What is Data Engineering, and Why is it important? Everything You Need to Know

Data production and storage have drastically increased in the past few years. By 2025, it is estimated that 200 Zettabytes of data will be stored globally.

Data production and storage have drastically increased in the past few years. By 2025, it is estimated that 200 Zettabytes of data will be stored globally. And this figure isn't set to slow down anytime soon — by 2022; enterprises store approximately 2.02 petabytes of data — an increase of 42.2% over the two years!

Big Data Analytics is rapidly emerging as a multi-billion dollar market. Companies are starting to understand the importance of leveraging their data for better decision-making, enterprise protection and growth. The US economy loses up to $3.1 trillion every year due to poor data quality — but Fortune 1000 companies can benefit from additional net income reaching an impressive $65 million when increasing data accessibility by 10%. 

To unlock these opportunities from big data, businesses require staff with unique skill sets in data governance and strategy – such as engineers and scientists specialized in machine learning. This comprehensive guide focuses on the data engineering definition and its roles, functions, and responsibilities. It also highlights how vital data engineering is for businesses in deriving tangible value from their data. Get ready to dive in and learn everything there is to know about data engineering!

What is Data Engineering?

Data engineering is a vital part of business process management. It refers to creating and maintaining systems that collect and store data from various sources. Data engineers use software engineering principles, interface development, and algorithmic building to allow for a smooth information flow. As such, it is used in multiple industries, including retail, banking, finance, healthcare, education and manufacturing. Data engineering also provides an essential foundation for data scientists as they apply their analytical techniques. Professional data engineers design databases to enable easy access according to analysis needs.

Why Is Data Engineering Important?

Data engineering is a crucial component in the time of big data. The benefits of data engineering is that businesses have access to an overwhelming amount of information from physical and digital sources. Although this can be beneficial, it can also lead to information overload with fragmented data that hampers organizations from deriving applicable insights and obtaining a clear background on their operations. This is where data engineering comes into play.

Data engineering helps to streamline data for reliable use by data scientists and permits organizations to unlock the bountiful advantages of data analytics through developed data infrastructure. There are also several reasons why data engineering is important, including:

Data Quality

Data engineering is crucial to data quality assurance; it ensures data accuracy, reliability and consistency. Data engineers transform, clean and validate the data and perform compliance checks to ensure the data conforms with industry standards. Consequently, organizations wishing to get reliable and valid results from their analytics projects must consider the paramount importance of data engineering.


Data engineering is a crucial part of modern businesses and organizations, as it is necessary to process large amounts of data efficiently. As data volumes continue to rise rapidly, having systems built to handle large amounts of data without compromising performance or accuracy becomes even more vital. Data engineers construct these systems for this purpose so businesses can have scalable and efficient processes for large datasets.


The data engineering process optimizes and analyzes data the way organizations access it,  By streamlining data processing and storage speeds, businesses can quickly gain actionable insights that help them make crucial real-time decisions. This is especially important for companies that need to react quickly to changing customer needs or market conditions. Data engineers are key professionals in this field and play an invaluable role in helping organizations stay competitive and agile.


Businesses process vast volumes of data, so secure data engineering is essential. Data engineers use specialized encryption techniques, access control systems and other security protocols to create secure frameworks that protect stored information from unauthorized access, theft or loss resulting from cyberattacks and breaches. Data security is a critical component for organizations that handle sensitive data, as it ensures the safety, integrity and availability of digital resources.


Data engineers are critical in helping organizations bridge their data systems across multiple sources. By creating pipelines that can integrate different data places, they help organizations gain access to more comprehensive datasets. As such, businesses can analyze this integrative data and uncover new opportunities or trends they may have overlooked. This unified system is also advantageous for gaining a competitive edge when identifying potential growth avenues.

What Is The Role of a Data Engineer?

Data engineers are the ultimate multi-taskers in the data field, covering multiple facets of data engineering, from seeking out and curating raw datasets to transforming and loading them into storage. Their expertise helps organizations make informed decisions based on insights gleaned from these real-world data sets. 

The responsibilities of a data engineer span a variety of development as well as computing aspects. Not only do they create pipelines for efficiently retrieving and indexing data, but they also analyze that very same data to ensure it meets industry regulations and practices. On top of that, the engineer must understand various pros and cons related to storing, querying, processing or joining any given dataset, actionable tasks that require high levels of technical knowledge. 

The Origins of Data Engineering: A Brief Overview

Data engineering as a profession dates back to when databases, SQL Servers and ETL came onto the scene. IBM popularized database management systems in the 1970s, and "information engineering" was coined to describe data analysis that incorporated software engineering. Then, with the rise of the internet in the 1990s and 2000s, "big data" entered into play. This brought about a new job title: Data Engineer. 

In 2011, this term started emerging within companies like Facebook and AirBnB, where gigantic amounts of potentially valuable real-time data had been amassed. These firms required software engineers who could develop effective tools for dealing with all the data rapidly and accurately - thus, “data engineering” arrived on the scene. It involves concentrating intensely on items such as metadata management, technology infrastructure of data, data warehousing, mining information models and crunching numerical values sensibly.

How Does a Data Engineer Add Value?

Data engineers are key players in an organization's efforts to collect, process and deliver data. They design and build resilient, effective pipelines that draw from different sources and harmonize disparate data sets. This data is then stored in a scalable format for end-users - business intelligence tools or other applications - to access. 

Data engineers are responsible for ensuring that the pipelines they create have the right inputs and outputs; this often includes reconciliation with source systems and building additional pipelines to validate results. To do so successfully, they must continuously maintain those systems, leveraging various monitoring protocols such as site reliability engineering (SRE). Ultimately, data engineers contribute tremendous value by streamlining complex applications of data engineering that transform raw information into actionable assets.


Data engineers play a key role in the establishment of data pipelines. Two popular options are ELT (Extract-Load-Transform) and ETL (Extract, Transform and Load). With ELT, data is extracted from multiple sources before being loaded into one central point. This approach provides a variety of advantages as it simplifies subsequent processes and systems that interact with the source data. By storing everything in one place first, architectures like data lakes become more efficient. Furthermore, this approach is beneficial if you need to join or transform data from different systems. 

By contrast, ETL provides a good option if faster results are desirable - especially when there’s a need to store the output of these transformations into databases or other storage technologies quickly and efficiently.

While it's generally recommended to use ELT (extract-load-transform) processes for increased data performance, availability, and enablement, that isn't always the case. In certain situations where relatively simple transformations are being applied to a single source of data, ETL (extract-transform-load) can be the more appropriate option due to simplified system complexity at the cost of data enablement. However, with an ELT process, the transformation is done before loading into a file system, database or warehouse, resulting in less dependency on other systems and faster execution time compared to ETL.


Data engineers must prioritize not just accuracy but performance when dealing with back-end data administration. With gigabyte, terabyte, and even petabyte-scale data sets, they ensure that the data meets service level agreements (SLA). To gauge the performance of their data engineering, it's important to establish how rapidly new data is received and how quickly data transformations complete and end up in the target destination. As businesses desire that up-to-date information is provided as soon as possible, several processes are involved in this journey that impact finalized results. Therefore, managing those variables efficiently is critical for successful outcomes.

Continuous Integration and Continuous Delivery

The modern approach to software deployment no longer works with a "set it and forget it" mentality. As data governance requirements, best practices, security procedures, tooling, and business situations continually attempt to keep up with changes in the industry; organizations must make sure their production environment remains dynamic. This increases efficiency and reduces the risk associated with manual testing. 

Automated processes that use unit and integration tests are necessary for the smooth promotion of code. These tests will prove that each piece of code functions properly in different cases, eliminating the need for live testing on the production platform. This limits potential customer dissatisfaction from unexpected bugs or errors resulting from missed testing checkpoints. An automated system is better equipped to scale as your team's needs change over time. 

Integration tests are the next step up from unit tests. This testing layer checks that when different pieces of code work together, they result in the desired outcome for a given set of inputs. It is essential to ensure that systems function as anticipated against one another.

The combination of unit and integration tests with techniques such as blue-green deployments can substantially reduce customer disruption and business risks. All changes are validated through established tests before they reach any production environment, thus guaranteeing stability and reducing disruptions to customers.

Disaster Recovery

Due to the inevitability of system failures, businesses must be prepared with disaster recovery standards. Recovery Time Objective (RTO) and Recovery Point Objective (RPO) are metrics used to evaluate critical scenarios. Data engineers leverage these components to meet the requirements of their companies, allowing for minimum downtime and assurance that customers will be fine. Advanced planning is often needed to ensure that data pipelines, databases, and warehouses can quickly restore operations without compromising quality. Preparing ahead of time is essential in this area, as comprehensive measures must be taken promptly to minimize the impact on customer experience.

In-Demand Roles in Data Engineering

Data engineering requires a high grade of expertise and knowledge. Generally, IT professionals begin their data engineering careers as software engineers or Business Intelligence (BI) analysts and work up to management roles. The most common positions in this field are data architect, big data engineer, Machine Learning (ML) engineer, cloud data engineer, data warehouse engineer, technical architect, solutions architect and Extract, Transform, Load (ETL) developer. Succeeding in these roles requires technical knowledge of the processes surrounding data engineering and practical experience.

Role Of Machine Learning In Data Engineering 

Data engineering has become an essential component of any modern business, and machine learning is its driving force. ML algorithms can automate data preparation, integration, quality monitoring, transformation and modeling tasks, such as identifying outliers and filling in missing fields so that data scientists can focus on higher-level tasks like creating data architectures or optimizing pipelines. Moreover, machine learning can also help improve data quality by detecting inconsistencies or flagging invalid values. By leveraging the power of machine learning, data engineers can consistently produce high-quality datasets ready for analysis.

Also, data integration, transformation, and modeling are all essential tasks in the field of data engineering. Data integration entails combining information from multiple sources into a centralized location, such as a data warehouse or data lake, which can prove to be an intricate task due to differing formats or schemas. Machine learning algorithms can assist with this step by finding connections between different datasets according to specific characteristics (e.g., customer name or product ID). 

Data transformation is also important and involves converting unstructured into structured data that may then be used for analysis. AI-based algorithms can assist here by identifying patterns in the source data and applying pertinent transformations that enhance its quality; for instance, ML models could spot labels embedded in the text or visual media. 

Finally, sophisticated predictive models are created via data modeling so that trends may be identified. AI algorithms have become widespread tools for aiding in the production of these resulting models; they are frequently employed to recognize patterns and make mindful estimates concerning future changes such as customer behavior or market performance.

Essential Skills for Data Engineers

Data engineers usually possess the skills of traditional software engineers plus a few extras. Here are some of the tools and responsibilities associated with data engineering: 

ETL Tools 

ETL stands for "extract, transform, and load, " which is essential to data engineering. Low-code development platforms are replacing traditional ETL tools, but it's still important for data engineers to have a working knowledge of them. In particular, Informatica and SAP Data Services get used regularly in this space. 

Programming Languages

Data engineering involves quite a few languages, such as back-end languages, query languages, and specialized languages for statistical computing (Python, Ruby, Java C#; SQL; R). Python is often combined with R and SQL because it is general-purpose and powerful while being easy to use due to its extensive library. SQL serves as a standard language when conducting queries on relational databases central to many tasks. As for R - it’s especially popular among statisticians/data miners because it provides a software environment suited to those pursuits.


Application programming interfaces (APIs) are essential to any data engineering project. These REST APIs, in particular, provide reliable communication over HTTP. As such, they're vital to the success of any web-based tool. 

Data lakes And Warehouses.

Data warehouses and data lakes are also integral to successful data engineering tasks – business analysts utilize computer clusters to manage large datasets and gain insights from them. Data frameworks such as Spark and Hadoop facilitate this process, making it easier to prepare and process large amounts of data for mining, analysis, and more.

What Are The Data Engineering Frameworks?

Data engineering is a growing field, and mastering the right data engineering frameworks is essential for success. The following six frameworks are some of the top open-source tools in your arsenal: Apache Hadoop, Apache Spark, Apache Kafka, Apache Airflow, Apache Flink, and Apache Beam. 

Apache Hadoop offers distributed storage and processing of large datasets. You can look to Spark or Kafka for real-time streaming and machine-learning tasks. Airflow enables workflow automation, while Beam is ideal for batch and real-time stream processing. Flink will provide high throughput and low latency capabilities, whatever your needs may be. With these jars of tools in your pocket, you'll be well-equipped to handle any data engineering challenge that comes your way!


Data engineering is an invaluable component in helping businesses reach their goals. Data engineers employ powerful techniques and tools to get data ready for analysis. Of course, raw data is only very useful if it's organized and readable, which is where data engineering comes in. That's why Agilisium is well equipped to provide quality data engineering solutions ; we understand the importance of such a specialized area to help your organization progress and grow.

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