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The Future of Data Engineering Key Insights from the Summit

I had the privilege of speaking and actively participating in thought-provoking discussions in the recently concluded Data Engineering Summit 2023. In this article, I share key insights from my own talk, as well as my takeaways from the keynotes and engaging conversations I had with fellow data enthusiasts at the summit.

  1. Smart Data Engineering is flipping traditional approaches – Intelligent systems, techniques, and methodologies are being employed to improve Data Engineering processes and provide clients with added value. Organizations are dedicating resources to implementing cutting-edge AI technologies that can enhance various Data Engineering tasks, from initial ingestion to end consumption. The emergence of Generative AI is transforming the way data is analyzed and utilized in organizations. While it is currently revolutionizing the consumption side of the industry, the pace of developments indicate that it will soon have a significant impact on Data Analytics workloads. This shift towards Generative AI will pave the way for new approaches to Data Engineering projects in the upcoming quarters, resulting in increased efficiency and effectiveness.

  2. FinOps will be a game changer – As companies move their Data and Analytics workloads to cloud-based platforms, they are discovering the potential for costs to go out of control without careful management. Though various solutions exist, few provide a sufficient return on investment, leaving customers in search of fresh methods to manage expenses across cloud infrastructure. FinOps provides monitoring teams with tools they need for cloud cost screening and control while promoting a culture of cost optimization by increasing financial accountability throughout the organization. CFOs are especially pleased with this development and are keen on spreading this cost-conscious approach.

  3. Data Observability is not a buzzword anymore – Mature organizations are proactively utilizing observability to intelligently monitor their data pipelines. Unforeseen cloud charges can arise from occurrences such as repetitive invocation with Lambda or the execution of faulty SQL code, which can persist unnoticed for prolonged periods. The implementation of observability equips operations teams with the ability to better comprehend the pipeline’s behavior and performance, resulting in the effective management of costs associated with cloud computing and data infrastructure.

  4. Consumption-based D&A chargeback is the way to go – Shared services teams are encountering challenges when it comes to accurately charging their internal clients for their utilization of D&A services. The root of this problem is attributed to the lack of transparent cost allocation mechanisms for data consumption, which makes it difficult to determine the genuine value of a D&A service. The solution lies in implementing consumption-based cost chargeback, which not only addresses the current challenges but also prompts businesses to adopt more intelligent FinOps models.

In summary, the summit provided valuable insights into the latest trends, challenges, and opportunities in the field, highlighting the importance of collaboration, innovation, and upskilling. There are many exciting developments that promise to revolutionize the industry. As we move towards a data-driven world, it is clear that data engineers will play a crucial role in shaping our future, and it is essential that they stay informed, adaptable, and agile to keep up with the rapidly evolving landscape.

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