Xorbix Technologies
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Project Description

A global leader in commercial real estate services partnered with Xorbix Technologies to enhance their data analytics capabilities. The client relied on Databricks dashboards powered by data from multiple sensors installed in apartment buildings to gain real-time operational insights. However, frequent workflow failures and performance issues were affecting the reliability of their analytics. Xorbix’s data engineering team was brought in to optimize and stabilize Databricks workflows, refine data ingestion processes, and ensure continuous, error-free streaming. Being an office partner of Databricks and having the right expertise in advanced troubleshooting, performance tuning, and proactive alerting systems, Xorbix successfully transformed the client’s workflow into a highly efficient and scalable data environment.

Challenge

Problem

1. Frequent workflow bottlenecks in Databricks leading to dashboard failures.

2. Memory exhaustion issues in Spark structured streaming jobs.

3. Delayed analytics updates affecting real-time insights.

4. Lack of proactive alerts for workflow failures.

5. Unoptimized resource usage causing performance degradation.

6. Inconsistent data validation and aggregation across multiple data sources.

 

Project Goals

Workflow Optimization

  • Identify and resolve Spark performance issues.
  • Optimize Databricks cluster configurations for stability.
  • Streamline workflows for faster data processing.

Data Reliability

  • Refine raw data into validated, actionable insights.
  • Maintain continuous operation through efficient streaming.
  • Reduce data latency across ingestion and aggregation layers.

Monitoring & Maintenance

  • Implement automated alerts for workflow failures.
  • Establish proactive monitoring systems for performance tracking.
  • Ensure scalability for future data expansion.

Solution

Xorbix; an expert in Databricks services, utilized Mutiple UI tools, including Spark UI and Clusters UI, to pinpoint workflow bottlenecks, implemented watermarking for continuous operation, and introduced email notifications for real-time failure alerts, resulting in a stable, high-performance analytics workflow.

Workflow Optimization

  • Identified memory exhaustion through Spark UI analysis.
  • Tuned Databricks cluster resources for optimal load balancing.
  • Streamlined Spark structured streaming jobs to reduce runtime.
  • Improved overall system reliability and performance.

Data Management & Validation

  • Enhanced ingestion via Amazon Kinesis for real-time data flow.
  • Implemented data aggregation and validation within Databricks.
  • Integrated Postgres for secure batch analytics.
  • Reduced redundancy and improved data consistency.

Proactive Monitoring

  • Added email alerting for immediate workflow failure detection.
  • Established a monitoring framework for ongoing performance tracking.
  • Logged workflow events for audit and debugging.
  • Reduced downtime through early detection and rapid response.

Continuous Operation & Maintenance

  • Implemented watermarking to prevent data duplication.
  • Ensured seamless streaming across high-volume sensor data.
  • Regularly tested fault tolerance and recovery systems.
  • Enabled future scalability through modular architecture.

High Level Architecture

Innovations

  • Implemented watermarking to maintain seamless data streaming without duplication.
  • Integrated automated email notifications for instant issue detection and faster recovery.
  • Used Spark UI and Cluster UI to diagnose performance bottlenecks with precision.
  • Established proactive monitoring systems for real-time workflow visibility.
  • Adopted Agile-based optimization cycles to ensure continuous improvement.

Security

  • Ensured secure data transfer across Amazon Kinesis, Databricks, and Postgres.
  • Configured role-based access controls to limit unauthorized data exposure.
  • Enforced data validation and encryption for all streaming and batch processes.
  • Maintained HIPAA-aligned data handling standards for client data protection.
  • Conducted regular security testing to identify and mitigate potential risks.

Core Technologies

  • Amazon Kinesis for real-time, high-volume data ingestion.
  • Databricks for data aggregation, transformation, and workflow orchestration.
  • Postgres Database for batch analytics and long-term data storage.
  • Spark Structured Streaming for reliable, scalable data processing.
  • Email Automation Tools for proactive alerting and workflow monitoring.

Process

Team

  • A certified Databricks developer and data engineers collaborated with the client’s analytics team.
  • Xorbix’s experts specialized in data ingestion, aggregation, and validation pipelines.
  • Cross-functional collaboration ensured both technical accuracy and business alignment.

General Development

  • Followed an Agile methodology for iterative improvement.
  • Prioritized performance tuning and workflow stability in each sprint.
  • Conducted continuous optimization for scalability and resource efficiency.

Testing

  • Validated watermarking functionality for continuous stream performance.
  • Tested alert systems for accuracy and real-time responsiveness.
  • Performed load and regression testing to ensure workflow resilience.

Results

  • Resolved persistent workflow bottlenecks, improving system reliability.
  • Enhanced dashboard performance with faster and more accurate data refreshes.
  • Improved data quality and validation, ensuring consistent analytics output.
  • Enabled real-time issue alerts, minimizing downtime and workflow failures.
  • Increased operational efficiency, allowing faster business insights and decisions.
  • Strengthened system scalability, supporting future data and feature growth.
  • Delivered a robust, secure, and high-performing analytics ecosystem for the client.
Contact Xorbix

Partner with Xorbix to transform your data workflows into scalable, intelligent, and efficient systems that empower better decision-making.

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802 N. Pinyon Ct,
Hartland, WI 53029

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