3 Biggest Challenges for Data Engineersĭata engineers have become a critical asset of machine learning teams. We look into the power of integrating your tools with Apache Airflow and how it can benefit your enterprise. In this blog post, we will address the biggest challenges data engineers face in their work and in their tooling. In fact, data engineers have to face a number of challenges in order to navigate the distributed ecosystems and architectures of their organizations in order to provide the best end to end products. Oftentimes, it has caused an overload and overlap of features amongst data platforms and applications where data engineers have many tools with few workbench space.Īlongside this multiplicity of tooling, there is a gap between tools like Spark, Bash scripting, Kubernetes, Python scripting, and in-house applications. Moreover, the ever-growing world of open-source and vendor products has left many CTOs and engineers wondering what tooling to choose for their enterprise architectures. The overwhelming amount of data we consume daily has stifled many engineers from proper data pipeline development and engineering.
Over the last 15 years in the world of digital transformation, many have moved from file cabinets to data warehouse clusters to cope with an explosive growth in data. Hector Robles 3 August 2022 Apache Airflow: Overview, Use Cases, and Benefits
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