Data engineering tools have become increasingly crucial for organizations to manage and analyze large data sets. With 2023 well underway, it’s important to look at the tools recommended by industry experts to ensure that your data operations are efficient and effective.
The data-driven businesses are hiring engineers to leverage the power of data. Data engineers are responsible for building data pipelines required for infrastructure designs. The best data engineering tools combine programming languages and data warehouses to collect and analyze large data sets.
Whether you’re a business planning to incorporate data engineering tools or an aspiring engineer, you have come to the right place. In this data engineering interview series, we will help you learn how businesses and engineers can prepare for the future with the right tools.
Expert Predictions About Top Data Engineering Tools
The global big data market is predicted to reach US 103 billion dollars by 2027, more than double its expected market size in 2018. That is why businesses seek solutions and tools to utilize big data to improve their internal business operations.
Before we talk to the experts, let us understand what data engineering is.
Data engineering is the process that involves extracting, transforming, and loading data into a data lake or data warehouse. Using engineering and analytical tools primarily aims to solve business problems using big data.
To help our readers understand the future of data engineering and the tools involved in analysis, the Inferenz Tech team conducted an interview with Ms. Aparna Varma. She holds years of experience as Microsoft Certified Technology Specialist and will shed light on hidden facts about data engineering in this interview.
Internal Team: Hello, Ms. Aparna. Thank you for your valuable time.
Ms. Aparna: It is my pleasure to be here.
Internal Team: So, Ms. Aparna, data engineers today are confused about choosing the right data engineering tool and programming language. Many experts prefer Python as it is highly flexible and easy to use. According to you, should data engineers learn only Python?
Ms. Aparna: Data engineers should not limit themselves to learning only Python. While Python is a widely used programming language in data engineering and is commonly used in data science and machine learning, there are other programming languages that are also useful for data integration, such as SQL, Java, and Scala. It is beneficial for data engineers to be familiar with multiple programming languages, as different tools and technologies may require different languages. Additionally, learning multiple languages can broaden a data engineer’s understanding of different programming concepts and paradigms, making them more versatile and adaptable in their field.
Internal Team: We agree with you. Data engineers must learn different skills and tools to stay ahead of the competition. So, according to you, which data engineering tools will be leveraged by businesses in 2023 and why?
Ms. Aparna: It is likely that businesses in 2023 will continue to leverage a variety of data engineering tools in order to effectively manage and analyze their data. Some of the most commonly used data tools include:
- Apache Hadoop: Hadoop is an open-source framework that allows businesses to store and process large amounts of data on commodity hardware. It is widely used for big data processing and is likely to continue to be a popular choice in 2023.
- Apache Spark: Spark is a fast, general-purpose cluster-computing framework that can be used for data processing, machine learning, and graph processing. It is known for its speed and ability to handle large data sets, making it a valuable tool for businesses in 2023.
- Apache Kafka: It is a distributed streaming platform that can be used for data integration and real-time data processing. It is a popular choice for businesses that need to process data in real time and is likely to continue to be widely used in 2023.
- Apache Airflow: Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. It is commonly used for data pipeline management and is likely to be a popular choice for businesses in 2023.
- TensorFlow: It is an open-source machine learning library that can be used for various tasks, including natural language processing, image recognition, and predictive modeling. It is likely to continue to be a popular choice for businesses in 2023 as machine learning becomes increasingly important for businesses.
These tools are likely to be leveraged by businesses in 2023 as they provide the necessary infrastructure and functionality to handle large amounts of data, process it in real-time, and perform advanced analytics. Additionally, the open-source nature of these tools makes them more accessible and cost-effective for businesses of all sizes.
Internal Team: Thank you for sharing the best data engineering tools suitable for small and large enterprises. However, many data engineers use the three most common tools to extract from multiple data sources: PyTorch, TensorFlow, and Keras. According to you, which tool is the expert recommendation and why?
Ms. Aparna: Sure. It ultimately depends on the specific use case and the individual’s expertise. However, in general, experts recommend the following:
- PyTorch: It is recommended for researchers and developers who prefer a more dynamic and flexible framework. PyTorch allows for easy experimentation and has a more intuitive API compared to TensorFlow. Additionally, it has built-in support for CUDA and can easily run on GPUs.
- TensorFlow: It is recommended for developers and researchers who are looking for a more production-ready framework. TensorFlow has a more robust ecosystem and is better suited for deploying models in production environments. Additionally, it has a wide range of tools and libraries for monitoring and debugging models.
- Keras: It is recommended for beginners and developers who are looking for a simple and easy-to-use framework for building deep learning models. Keras provides a high-level abstraction for building models and is built on top of TensorFlow or PyTorch. It allows for rapid prototyping and is great for quickly building and testing models.
Ultimately, the choice of the best framework depends on the specific use case and the individual’s expertise. It is recommended to try out multiple frameworks and see which one works best for a given task.
Internal Team: Many engineers and experts say that Python data engineering tools will grow in 2023 and beyond. What is your take on it? What do you think about the growth of Python data engineering tools?
Ms. Aparna: Experts predict that Python data engineering tools will continue to grow in popularity and usage due to their ability to handle large amounts of data and perform complex data analysis tasks. Python is a versatile programming language widely used in data science and machine learning, and its libraries and frameworks for data engineering, such as Pandas, Numpy, and Dask, are well-established and widely adopted.
Additionally, the growth of big data and cloud computing has increased the demand for data engineers who can work with large datasets and distributed systems, which Python is well-suited for. Overall, experts believe that Python will continue to be a key tool in data engineering and will see continued growth in the field.
Internal Team: So, here is the last question. Could you please share your thoughts on SQL? Is SQL and database knowledge enough to be a successful data engineer in 2023?
Ms. Aparna: SQL and database knowledge are important skills for a data engineer, but they are not the only skills needed to be successful in 2023. Other important skills for a data engineer include:
- Programming skills: Data engineers often work with multiple programming languages like Python, Java, and R to extract, transform, and load data into databases.
- Data warehousing: Understanding data warehousing concepts and technologies, such as data marts and data lakes, is important for data engineers to be able to design and implement data pipelines.
- Cloud computing: More and more companies are moving their data infrastructure to the cloud, so knowledge of cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure is important for data engineers.
- Data modeling and data governance: Data engineers need to have an understanding of data modeling and data governance to ensure that data is accurate, consistent, and accessible for analysis.
- Big data technologies: As the volume and variety of data continue to grow, data engineers will need to have knowledge of big data technologies such as Hadoop, Spark, and Kafka to handle large-scale data processing.
Overall, while SQL and database knowledge are important skills for data engineers, they will need to have a broad set of skills to be successful in 2023.
Internal Team: Thank you for sharing your knowledge with our readers, Ms. Aparna. We hope you’ve had a wonderful time here.
Leverage The Power Of Data Engineering Tools In 2023
As you see, data engineering tools play an important role in managing large amounts of data. Ms. Aparna has shared some of the best data engineering tools that help in data storage, data transformation, and data management. In addition, the right data integration tool allows data engineers to analyze data and build a robust, responsive data analytics infrastructure.
If you plan to choose the best data engineering tools to analyze and manage massive amounts of data, contact Inferenz experts. The data analysts and engineers team will help you use the maximum power of stored data and solve business problems.
Quick Recap: Data Engineering Tools For 2023
Using specialized tools, data engineers build data pipelines and produce business intelligence/data visualization reports. The primary role of BI tools in the modern data stack is to make data-informed decisions and improve operational efficiency.
- Instead of learning only the Python programming language, data engineers should focus on upskilling and learning tools like SQL, Java, and Scala.
- Some of the top data engineering tools for businesses in 2023 include Apache Hadoop, Apache Spark, Apache Kafka, Apache Airflow, PyTorch, and TensorFlow. However, businesses should focus on understanding their business goals and choosing the tools that align with their needs.
- Experts predict that Python data tools like Pandas, Numpy, and Dask will keep growing in 2023. Python data tools are versatile as they can handle large amounts of cloud-based data.
- Aspiring data scientists and engineers should have programming skills, data warehousing, cloud computing, big data technologies, data modeling, data governance, SQL, and database knowledge.
We hope this interview series will help you understand the tools for data engineers. If you’re still confused about choosing data engineering tools to manage cloud data from multiple sources, contact Inferenz experts.