Data Engineering is a set of operations that focuses on creating mechanisms and interfaces for the seamless access and flow of information. It is the aspect of Data Science that emphasizes the practical applications of Data Analysis and Data Collection. The work of a Data Engineer is important in building the data stores that can take the insights and put them to practical use.
In this article, you will get an idea about Data Engineering as a whole. On that note, it explores the need for Data Engineering, the skills one needs to become a skilled Data Engineer, and the key roles that a Data Engineer operates in.
Table of Contents
What is Data Engineering?
‘Data Engineer’ as a term started doing the rounds back in 2011 in the circles of new data-driven companies like Airbnb and Facebook. These companies were dealing with massive amounts of potentially valuable real-time data, which created the need to develop tools that could handle all this data correctly and quickly.
‘Data Engineering’ as a term evolved to describe a role that marked the shift away from traditional ETL tools. It began to focus on developing new tools with the capability to handle increasing volumes of data. With the advent of Big Data, it grew in its definition to describe a kind of software engineering that focused deeply on data. This included Data Warehousing, Data Modeling, Data Mining, Metadata Management, and Data Crunching.
When looking at the hierarchy of needs in Data Science implementations, the next step after gathering your data for analysis is Data Engineering. It has grown as a discipline to offer reliable data flow and effective data storing while being in charge of infrastructure.
The ultimate goal of Data Engineering is to provide consistent and organized data flow to enable data-driven work like:
- Exploratory Data Analysis
- Populating Fields in an Application with External Data
- Training Machine Learning Models
There are multiple ways to achieve this data flow, the most common being the Data Pipeline. This is a system that comprises independent programs carrying out several operations on collected or incoming data. Data Pipelines are often distributed across several servers as follows:
What is Data Engineer?
Data Engineers are in charge of identifying patterns in large data sets and designing algorithms to make raw data more relevant to businesses. This IT position necessitates a diverse range of technical abilities, including a thorough understanding of SQL Database Design and several programming languages. However, data engineers must be able to communicate across departments in order to comprehend what business leaders want to gain from the company’s huge Datasets.
Data Engineers are often in charge of developing algorithms to make raw data more accessible, but in order to do so, they must first understand the company’s or client’s goals. When working with data, it’s critical to align business goals, especially for firms that deal with huge and complicated Datasets and Databases.
Data Engineers must also know how to improve data retrieval and create Dashboards, Reports, and other Visualizations for stakeholders. Data engineers may also be in charge of communicating data trends, depending on the organization. To help comprehend data, larger corporations frequently employ numerous Data Analysts or Scientists, but smaller businesses may rely on a data engineer to perform both functions.
Understanding the Importance of Data Engineering
The need for Data Engineering has become more apparent in the last decade or so. Also, a vast majority of companies underwent a digital transformation over the last decade. This ended up generating colossal volumes of new types of complex data.
This made the need to ensure and organize this data’s quality, availability, and security an absolute necessity. This was previously clubbed into the skillset of the unassuming Data Scientist. This resulted in Data Pipelines not being able to function properly which hurt a company’s ability to extract optimal value from their data projects.
As more companies strive to become AI-driven, Data Engineering becomes an important necessity to provide the foundation for fruitful Data Science initiatives. Since Data Scientists no longer have to worry about building the necessary infrastructure for their work, they can focus on what they do best.
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Why pursue a career in data engineering?
This is a lucrative and tough field to work in. You’ll play a critical part in an organization’s performance by making Data Scientists, Analysts, and decision-makers more accessible to the data they need to accomplish their jobs. To build scalable solutions, you’ll use your programming and problem-solving talents.
Data Engineers will be in high demand as long as there is data to process. Data Engineering, according to Dice Insights, is the top trending career in the technology industry in 2019, beating out Computer Scientists, Web Designers, and Database Architects. It was mentioned on LinkedIn as one of the careers to watch in 2021.
Data Engineer Salary
Data engineering is also a lucrative profession. According to Glassdoor (June 2021), the average pay in the United States is $111,933, with some data engineers making as high as $164,000 per year. When you compare this to other data roles like Data Analyst ($68,000) or Database Administrator ($81,444), it’s clear that Data Engineers are well compensated.
Data Engineer Certifications
There are only a few certificates dedicated to Data Engineering; however, if you want to expand your knowledge beyond Data Engineering, there are lots of different Data Science and Big Data Certifications to choose from.
However, any of these qualifications will look fantastic on your CV if you’re wanting to prove your worth as a Data Engineer:
Understanding the Skills Required to Become a Data Engineer
Data Engineers need a very special set of skills to create software solutions around data. The tools that are leveraged for this role are constantly changing and vary considerably by the industry as well. Here’s a list of technologies in job listings for the role of Data Engineer in the last year:
Apart from the technologies mentioned in the graph above, important skill areas for a Data Engineer are as follows:
- Distributed Systems: This includes software architecture and software engineering skills.
- Foundation Software Engineering: This involves having a deep knowledge of DevOps, architecture design, and agile service-oriented architecture.
- Analytics: This is a primary requirement for Data Scientists, however, understanding some of the different probabilistic principles or mathematical principles, or statistical analysis skills are a prerequisite to being able to properly manipulate the data. This ensures that the data is made available to the people who are doing the end analysis on it in an easy-to-understand format.
- Data Modeling: Data Modeling knowledge has gained significant importance since a Data Engineer needs to know how they are going to structure partitions, tables, where to denormalize and normalize the data in the warehouse, etc. Data Modeling also helps a Data Engineer understand the approach to retrieve certain attributes.
- Pandas: Pandas is a Python library that can be used for manipulating and cleaning the data.
Understanding the Responsibilities of a Data Engineer
Data Engineering teams cater to multiple teams for their data needs as follows:
- Product Teams
- AI and Data Science Teams
- Business Intelligence or Analytics Teams
Before any of these teams can work efficiently, there are certain needs that need to be looked after. These encapsulate the responsibilities of a Data Engineer. This can be carried out through multiple approaches to accommodate their individual workflows. A few key responsibilities of a Data Engineer are as follows:
Data Cleaning works in tandem with Data Normalization and Modeling. Data Normalization and Modeling is often considered as a subset of Data Cleaning. Data Cleaning includes numerous actions that make the data more holistic and uniform such as:
- Filling in missing fields wherever possible.
- Ensuring dates are in the same format.
- Casting the same data to a single type. For instance, forcing the strings in an integer field to be integers.
- Removing unusable or corrupt data.
- Constraining values of a field to a given range.
The specific actions you take to clean the data are highly dependent on the Data Model, the inputs, and the desired outcomes. Clean data is crucial for the following group of people:
- Product Teams need it to ensure that their product doesn’t give faulty information or crash during usage.
- Machine Learning Engineers need clean data to build generalizable and accurate models.
- Business Intelligence Teams need it to provide accurate forecasts and reports to the business.
- Data Scientists need clean data to perform accurate analyses.
Data Normalization and Modeling
After the data has been ingested into a system, it needs to conform to some kind of architectural standard. This is where Data Normalization and Modeling come in. Data Normalization refers to the set of tasks that make the data more accessible to users. This consists of the following steps (not limited to these steps):
- Fixing Conflicting Data.
- Removing Duplicates (Data Deduplication).
- Conforming Data to a specified Data Model.
These processes take place at different stages. The following image depicts a modified version of a Data Pipeline highlighting the different stages at which certain teams may access their data:
A well-architectured Data Model is crucial if your target user is a Product Team. It can be the difference between a barely responsive, slow application and one that runs more seamlessly. These decisions are often made through the collaboration between Data Engineering and Product Teams.
Data Normalization and Modeling usually come under the transform step of ETL along with Data Cleaning.
Data Accessibility refers to the ease with which the data can be accessed and understood by the customers. The definition, however, differs from customer to customer:
- Analytics Teams may prefer data that can be grouped based on a particular metric and can be accessed through a reporting interface or basic queries.
- Product Teams will often want data that can be accessed through straightforward and fast queries that don’t change much while focusing on product reliability and performance.
- Data Science Teams, on the other hand, might simply need data that’s accessible with some kind of query language.
Data Accessibility is closely tied to how data is stored that makes it a major component of the load step of ETL. It refers to how the data is stored for later use.
Understanding the Key Data Engineer Roles
Although Data Engineers have responsibilities as listed above, the daily routine of one may differ based on the type of company they work for. You can classify them into the following categories:
These engineers tend to be exceptional resources for mid-sized companies that have complicated Data Science needs. An engineer in this scenario will work with teams of Data Scientists to transform data into a useful format for analysis. This requires in-depth knowledge of computer science and distributed systems.
Engineers are focused on setting up and populating analytics databases. This involves working with Data Pipelines but is primarily focused on tuning databases for creating table schemas, with a focus on ensuring faster analysis. Database-centric engineers are usually found at larger companies with several data analysts that have their data distributed across databases.
Generalist Data Engineers typically work in a small team. Without the engineers, data analysts, and scientists don’t have anything to work with. This makes the engineer a critical first member of a Data Science team. When a Data Engineer is the only data-focused person at a company, they usually have to do more end-to-end work. For example in a generalist case, they may have to do everything from ingesting the data to processing it to carrying out the final analysis.
This requires more Data Science skills than most engineers have. However, this scenario also requires fewer systems architectural knowledge since small companies and teams don’t have a ton of users. This reduces the importance of engineering for scale. This is a good role for a Data Scientist making the shift to the engineering position.
Data Scientist vs Data Engineer, What’s the difference?
Data Engineers are data experts that prepare the “Big Data” infrastructure for Data Scientists to evaluate. They are software engineers who create, produce, and manage big data by designing, building, and integrating data from diverse sources. Then they construct complicated queries on top of it, ensuring that it is easy to use and runs smoothly, with the goal of improving the performance of their company’s Big Data Ecosystem.
They may also perform ETL (Extract, Transform, and Load) on large datasets to create big data warehouses that data scientists can use for Reporting and Analysis. Furthermore, because Data Engineers are mainly concerned with Design and Architecture, they are not expected to be experts in Machine Learning or Big Data Analytics.
Skills: Hadoop, MapReduce, Hive, Pig, Data streaming, NoSQL, SQL, programming.
Tools: DashDB, MySQL, MongoDB, Cassandra
A Data Scientist is the alchemist of the twenty-first century: someone who can transform unprocessed data into useful information. To answer crucial business challenges, Data Scientists use Statistics, Machine Learning, and Analytic methodologies. Their main goal is to assist businesses in turning large amounts of data into useful and actionable insights.
Data Science is not a new topic in and of itself, but it may be thought of as a higher level of Data Analysis that is aided and mechanized by Machine Learning and Computer Science. In other words, unlike ‘Data Analysts,’ Data Scientists are required to have strong programming abilities, the capacity to build new algorithms, handle massive data, and some domain knowledge in addition to data analytical skills.
Furthermore, Data Scientists are required to analyze and communicate the results of their research through Visualization Tools, Data Science Apps, or telling engaging tales about the solutions to their Data (Business) challenges.
To create statistical models or identify patterns in data, a data scientist’s problem-solving skills necessitate a mastery of both classic and novel data analysis methodologies. For instance, developing a recommendation engine, predicting the stock market, diagnosing patients based on their similarities, or detecting fraudulent transaction patterns.
Skills: Python, R, Scala, Apache Spark, Hadoop, machine learning, deep learning, and statistics.
Tools: Data Science Experience, Jupyter, and RStudio.
Tags: BI developer, Big Data, data analyst, data engineer, data science, data scientist, data scientist vs data engineer
This article talked about Data Engineering in great detail highlighting topics like the importance, skills, responsibilities of a Data Engineer, and the key roles for this position.
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