Back
Share
Table of Contents
Insights from a Data Engineering Leader Adopting Data Lakehouses AI-Augmented BI Deploying LLM-friendlier Data Stacks Implementing RAG Data Pipelines Setting up Event-Driven Systems Using Data Meshes Using Data Fabrics AI-Powered Data Management & Governance Data Engineering – A Prospective Career Choice!
Mar 2024
5 Min Read
8 Best Data Engineering Skills for 2024 and Beyond
For all the data enthusiasts around the world, 2024 is your year to plunge into the Data Engineering realm as it stands out as one of the most sought-after professions. Poised at the crux of all modern business operations, Data Engineering can help aspiring professionals create a data-driven reality, thus outshining as valuable assets to their organization. Data engineers, indeed, stand at the forefront of all effective data strategy operations, channelizing the influx of unstructured and structured data into the business systems.
Insights from a Data Engineering Leader
Delving deep into the nuances of Data Engineering, Suganthi Kandasamy, Director - Data Engineering, Cloud Destinations, strongly believes that this is an emerging field with promising scope and career opportunities for budding professionals. As the doyen of the Software Engineering and Data Engineering fields with a combined experience of 20+ years, she asserts, “As AI and its usage spread its wings encompassing all sectors of technology, Data Engineering in 2024 shall be seen to reap benefits out of it and outshine as the most promising career path”.
She adds, “The trending subject areas will be Large Language Model (LLM)-friendlier data stacks, Retrieval-Augmented Generation (RAG) data pipelines, data lakehouses, and AI-Augmented BI. We can already witness data engineers diversifying their data engineering skillsets to transform and reflect as specialists in cloud platform and Git operations”. Suganthi is a dynamic leader with an undivided passion for work, a keen strategic mindset, and a leadership style that perfectly balances empathy and decisiveness. She has been outclassing as a beacon of inspiration for her people to collaborate and unite the energy that they hold to realize their goals both at the personal and professional levels.
Considering Suganthi’s profound insights and conducting thorough research about the present Data Engineering industry, we have curated 8 topmost data engineering skills that any aspiring data enthusiast must possess. Keep reading further to understand these skills in detail!
1. Adopting Data Lakehouses
Known as easy-to-use and affordable data storage solutions, Data Lakehouses have been witnessing an increased demand in recent years as compared to cloud data warehouses. These data lakehouses combine the functionality of data warehouses with the flexibility and scalability of data lakes, thus facilitating unified data access, agility, and cost efficiency for data analytics.
With additional benefits such as reduced data movement and data copies, query acceleration, and much more, data lakehouses are becoming the primary architecture for delivering analytics. Further, in the current AI era, data lakehouses play a crucial role in improving AI-driven data management, governance, and compliance, delivering utmost value as opposed to traditional warehouses and data lakes.
2. AI-Augmented BI
Artificial Intelligence (AI) has been making great strides in the Business Intelligence (BI) space with its extensive capabilities of processing large silos of data, identifying patterns, and making smart predictions for analytics. The powerful integration of AI and BI tools can assist businesses in unlocking diverse opportunities in the data engineering realm by speeding up data analysis, devising business insights, and optimizing business outcomes. In addition, leveraging Advanced Analytics, Predictive Analytics, Natural Language Processing, Personalized Recommendations, Data Visualization, and various other applications and technologies further helps in churning the best of AI-augmented BI. But with the rising concerns around data security and privacy, it is essential for AI-Augmented BI tools to incorporate more robust security practices to ensure that the data is not just insightful but also protected.
3. Deploying LLM-friendlier Data Stacks
The integration of LLMs into modern data stacks has paved the way to advanced natural language processing, enhanced customer experiences, and significant business outcomes. It is quite evident that these LLMs have made processing and analyzing large silos of data easier, quicker, and more accurate, besides elevating the accuracy of natural language processing and text analytics. This allows data engineers to vest their sole focus on high-level analysis and decision-making.
LLMs also play a great role in querying databases and improving the quality of predictive models by identifying correlations and patterns. Clearly, the future of data stacks lies in the integration of LLMs, and it is imperative for organizations to embrace this trend to stay ahead of the data landscape.
4. Implementing RAG Data Pipelines
RAG Data Pipelines represent an advanced approach to natural language processing tasks by integrating information retrieval techniques with natural language generation models. These data pipelines perform a stream of operations, including data collection and preprocessing, indexing and retrieval, query processing, text generation, post-processing and evaluation, and monitoring and maintenance, offering extensive benefits. Some of these benefits include increased relevance, contextual understanding, handling of long-text scenarios, flexibility, adaptability, scalability, efficiency, interpretability, and much more. Besides, data redundancy, bias, and inaccuracy are reduced to a significant degree through these RAG data pipelines, thus making them a compelling choice for various natural language processing tasks and applications.
5. Setting up Event-Driven Systems
Event-driven Systems are characterized by their asynchronous, event-based communication patterns, offering high scalability, responsiveness, and real-time data processing. These systems are highly beneficial to both users and developers of modern, distributed applications, thereby enabling them to develop more responsive and agile software systems that can swiftly adapt to evolving demands and present interactive user experiences. Especially with the convergence of AI, the Internet of Things (IoT), Big Data, and Cloud Computing, event-driven systems will become an integral part of the data engineering landscape in the years to come. Some further advancements in event-driven systems entail dynamic scaling in cloud and hybrid environments, data consistency in asynchronous communication, complex system integration, robustness in high-volume data streams, and interoperability between diverse systems.
6. Using Data Meshes
Data Mesh stands out as the most crucial part of data and digital transformation in most organizations today. It also outshines as a business-driven strategy that most teams can leverage to build and deliver top-notch data products. Right from better data quality, data governance, agility, scalability, data access to decision-making, the implementation of data mesh delivers these wide range of benefits and much more to organizations. Especially with the rise of data lakehouses, the data mesh strategies have been garnering huge success.
7. Using Data Fabrics
Data Fabric has been revolutionizing the data management space by integrating diverse capabilities such as supporting multi-cloud deployments and hybrid cloud, implementing metadata management, deploying AI, and much more. Without any human intervention, data fabric software and platforms can detect differences between the expected and actual data patterns, thus identifying and resolving a spectrum of data problems and issues.
With the further adoption of Machine Learning (ML), data fabric platforms offer traceability and transparency of metadata analytics, thus delivering more value to the consumers.
8. AI-Powered Data Management & Governance
In the current AI era, solid data management and data governance stand out as the primordial aspects of managing AI models. The deployment of data lakehouse offers extensive data security and governance besides improving AI-powered data management and speed of AI data stack. In addition, AI and ML can be leveraged to reduce costs, optimize ETL/ELT, and ease the work of data engineers. As more AI-enabled applications are becoming prevalent in the recent times, data quality, management and governance are becoming even more important.
Data Engineering – A Prospective Career Choice!
The road ahead for data engineering in the coming years looks quite prospective for individuals with a relentless passion for working with data, a strong technical aptitude, and a desire to make a meaningful impact in today's data-driven world! Due to the growing demand, constantly evolving Big Data technologies, increasing focus on data quality and governance, burgeoning opportunities in AI and ML, rising need for cross-disciplinary skills and development, and many other reasons, learning data engineering and building relevant skillsets is undoubtedly a smart move. Beyond just dealing with numbers and computers, tune your skillsets to evolve into a data engineer who can help people and businesses function better!
Linkedin
