Data Engineering, AI can take care of repetitive tasks by reducing the number of time-consuming tasks in the field of quality assurance. With the help of techniques such as behavior-driven development and test-driven development, AI can also be trained in coding.
A Data Engineer is basically a software engineer specialized in data. Therefore, recent trends in software development also apply to Data Engineers. Some of them are as follows:
- HTTP/3: Data Engineers can make use of HTTP/3 in the layer of data collection. HTTP/3 is a protocol for network communications across the web.
- Blockchain can also be made as part of data sources for transacting data and for distributed storage.
- Often, the majority of Data Engineers spend their time in building and executing data pipelines. To ease this step, they can now use the AWS Lambda function to process the data.
Data Engineering Tools
Data Science projects largely depend on the information infrastructure structured by Data Engineers. They typically implement their pipelines based on the ETL (extract, transform, and load) model.
The Data Engineering basics revolve around the typical tools that find their usage in the daily life of a Data Engineer.
- Apache Hadoop: Hadoop is a collection of tools, namely, HDFS (Hadoop Distributed File System), MapReduce, etc. It acts as a foundation framework for storing and analyzing information.
- Relational and non-relational databases: SQL and NoSQL act as the basic tools for executing Data Engineering applications. They are known for handling enormous amounts of real-time unstructured and polymorphic data.
- Apache Spark: It is used for stream processing and batch processing. It is 100x quicker than MapReduce and is estimated to replace MapReduce in the Hadoop Ecosystem soon.
- Python: It is the most popular general-purpose language used for statistical analysis. A majority of Data Engineer job descriptions mention ‘fluency in Python’ as a mandatory requirement.
- Julia: Julia is yet another general-purpose programming language that is easy to learn. It has the capability to be used solely in data projects for prototyping and production.
Data Engineering Automation
The industry of Data Engineering is taking a step forward in automating the data pipeline to confine the process that goes into transforming and collecting data. This methodology, thereby, aids the workload on Data Analytics and Machine Learning.
Initially, we have seen that Data Science has adapted automation to conduct the most repetitive tasks. Now, Agile Data Engineering and DataOps tools are emerging within Data Engineering to handle the repetitive data pipeline work.
Agile Data Engineering is independent of the underlying execution platforms. On the other hand, the field of DataOps includes the techniques of DevOps, such as agility and continuous delivery. This, in turn, is implemented in the different environments of Data Analytics, including data warehouses, data sources, etc. The ultimate goal of automating Data Analytics is to enhance agility and reduce defects.
This automation also addresses Data Engineering and Artificial Intelligence tasks that start from data ingestion and goes through shaping the data and then preparing it for consumption.
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Data Engineering is all about dealing with scale and efficiency. Therefore, Data Engineers must frequently update their skill set to ease the process of leveraging the Data Analytics system. Because of their wide knowledge, Data Engineers can be seen working in collaboration with Database Administrators, Data Scientists, and Data Architects.
Without a doubt, the demand for skilled Data Engineers is growing rapidly without having to look back. If you are a person who finds excitement in building and tweaking large-scale data systems, then Data Engineering is the best career path for you.
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