Data is now an indispensable asset for modern-day organizations. From social media to forecasts, companies have all the data they need to create a holistic view of their operations. By 2028, global data creation will grow to more than 394 zettabytes. With such a huge dependence on data, organizations equipped with proper data engineering can access datasets at any time in a reliable, safe, and convenient manner. 

In this article, we shall explore the functionality and key elements of data engineering, along with tools and emerging trends for data engineering. 

Before moving forward, let’s understand what data engineering means. 

What is Data Engineering?

Data engineering is the discipline that creates data collection, transformation, storage, and analysis for large datasets. It allows data scientists to gather valuable information from their raw, chaotic data. Data engineering also includes data quality and access assurances. 

Data being abundant is also prone to quality issues. Businesses lose US$3.1 trillion due to poor data quality. Therefore, organizations' dependence on data requires data engineering to govern data management for downstream use, including analysis, machine learning, and forecasting. 

With that in mind, let’s understand the cooperation of data engineering. 

How does Data Engineering Work?

Data engineering outlines the overall design and development of data pipelines that convert raw data into structured assets while retaining their quality and reliability. 

Here’s a rundown on three phases of operation for data engineering:

1. Data Ingestion

Data ingestion is the movement of data from multiple sources into a particular ecosystem. These sources can be data lakes. Warehouses, cloud computing platforms, and databases. Data engineers employ Application programming interfaces (APIs) to connect data points to their respective pipelines effectively. 

2. Data Transformation

In this phase, data engineers remove eros and duplicate entries from the infected data. It maintains the data's overall reliability for executives and machine learning (ML) engineers. 

3. Data Serving

Data collection and processing are completed successfully, and end users receive the data. Some common data-serving methods include data modeling, visualization, and automated reporting systems. 

6 Key Elements of Data Engineering

The definition of data engineering is incredibly broad. To understand the discipline clearly, consider the following elements of data engineering.

1. Data Collection 

Data collection involves the process of extracting data in different formats from multiple sources. These datasets include everything from structured data in relational databases to semi-structured data such as website content and email. 

2. Data Storage

Data engineers use ingested data and design necessary storage solutions, such as cloud data warehouses, data lakes, or NoSQL (non-structured query language) databases. Depending on organizational frameworks, data engineers are also responsible for appropriate data management across such storages. 

3. Data Processing

Transforming raw data into usable format involves necessary data cleansing to remove inaccuracies and standardize data consistency to facilitate appropriate analysis. It is, therefore, one of the primary elements of data engineering. 

4. Data Integration 

Data extracted from multiple sources needs tools to combine it into a unified view for analytics. Implementing ETL (Extract, Transform, Load) processes for data extraction and loading into central repositories depends on effective data integration.

5. Data Quality and Governance

Another important element of data engineering is ensuring that data is accurate and accessible. Data engineers use data validation checks and implement data governance policies to monitor the overall quality of the data and prevent errors and inconsistencies. 

6. Security and Compliance

A key element of data engineering is ensuring that organizational data comply with industrial privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPPA). 

Your raw data has no value without proper data engineering.

Now, with a better understanding of some of the key elements of data engineering, let’s look at some tools data engineers can use to optimize data workflows. 

Tools for Data Engineering

Data engineers must be proficient in numerous tools and techniques to streamline data flow, storage, management, and quality across multiple platforms. 

Here are some of the common tools popular in data engineering:

1. Data Storage Solutions

• Relational Databases: They organize data according to a system with predetermined relationships. Data is put in rows and columns, showcasing the relationship between data points. This structure offers storage for complex data queries. Relational database management systems (RDBMS) are prevalent among data engineers, and MySQL and PostgreSQL are the two leading open-source RDBMS options. 
• Data Warehouses: Data warehouses collect and standardize data from multiple sources into a single source. It consists of a three-tiered structure: the bottom layer for data storage, the middle layer for fast queries, and the upper layer for the user. Data warehouses enhance overall data quality and offer valuable insights for businesses' strategic decision-making.
• Data Lakes: Data Lake offers a freeform data management solution for both structured and unstructured datasets. In contrast to data warehouses, they are cost-efficient and flexible. However, without proper management, data lakes can soon become swamps of huge unstructured data that are difficult to navigate. Several data lakes are built on the Hadoop product ecosystem and have real-time data processing solutions like Apache Spark and Kafka.
• NoSQL Databases: They enable data engineers to develop storage solutions that do not depend on traditional models. They offer flexibility and better scalability than SQL-based relational databases. 

2. Data Pipelines

• ETL (extract, transform, load): ETL pipelines streamline the storage and retrieval of data within databases. The raw data is standardized with scripts and loaded into storage. ETLs are common with data integration methods. 
• ELT (extract, load, transfer): ELT pipelines extract raw data into central repositories through appropriate data transformation processes. In contrast to ETL pipelines, ELT pipelines are more flexible. 

A well-designed data pipeline is the core foundation of every data-driven decision.

3. Programming Languages

• SQL: Structured querying language is one of the most common database development and manipulation languages. It forms the basis for all relational databases. 
• Python: It offers a wide range of prebuilt modules to speed up several operations of data engineering processes. Python is the foundation of several user-facing applications. 
• Java: Java is a common choice for the backend of several data engineering pipelines. It is best for developing in-house data processing solutions. 

Future Trends in Data Engineering

1. Advanced LLMs

• Experts believe that the global Large language model (LLM) market will grow to US$259,8 million by 2030. 
• LLMs benefit data engineers with automated data ingestion, cleansing, and pipeline development. 
• Advanced LLMs also optimize foundation models for enhanced data security and building validation systems. 

2. Enhanced Data Observability

• Data observability empowers data engineers to streamline their AI development processes and avoid data-related roadblocks.
• Artificial intelligence (AI) models ensure data quality is prevalent, and 97% of IT professionals are adopting AI and big data solutions. Traditional data monitoring tools often fail to meet the demands of complex data, substantiating the role of AI-based data engineering models.  
• Data observability offers a proactive approach to data management to build reliable AI systems. 

3. DataOps

• DataOps uses a continuous feedback loop to streamline data delivery and foster collaboration between data teams and stakeholders. 
• DataOps proactively isolates and remediates issues to efficiently deploy data pipelines and extract a company’s data's full potential. 
• It is emerging as a valuable asset for data engineers and scientists, automating data workflows like data extraction and transformation. 

4. Data Vault

• Data lakes are revamping data analytics; however, managing large unstructured data is difficult, while data vaults offer flexibility and suitability of data. 
• Data vault enables fast data loading, ensuring analysts gain valuable insights from their raw data.  
• Data integrity is the priority, and fostering trust and transparency within an ecosystem makes data vaults indispensable for data engineering. 

The future of data engineering depends on businesses' ability to accept modern technologies to streamline their data workflows. Let’s see how QuartileX makes it possible.

Data Engineering with QuartileX

At QuartileX, we offer businesses the ability to transform unstructured data into actionable insights for optimal business performance. 

Here’s how we benefit businesses from our tailored data engineering solutions:

• Develop scalable data pipelines with efficient storage, integration, and quality. 
• We optimize ELT pipelines using cost-effective data transformation processes for data workflows. 
• Our customized data engineering solutions use industry-leading tools, such as Hevo Data and dbt, to minimize infrastructural overhead and optimize operations.

With an advanced data engineering solution in place, you can have complete control over your data and gain strategic insights from your raw data. Take a closer look at QuartileX’s cutting-edge data engineering services for a better hold of your data. 

Conclusion

Advanced data engineering solutions are emerging as a modern component of data science. Businesses looking to upscale their data workflows can use data engineering to maintain their data quality and compliance for secure long-term success. Emerging trends like data vaults, DataOps, and advanced LLMs are revamping data engineering, allowing businesses to stay ahead with confidence. 

At QuartileX, data engineers can build robust and scalable data pipelines to streamline their data management operations. With our end-to-end expertise, data engineering is made simple with effortless integration of advanced tools to maintain the overall reliability of data pipelines. 

Ready to take your data engineering journey to the next level? Talk to our data expert now to have a comprehensive discussion on streamlining your data workflows to become future-ready.