Data, AI & Machine Learning Engineering Solution
Organizations are increasingly migrating their analytics and data processing workloads to the cloud for better scalability and flexibility. However, with this comes the challenges of managing cloud resources efficiently and controlling associated costs.
Databricks, a unified analytics platform built on Apache Spark, has emerged as a powerful solution for data engineering, data science, and Machine Learning workloads in cloud environments. As companies deploy more complex data pipelines and analytics solutions using Databricks across AWS, Azure, and Google Cloud, understanding and optimizing cloud usage and costs has become a critical concern for IT leaders and finance departments alike.
Databricks employs a cloud-based consumption model with per-second billing precision, allowing organizations to pay exclusively for the computing resources they utilize. This pay-as-you-go approach eliminates the need for upfront financial commitments or long-term contracts, providing flexibility for resources.
The primary challenge users face with Databricks lies in the complexity and potential lack of transparency surrounding its cost structure. This complexity arises from several factors:
There are several ways to manage and optimize Databricks costs:
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