Data Science

Top 5 Data Engineering Trends to Watch Out in 2024

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Data Engineering is a field that specifically deals with creating or building systems that can easily handle large amounts of data. The handling will involve collecting, storing, and making the data useful for analysis. 

With the explosive growth of data worldwide, Data Engineering has become indispensable for businesses seeking insights and innovation. Yet, today, data’s sheer volume, variety, and velocity present challenges in extracting its value efficiently, and this is where data engineering meets AI.

AI revolutionizes data engineering by automating processes, optimizing workflows, and revealing insights in complex datasets. It transforms data cleansing, integration, predictive analytics, and anomaly detection, enhancing how organizations leverage their data, thus making data engineering more impactful than ever and giving rise to its various trends.

Hence, with this blog, we will discuss some of the most important & useful data engineering trends of 2024.

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Rapid Growth of Data Engineering in India

With the rise of digitalization after COVID-19, companies are generating massive amounts of data that must be managed, processed, and analyzed efficiently. This has created a high demand for skilled data engineers who can design and implement robust data pipelines and infrastructure.

  • India boasts a significant number of engineering undergraduates, coupled with a robust ecosystem for their training, contributing to the talent pool in this field.
  • Furthermore, with an increasing number of Indian companies acknowledging the significance of data-driven decision-making, substantial investments are being made in establishing robust data engineering teams.
  • Data-based solutions have been adopted across various sectors, including small and medium enterprises (SMEs), further driving the demand for data engineers.
  • Lastly, government initiatives aimed at digitizing India and investments in data centers nationwide have created a conducive environment for the growth of data engineering in the country.

Top 5 Data Engineering Trends To Watch Out in 2024

Data engineering is the cornerstone of contemporary data-driven businesses, enabling the gathering, storage, processing, and analysis of extensive datasets. As technology progresses at an unparalleled rate, it is essential for organizations aiming to maximize their data assets to remain updated on emerging trends.

Let’s discuss some of the top data engineering trends you should watch in 2024. 

#1 Rise of Real-time Data Processing

As the name indicates, Real-time data processing refers to the ability of systems to analyze & process data as soon as it is generated. 

Unlike traditional processes, which take multiple days or sometimes even weeks to provide outcomes from given information, real-time data can do the same quickly, saving data engineers time and effort. 

  • Working Mechanism

Although the exact working of real-time data processing will mostly depend on a company’s specific environment, however, the foundation will be the same. 

When bulk data is generated, the server fetches or receives it as input. The server or the system first determines how to process the input information in real-time. Depending on the answer, the system decides what to do.

Finally, the data may be sorted, stored, analyzed, reported, or checked depending on the given case or type to initiate the required actions. 


There are numerous benefits of real-time data processing in today’s market. 

  • Quick Decision-making: Through real-time data processing, data engineers can quickly analyze and process data and make informed decisions accordingly.  
  • Identify & Fix Issues Faster: Obviously, when engineers get outcomes from the generated data in no time. They will have the opportunity to identify & fix issues faster before it’s too late. 
  • Opportunity to remain competitive in the market: By quickly analyzing large amounts of data, both businesses & engineers can determine the latest trends in the market. Besides this, they can also know what’s working best for them and what’s not.


Real-time data processing can be used to perform several tasks, including:

  • Better Experience: Using real-time data analytics, brands can take the necessary steps to provide a top-notch user experience. 
  • Improved Engagement: A good customer experience automatically leads to maximum engagement and sales.  
  • Robust Security: Real-time data processing can efficiently detect flaws or glitches in the working environment, leading to better risk management. 

#2 Impact of Large Language Model (LLM)

Large Language Models are advanced machine learning models that efficiently understand and generate human language text. They are trained on many datasets, including blog posts, emails, research papers, journals, etc. For this reason, they are referred to as “large” language models. 

The LLM models greatly influence how data engineers or organizations approach routine tasks like content generation, integration, etc., for more strategic work.

  • Working of LLM

LLM uses deep learning (AI technology) to efficiently understand how the given content’s characters, words, phrases, and sentences are connected to convey the required information. 

Also read: Machine Learning vs. Deep Learning

After this, the models generate a human language response according to the programmer’s input. 


Some useful benefits of LLM models in the field of data engineering. 

  • Automation of repetitive tasks: These models can automate several repetitive tasks for data engineers, including writing data scripts, translating text into other languages, and many more. This will save a significant amount of time & effort and result in maximum accuracy. 
  • Deeper understanding of data: LLM models can quickly understand large amounts of documents, codes, and data dictionaries for further processing. This will greatly help data engineers to improve the ability of the projects they are working on. 
  • Quick Data Quality Improvement: The LLM model can also efficiently detect flaws or errors and quickly provide an improved version.   


LLM models are used in numerous daily tasks; some are discussed below. 

  • Summarizing: Summarizing is a widely known writing technique that involves condensing large amounts of information for better processing. Now, thanks to LLM, numerous AI-based tools can automatically summarize text online within seconds without compromising accuracy. 
  • Chatbots: LLM models are used in chatbots or virtual assistance to quickly and accurately respond to customers’ queries 24/7. 
  • Sentiment Analysis: Data engineers can use LLM models to efficiently understand the sentiment behind customer reviews, social media comments, etc. 

Also read: How To Perform Twitter Sentiment Analysis

#3 Cloud-Based Data Engineering

This is yet another data engineering trend to watch in 2024. It refers to a process of designing, building, and managing data workflows within a cloud-based environment. 

Cloud-based data management has solved the issue of engineers scaling up hardware storage devices or systems regularly.

Also read: Top Data Engineering Skills to Learn

  • Working

Cloud-based data engineering does not work because it revolves around pre-built cloud platforms such as Google Cloud, Amazon Web Services, and Microsoft Azure. As an engineer, you just need to sign up for any of these by filling out the form and starting to use cloud storage. 


Here are some of the most useful benefits of cloud-based data engineering. 

  • Maximized scalability: In traditional data engineering, storing huge amounts of data was one of the major challenges for organizations and engineers. Fortunately, that’s not the case, as several cloud-based platforms are now available that allow users to store data quickly and efficiently. 
  • Better flexibility: Most online cloud-based platforms offer several built-in features & services that engineers can use to perform tasks according to their specific needs and requirements. 
  • Security: Cloud-based service providers such as Google or Amazon offer robust security to ensure the stored information is completely safe from unauthorized access. 
  • Cost-effectiveness: Compared to on-site infrastructure expenses, cloud-based storage is more affordable. 


Below are some of the applications of cloud-based data engineering in different industries.

  • Finance & Banking: Cloud-based platforms can process large amounts of financial transactions, prevent fraud, and more. These will allow data engineers to quickly make informed decisions, resulting in streamlined workflows. 
  • Health Care: Cloud-based data engineering can also efficiently analyze medical reports, clinical data, and genomic information to accelerate research. This will improve patient care by personalizing the treatment process. 
  • Media Industry: Media companies can utilize cloud-based data to determine what content most audiences prefer and create fully tailored content for maximum audience engagement. 

#4 DataOps and DevOps for Data

DataOps is an approach or method for data analytics and data-driven decision-making based on the agile development methodology. It specifically aims to reduce the overall cost of data management, improve data quality, and deliver useful insights to businesses by creating data pipelines. 

On the other hand, DevOps is a software development approach that aims to remove roadblocks to faster software delivery. 

Both DataOps & DevOps are considered trends in the field of data engineering. 

  • Working Approach

The working mechanisms of DataOps and DevOps differ slightly. Below are the common working steps of both these data approaches. 

  • Collaboration: In both these approaches, the silos are broken down into data engineers, analysts, and scientists. This ensures everyone is on the same page. 
  • Automation: In DataOps, repetitive tasks like data ingestion, transformation, etc., are automated to save time and effort. In contrast, routine tasks in the development and testing phases are automated in DevOps. 
  • Data Quality & Governance (Only for DataOps): DataOps requires applying data governance practices to ensure data is credible and relevant. 
  • Monitoring: Finally, both data approaches involve continuously monitoring the ongoing work to avoid any mistakes or mismanagements. 


Below are the benefits of DataOps & DevOps. 

  • DataOps Benefits: 
  1. Faster time to insights: By automating repetitive tasks for better workflows, DataOps reduces the time to get valuable insights from raw data. 
  2. Reduced chances of errors: DataOps involves proactively monitoring data pipelines to identify and fix issues before they cause significant damage. 
  • DevOps Benefits: 
  1. Maximized collaboration: DevOps encourages communications between all the data engineers and analysts for better workflow. 
  2. Better control and testing: Engineers can achieve better control and create quality software by continuously testing it during development.


Both these approaches or methods are used in numerous tasks related to data engineering, including: 

  • DataOps Applications:
  1. Data Pipeline Arrangement: Numerous advanced tools on the Internet help engineers automate data flow throughout the pipeline.  
  2. Data Quality Management: DataOps involves regular quality checks to ensure that everything is being performed according to the requirements or not. 

Also read:

  • DevOps Applications:
  1. Infrastructure as code (IaC): DevOps requires managing the software infrastructure through code; this ensures maximum consistency, reliability, and control. 
  2. Continuous Integration & Delivery: By automating the testing and development process of software creation, DevOps ensures quick delivery within the given timeframe. 

#5 Data Governance

This is the final data engineering trend that we will discuss today in this blog. Data governance is a process that refers to efficiently managing the availability, usability, integrity, and security of data within a system. 

Earlier, data engineering was mainly focused on building and maintaining pipelines. However, data governance has added multiple layers to this process: how data is used and managed. 

However, it is important to note that the primary purpose of data governance is to ensure that the information being used for analysis is high-quality and perfectly meets regulatory requirements.  

  • Working Approach

Data governance works hand-in-hand with data engineering. Below, we have explained the working approach. 

  • Setting the standards: It first establishes different standards/policies to efficiently guide the engineers in collecting, storing, transforming, and managing data. 
  • Taking steps to ensure quality and consistency: Data governance involves different practices, such as validation, lineage tracking, and more. These practices greatly assist engineers in identifying inconsistencies in the data. 
  • Security: It also involves setting security protocols for engineers who want to access, modify, or encrypt the saved data. The main reason behind setting these protocols is to prevent the risk of data breaches. 


Here are some of the notable benefits of data governance. 

  • Enhanced decision-making: With good data governance, engineers will have full confidence in the quality of their data, allowing them to make informed decisions about further analysis. 
  • Maximum ROI: Data is a valuable asset. By ensuring its accuracy, reliability, and consistency, engineers can increase the chances of achieving the required organizational objectives, such as better sales. 
  • Collaboration: Data governance encourages clear communication between all the engineers to avoid confusion. 


There are multiple applications of data governance, some of which are – 

  1. Retail: Data governance efficiently defines the data quality expectations for customer information. This then assists in ensuring that the information used for targeted marketing campaigns is accurate & up-to-date. 
  2. Government Agencies: Data governance also ensures that government departments handle citizens’ critical data responsibility for maximized privacy. 

Key Skills & Tools For Data Engineers

Data engineering is a field that requires a blend of technical skills, domain knowledge, and tools to manage and manipulate large datasets effectively. Here are some important skills and tools commonly used by data engineers:

  • Data Processing Frameworks

Hadoop: An open-source framework for distributed storage and processing of large datasets across clusters of computers.

Spark: A fast and general-purpose cluster computing system for big data processing.

Hive: A data warehouse infrastructure built on top of Hadoop for querying and analyzing large datasets stored in Hadoop files.

  • Data Warehousing

Understanding of principles and practices of data warehousing, including ETL (Extract, Transform, Load) processes, data modeling, and schema design.

  • Data Analysis and Visualization

Tableau: A powerful data visualization tool for creating interactive and shareable dashboards.

Data Analysis: Proficiency in analyzing datasets to extract meaningful insights and trends.

  • Machine Learning

Understanding of machine learning concepts and techniques for implementing predictive models and algorithms on large datasets.

  • Programming Languages

Python: Widely used for data manipulation, scripting, and building data pipelines.

SQL: Essential for querying and manipulating data stored in relational databases.

  • Big Data Technologies

PySpark: Python API for Apache Spark, used for processing large-scale data.

Big Data: Knowledge of concepts and technologies related to managing and analyzing massive volumes of data efficiently.

  • Cloud Platforms

  1. GCP (Google Cloud Platform)
  2. AWS (Amazon Web Services)
  3. Azure (Microsoft Azure)

These platforms offer various services for data storage, processing, and analytics in a scalable and cost-effective manner.

key tools for data engineers

key skills for data engineers

Salary of Various Data Science Job Roles 2024

In India, salaries in Data Science vary based on roles and responsibilities, fluctuating annually due to various factors such as market trends, emerging tools, evolving job roles, required skills, economic conditions, and more. Here’s a breakdown of the base pay associated with different data science roles.

  • Data Scientist: 12.5 LPA
  • Data Analyst: 10 LPA
  • Deep Learning Engineer: 10 LPA
  • Data Engineer: 8- 10 LPA
  • Software Developer: 8 – 10 LPA
  • Big Data Engineer: 10 -12 LPA
  • Data Architect: 12.3 LPA


In 2024, the landscape of data engineering has evolved significantly, driven by the relentless surge in data volume and complexity. As businesses increasingly rely on data to drive insights and innovation, the role of data engineering has become more indispensable than ever before.

However, amidst these challenges, the integration of AI technologies has emerged as a beacon of hope, revolutionizing data engineering processes and unlocking unprecedented opportunities. From automating mundane tasks to uncovering valuable insights hidden within vast datasets, AI is reshaping how organizations leverage their data.

As we move forward into 2024 and beyond, accepting these trends and technologies will be essential for organizations to stay competitive and unlock the full potential of their data assets.

Nidhi is currently working with the content and communications team of AnalytixLabs, India’s premium edtech institution. She is engaged in tasks involving research, editing, and crafting blogs and social media content. Previously, she has worked in the field of content writing and editing. During her free time, she indulges in staying updated with the latest developments in Data Science and nurtures her creativity through music practice

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