7 Gen-AI Tools to Evaluate and Analyse as a Data Engineer

In today’s data-driven world, effective data analysis is crucial for making smart decisions. Organisations rely on data engineers to find valuable insights from large datasets. However, handling huge and complex data can be challenging. That’s where AI-powered tools come in—they transform data analytics by automating tasks, improving accuracy, and speeding up workflows. 

Recent stats show that 53% of companies use AI and machine learning to improve data analysis, according to Forbes. Moreover, the global AI market is expected to grow to $267 billion by 2027, driven largely by data analytics. 

In this article, we explore eight advanced AI tools that help data engineers extract useful insights more efficiently. 

GitHub Copilot: Generating Code Across Languages 

GitHub Copilot is an advanced AI tool that acts as a pair programmer, helping developers write and understand code efficiently. It generates code snippets and complete functions across various programming languages and frameworks. This is particularly useful for data engineers who need coding support, especially when working with unfamiliar languages or frameworks. 

Additionally, GitHub Copilot is adept at data preprocessing tasks, providing suggestions on handling missing values in data frames. This simplifies the process of cleaning data, allowing developers to concentrate on higher-level data analysis tasks. By harnessing AI capabilities, GitHub Copilot boosts productivity and improves the quality of code, serving as a versatile tool in modern software development and data engineering workflows. 

Julius AI – Simplifying Data Analysis 

Julius AI simplifies data analysis by using advanced machine learning to interpret complex datasets. It identifies patterns, outliers, and correlations, helping data engineers extract valuable insights efficiently from structured databases or unstructured text. 

The tool provides an easy-to-use interface for creating interactive charts and graphs without needing extensive coding knowledge. Julius AI democratises data analysis by automating tasks like data preprocessing and model selection, making it accessible even for non-data scientists. 

In practical applications, Julius AI enhances marketing campaigns by optimising ad spend based on customer behaviour insights. In healthcare, it aids researchers in predicting disease outcomes from patient records. Supply chain managers use it to improve efficiency by analysing demand patterns, while financial teams rely on it for detecting anomalies in transactions to prevent fraud. 

DataLab: Where Data Meets Conversation 

DataLab simplifies data analysis with its AI-powered data notebook. It features a unique chat interface driven by generative AI, allowing users to converse with their data. You can ask questions, explore patterns, and iterate towards insights as if collaborating with a skilled colleague. 

The platform offers a powerful integrated development environment (IDE) supporting R, Python, and SQL. It comes equipped with essential data science packages for coding, reviewing, and executing analyses seamlessly. 

DataLab effortlessly transforms analyses into visually appealing, shareable reports, facilitating clear communication of findings to stakeholders. Its intuitive AI Assistant guides users through data exploration, code generation, and error correction, streamlining the journey from raw data to actionable insights. This makes data analysis accessible and efficient, even for those new to the field. 

DataCode AI: Streamlining Development Processes 

DeepCode AI stands out as an AI-powered code review tool designed to enhance code quality and security across various programming projects. It conducts thorough analyses of codebases, swiftly pinpointing bugs, vulnerabilities, and performance bottlenecks. The tool provides real-time feedback, allowing developers to address issues promptly during the coding phase. Supporting multiple programming languages and integrating seamlessly with popular Integrated Development Environments (IDEs), DeepCode AI accommodates diverse development environments. It proves especially beneficial for data engineers aiming to streamline their development process by ensuring robust code quality and fortifying security measures proactively. By leveraging AI, DeepCode AI empowers teams to maintain high standards of reliability and efficiency in their software development efforts. 

Tabnine: Providing Accurate & Context-Aware Code Suggestions 

Tabnine is an AI-powered code completion tool designed to predict your next coding steps by analysing context and past code patterns. It significantly boosts coding speed by providing accurate and context-aware code suggestions. Supporting multiple programming languages, Tabnine seamlessly integrates with a variety of Integrated Development Environments (IDEs), enhancing its versatility across different development setups. This makes it particularly beneficial for data engineers seeking to expedite coding processes without sacrificing accuracy. 

Apache MXNet: Bringing Scalability, Efficiency & Flexibility 

Apache MXNet is an open-source deep learning framework known for its efficiency and flexibility. It supports multiple programming languages and excels in scalability, making it ideal for handling large-scale neural networks effectively. Data engineers utilise MXNet extensively for building and training machine learning models, particularly those that incorporate complex deep learning architectures. Its robust capabilities empower developers to implement cutting-edge algorithms and perform advanced computations efficiently. MXNet’s flexibility allows for seamless integration into various development environments, ensuring compatibility across diverse computational setups. 

TensorFlow: Processing Across Multiple Devices & Servers 

TensorFlow is a widely-used open-source deep learning framework renowned for its industry adoption and robust features. It offers high-level APIs that simplify the development of neural networks and other machine learning models. TensorFlow supports distributed computing, enabling efficient processing across multiple devices and servers. Data engineers rely on TensorFlow for a variety of tasks, from developing and training sophisticated machine learning models to deploying them in real-world applications. Its versatility and scalability make it particularly suitable for implementing complex deep learning algorithms and handling large datasets effectively. 

Merit’s Expertise in Data Aggregation & Harvesting Using AI/ML Tools 

Merit’s proprietary AI/ML tools and data collection platforms meticulously gather information from thousands of diverse sources to generate valuable datasets. These datasets undergo meticulous augmentation and enrichment by our skilled data engineers to ensure accuracy, consistency, and structure. Our data solutions cater to a wide array of industries, including healthcare, retail, finance, and construction, allowing us to effectively meet the unique requirements of clients across various sectors. 

Our suite of data services covers various areas: Marketing Data expands audience reach using compliant, ethical data; Retail Data provides fast access to large e-commerce datasets with unmatched scalability; Industry Data Intelligence offers tailored business insights for a competitive edge; News Media Monitoring delivers curated news for actionable insights; Compliance Data tracks global sources for regulatory updates; and Document Data streamlines web document collection and data extraction for efficient processing.

Key Takeaways 

Importance of AI in Data Analysis: AI-powered tools like Julius AI and DataLab are transforming data analysis by automating tasks, improving accuracy, and speeding up workflows, addressing the challenges posed by large and complex datasets. 

Tool Diversity and Use Cases: Each tool serves specific purposes—Julius AI enhances marketing and healthcare analytics, DataLab simplifies data exploration and reporting, and DataCode AI (DeepCode AI) strengthens code quality and security in software development. 

Versatility of AI in Development: GitHub Copilot aids in code generation and data preprocessing, while Tabnine offers context-aware code suggestions, showcasing AI’s role in enhancing productivity and accuracy in coding tasks. 

Frameworks for Machine Learning: Apache MXNet and TensorFlow are pivotal for building and training machine learning models, emphasising scalability, efficiency, and flexibility across diverse programming environments. 

Industry Adoption and Growth: The adoption of AI in data analytics has surged, with 53% of companies leveraging AI and machine learning to bolster their data analysis capabilities, as reported by Forbes. This growth underscores AI’s pivotal role in modern software development and data engineering. Additionally, the global AI market is forecasted to reach $267 billion by 2027, driven largely by advancements in data analytics. These statistics underscore the transformative impact of AI technologies, empowering organisations to enhance productivity, extract actionable insights efficiently, and stay competitive in the digital era.