Data Collection Strategies for AI and ML Models
Data serves as the lifeblood of Artificial Intelligence (AI) and Machine Learning (ML) models, powering their development, refinement, and performance.
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Home / Case Studies / Bricks, Bytes, & Databricks: Streamlining Real Estate Workflows for the Future
A global leader in commercial real estate services that offers a wide array of services including, but not limited to, property management, investment management, and valuation services. One of their innovative approaches involves utilizing technology to optimize and enhance workflows, particularly in analytics dashboards that use data from various sensors installed in apartment buildings. This not only involves ingesting and validating data but also refining raw data into actionable insights, thereby enabling comprehensive analytics.
The main challenge encountered in the Databricks case was the existence of issues and bottlenecks in the Databricks dashboard workflows, which were leading to workflow failures and, consequently, negatively impacting the performance of the analytics dashboards.
The main challenge encountered in the Databricks case was the existence of issues and bottlenecks in the Databricks dashboard workflows, which were leading to workflow failures and, consequently, negatively impacting the performance of the analytics dashboards.
Xorbix made strategic use of the Databricks UI tools, including the Spark UI and Clusters UI, to accurately pinpoint and resolve a memory exhaustion issue that was plaguing a Spark structured streaming job. To ensure continuous and seamless operation, they implemented watermarking. Furthermore, to facilitate proactive issue resolution, they established email notifications to alert the team immediately in case of any workflow failures. This multifaceted approach not only addressed the memory exhaustion problem but also enhanced the overall robustness and responsiveness of the system.
Proactive alerts via email notifications streamlined issue resolution, and watermarking ensured continuous operation of the Spark structured streaming job.
The team included a certified Databricks developer, contracted specifically for their expertise in optimizing and enhancing workflows within the Databricks dashboard. The team specialized in data ingestion, streaming, aggregation, and validation as well as database management and analytics.
Utilizing an agile methodology, the development involved addressing workflow issues, improving performance, and refining raw data into actionable insights. Regular iterations and proactive adjustments were made to ensure optimal performance.
Testing involved validating the functionality and responsiveness of the implemented solutions, such as watermarking and email notifications, and assessing their impact on workflow performance and issue resolution.
The team included a certified Databricks developer, contracted specifically for their expertise in optimizing and enhancing workflows within the Databricks dashboard. The team specialized in data ingestion, streaming, aggregation, and validation as well as database management and analytics.
Utilizing an agile methodology, the development involved addressing workflow issues, improving performance, and refining raw data into actionable insights. Regular iterations and proactive adjustments were made to ensure optimal performance.
Testing involved validating the functionality and responsiveness of the implemented solutions, such as watermarking and email notifications, and assessing their impact on workflow performance and issue resolution.
The optimization and enhancement of workflows, implementation of watermarking, and introduction of email notifications for workflow failures resulted in streamlined issue resolution and continuous operation of the Spark structured streaming job.
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