Round The Clock Technologies

Blogs and Insights

Key Data Engineering Tools and Technologies for 2024 

The amount of data businesses generate is exploding. By 2025, the global datasphere is projected to reach a staggering 180 zettabytes – that’s 180 trillion gigabytes! This “big data” presents a golden opportunity to extract valuable insights that can fuel strategic decisions, optimize operations, and drive innovation. But harnessing this power requires a specialized skillset: data engineering. 

Data engineering services empower organizations to collect, clean, transform, and store vast amounts of data. These services encompass everything from building robust data pipelines to architecting powerful data warehouses. By leveraging data engineering solutions, companies can utilize the true potential of their information assets. 

Statistics Shaping the Global Data Engineering Landscape

The demand for expert data engineering services and solutions is booming across the globe. A market report by Market Data Forecast predicts that the global big data and data engineering services market is projected to nearly double in size by 2029, reaching a staggering USD 169.9 billion. This translates to a significant 17.6% compound annual growth rate (CAGR) from 2024 to 2029. This impressive growth signifies the increasing demand for data engineering solutions as organizations grapple with managing and leveraging their ever-growing data volumes.

Moreover, with the market for data engineering solutions booming, a vast array of tools and technologies are vying for attention. Choosing the right tools can be a complex task, but it’s crucial for success. Factors like scalability, security, and ease of use all play a vital role. Selecting the most appropriate tools for specific needs ensures efficient data management, maximizes return on investment, and empowers businesses to effectively navigate the ever-growing data deluge. In this blog, we will explore the top 10 data engineering tools for 2024, their strengths, weaknesses, and how they can fit into your specific data wrangling needs. So, get ready to unlock the power of data!

Key Data Engineering Tools to Rule in 2024

The data engineering landscape is filled up with innovative tools, each catering to specific needs. Here’s a curated list of the top 10 data engineering tools you should consider in 2024:

Programming Languages


This versatile programming language reigns supreme in data engineering due to its readability, extensive libraries (like pandas and NumPy), and vast community support. ( Some of the key strengths and weaknesses of Python are listed below:


Readability and Learnability: Python’s syntax is clear and concise, making it easier to learn and understand compared to complex programming languages. This is especially beneficial for beginners or those transitioning from non-programming backgrounds. 

Extensive Libraries: Python empowers data manipulation and analysis with its rich library ecosystem. Libraries like pandas (data structures and analysis) and NumPy (numerical computing) offer powerful tools for cleaning, transforming, and exploring data. 

Strong Community Support: Python enjoys a large and active community of developers who contribute to libraries, create tutorials, and offer support through forums and online resources. This makes it easier to find solutions and troubleshoot issues. 


Performance for Computationally Intensive Tasks: While Python is versatile, it can be slower for tasks requiring heavy computations compared to compiled languages like C++. This may not be a significant issue for most data wrangling activities but keep it in mind for complex calculations.

Overall, Python is excellent for most data wrangling tasks due to its readability and extensive libraries. Pandas offer powerful tools for data cleaning, transformation, and analysis.

SQL (Structured Query Language)

Structured Query Language (SQL) is the building block for wrangling data in databases. Familiarity with SQL allows data engineers to efficiently extract, transform, and load data into various systems. ( Some of the key strengths and weaknesses of SQL are listed below:


The Foundation of Data Manipulation: SQL is the cornerstone of interacting with relational databases. It provides a standardized way to efficiently extract, transform, and load (ETL) data within these systems. 

Simplicity and Readability: SQL syntax is relatively simple and easy to learn, making it accessible to users with basic database knowledge. 


Limited Functionality for Complex Data Wrangling: While SQL excels at basic data cleaning and transformation, it may lack the power and flexibility needed for complex data manipulation tasks often encountered in wrangling diverse data sets. 

It is ideal for basic data cleaning and transformation tasks within relational databases. It may require additional tools for complex data wrangling.

Database Management Systems 


This open-source, object-relational database management system (DBMS) offers robust features, high performance, and scalability, making it a popular choice for data storage. ( Here are the key strengths and weaknesses of PostgreSQL:


Robust Features and Scalability: As an open-source object-relational database management system (DBMS), PostgreSQL offers a rich set of features for data storage and management. Its architecture allows it to handle large datasets efficiently with strong scalability for growing data volumes. 


Focus on Structured Data: PostgreSQL is primarily designed for structured data with a predefined schema. It may not be the most suitable choice for wrangling large amounts of unstructured data, such as social media feeds or sensor data. 

In short, it is suitable for data wrangling tasks involving structured data stored in PostgreSQL databases. It requires integration with Python or other tools for more complex transformations.


This NoSQL document database excels at handling large volumes of unstructured data, making it ideal for modern applications that generate diverse data formats. ( The strengths and weaknesses include: 


Handling Unstructured Data: MongoDB is a NoSQL document database that excels at storing and managing large volumes of unstructured or semi-structured data. Its flexible schema allows for diverse data formats without rigid pre-defined structures. 

Scalability and Performance: MongoDB offers good scalability for handling growing data volumes and provides efficient performance for queries involving unstructured data. 


Specialized Query Language: Unlike SQL, MongoDB uses a different query language called MQL (MongoDB Query Language). This can create a learning curve for users familiar with traditional SQL. 

Limited Functionality for Complex Cleaning and Transformation: While MongoDB can handle basic data wrangling tasks, it might require additional tools like Python libraries for more complex data cleaning and transformation. 

It is a good choice for wrangling large amounts of unstructured data. Consider combining with Python libraries for advanced data cleaning and transformation.

Big Data Processing Tools 

Apache Spark

This open-source, unified analytics engine tackles big data processing with lightning speed. Its in-memory capabilities enable real-time data analysis for faster decision-making. ( The key strengths and weaknesses of Apache Spark include:


High-Speed Big Data Processing: Apache Spark is designed for processing big data at lightning speed. Its in-memory capabilities allow it to perform calculations on data residing in memory, significantly reducing processing times. 

Real-Time Data Analysis: Spark’s processing speed opens doors for real-time data analysis. It enables businesses to gain insights from data streams as they arrive. 


Learning Curve: Spark has a steeper learning curve compared to some other tools on this list. Its distributed computing environment (cluster) requires some technical expertise to set up and manage for optimal performance. 

Overkill for Smaller Datasets: Spark’s true power lies in its ability to handle massive datasets. Using it for smaller data wrangling tasks might be overkill and introduce unnecessary complexity. 

Overall, it is an ideal choice for wrangling large, complex datasets that require real-time processing. May be overkill for smaller datasets.

Data Pipeline Management Tools 

Apache Kafka

This distributed streaming platform acts as the backbone for real-time data pipelines. It efficiently ingests, stores, and processes high-velocity data streams. ( Some of the key strengths and weaknesses of Apache Kafka are listed below:


Real-Time Data Pipelines: Apache Kafka shines as a distributed streaming platform designed specifically for building real-time data pipelines. It efficiently ingests, stores, and processes high-velocity data streams, making it ideal for applications that require continuous data flow analysis. 

Scalability and Fault Tolerance: Kafka offers excellent scalability to handle increasing data volumes. Its distributed architecture ensures fault tolerance, meaning the system can continue functioning even if individual nodes fail. 


Focus on Data Movement: Kafka primarily focuses on data ingestion and movement rather than data wrangling itself. While it can filter and transform data streams to some extent, it’s often used in conjunction with other tools for more comprehensive data cleaning and transformation. 

In a nutshell, this tool helps integrate real-time data streams into your data wrangling workflow.

dbt (Data Build Tool)

This open-source transformation framework streamlines the creation and maintenance of data pipelines. It enforces coding standards and simplifies data lineage tracking. ( The strengths and weaknesses include:


Streamlined Data Pipeline Creation: dbt acts as an open-source framework that simplifies the process of building and maintaining data pipelines. It enforces coding standards and best practices, making pipelines more reliable and easier to manage. 

Improved Data Lineage Tracking: dbt simplifies data lineage tracking, which refers to understanding the origin and transformation steps of data throughout the pipeline. This improves data quality and facilitates troubleshooting. 


Technical Knowledge Required: Effectively using dbt requires familiarity with SQL and data warehousing concepts. Users need to understand data modeling techniques and how to write SQL queries for data transformations. 

This tool is used in building and managing data pipelines that include data cleaning and transformation steps. 

Data Warehousing Tool 

Snowflake Data Warehouse

This cloud-based data warehouse offers exceptional scalability and flexibility. It allows data engineers to manage and analyze massive datasets without infrastructure headaches. ( The key strengths and weaknesses of Snowflake Data Warehouse include:


Scalability and Flexibility: Snowflake is a cloud-based data warehouse built for exceptional scalability and flexibility. It allows businesses to manage and analyze massive datasets without the burden of infrastructure setup and maintenance. Users can easily scale storage and compute resources based on their needs. 


Subscription-Based Pricing: Snowflake operates on a subscription model, which can be expensive for smaller datasets or businesses with limited budgets. The cost can scale proportionally with data volume and usage. 

It is suitable for storing and wrangling very large datasets. Integrate with Python or other tools for data cleaning and transformation before loading into Snowflake.

Workflow Orchestration Tools

Apache Airflow

This open-source workflow orchestration platform automates and schedules data pipelines. It ensures data tasks run smoothly and efficiently. ( The key strengths and weaknesses of Apache Airflow include: 


Workflow Orchestration: Apache Airflow is designed to automate and schedule data pipelines. It allows users to define dependencies between tasks and ensure they run smoothly and efficiently in the desired order. 

Scalability and Monitoring: Airflow offers good scalability to handle complex data pipelines with numerous tasks. It provides built-in features for monitoring pipeline execution and identifying any issues. 


Initial Setup and Configuration: Setting up and configuring Airflow can involve some initial effort. Users need to define workflows and dependencies between tasks, which requires technical knowledge. 

This tool is utilized in automating data wrangling workflows, especially for complex pipelines with multiple dependencies.

Containerization Tools 

Docker and Kubernetes

Docket and Kubernetes empower data engineers to package applications with their dependencies into standardized units (containers). This facilitates portability and streamlines deployment across various environments. (,


Portability and Streamlined Deployment: Docker and Kubernetes are containerization tools that allow packaging data wrangling scripts and libraries along with their dependencies into standardized units called containers. These containers are lightweight and portable, making them easy to deploy across different computing environments without worrying about compatibility issues. 

Efficiency: Containers are efficient as they share the operating system kernel with the host machine, reducing resource consumption compared to virtual machines. 


Complexity for Advanced Workflows: While containerization simplifies deployment, managing complex data wrangling workflows with multiple interconnected containers might require additional configuration and orchestration tools. 

These tools are particularly useful for deploying data wrangling scripts and libraries across different environments. This ensures consistency and simplifies the wrangling process. 

By understanding the strengths and weaknesses of these data engineering tools, you can make informed decisions about which ones best suit your specific data wrangling needs and project requirements.

Supercharge Your Data Strategy with Round The Clock Technologies

At Round The Clock Technologies, we understand the challenges and opportunities that come with big data. Our team of experienced data engineering experts possesses the expertise to leverage the latest tools and technologies, including those mentioned above, to help you navigate the ever-evolving data landscape. 

We offer a comprehensive suite of data engineering services and solutions, tailored to your specific needs. Here’s how we can partner with you: 

Data Engineering Consulting: Our experts will assess your current data infrastructure, identify areas for improvement, and recommend a data engineering strategy that aligns with your business goals. 

End-to-end Data Pipeline Development: We design and build robust data pipelines that efficiently collect, clean, transform, and deliver data to your analytics platforms. 

Data Warehouse Design and Architecture: Our team will help you architect a scalable and secure data warehouse to store and manage your massive datasets. 

Data Analytics Enablement: We ensure your data is readily available for analysis by integrating your data pipelines and warehouse with your preferred analytics tools. 

Contact us today and learn how we can help you in unlocking the valuable insights that fuel your data-driven success!