Managing Risk with Databricks: Don’t Burn the House Down

At zeb, the data governance practices we implement with Databricks are essential to mitigate risks such as data breaches, regulatory non-compliance, and poor data quality. These practices bolster fundamental aspects of your infrastructure such as databases, pipelines, and security layers to ensure the reliability of Databricks’ advanced features and capabilities. What’s more, proper implementation helps prevent expensive and labor- intensive data issues. Many AI companies deploy AI systems while overlooking how governance affects data quality and cloud infrastructure. Without a solid data foundation, your jump into AI will lead to poor model performance, wasted resources, and unmet expectations.

Model and endpoint risks

There are a number of problems zeb helps you stay in front of when it comes to AI and machine learning models and their endpoints, including security vulnerabilities, model drift, and unauthorized access. Security vulnerabilities such as data poisoning and bad data can lead to faulty predictions, data attacks, and privacy attacks. Model drift, the gradual decline in a model’s predictive accuracy, is often a direct result of unexpected changes to underlying data and relationships between input features and target variables. Unauthorized access can also be attributed to unintended model tampering and a lack of data governance.

Performance risks

Without proper setup, your system is sure to experience slow processing times, bottlenecks, and inefficient use of resources. Whether it’s redundant processing, under- or over-utilization of data, or misapplication of data, these inefficiencies result in wasted time, bandwidth congestion, potentially inaccurate results, as well as the additional human resources required to correct and troubleshoot these issues.

Operational risks

In addition to sub-optimal system performance, inadequate data governance can also impact operations in several significant ways.

System downtime occurs when systems become overloaded, data bases are inefficient, and software is not properly integrated, which can cause critical components, applications, or services to become unavailable. These failures can lead to catastrophic issues such as lost revenue, compromised security, legal penalties, and loss of consumer trust.
Data loss is another serious concern for organizations that lack data governance. Insufficient data replication, databases exposed to cyberattacks, and improperly managed data pipelines can result in corrupt datasets that cripple analytics, reporting, strategy, and ultimately, decision making.
Maintenance challenges are a third operational concern. With complex infrastructure and data systems, even the slighted poorly documented or misaligned data can make routine updates, troubleshooting, and scaling difficult. Resources and expertise are spent troubleshooting and rebuilding instead of on innovation and growth.

Best practices lead to a better platform experience

The need for data governance underscores the importance of choosing a digital transformation partner who understands how to mitigate risks and leverage Databricks effectively and efficiently. As your partner, zeb can optimize Databricks as a unified solution for your organization through:

Data Ingestion Optimization: Efficiently load data using optimized connectors, Delta Lake for storage, and auto-scaling.
Data Governance Implementation: Establish governance with Unity Catalog to manage access, ensure data quality, and comply with regulations.
Cost Management: Monitor resource usage and use cost allocation tags to track spending.
Performance Optimization: Improve query performance by appropriately partitioning data, using caching, and optimizing queries.
Infrastructure Security: Secure your system by implementing network policies, access controls, and monitoring tools to protect data.
Automating Workflows: Use Databricks Jobs and Delta Live Tables to automate data processing pipelines and workflows.
Operations Monitoring: Set up monitoring dashboards and alerts to identify issues and ensure system reliability.
User Education: Provide training and resources to help your team use Databricks effectively and efficiently.
AI and ML Lifecycle Optimization: Implement MLOps (Machine Learning Operations) practices for model deployment, monitoring, and retraining.
Lakehouse Architecture: Implement a lakehouse architecture using Delta Lake to combine the best features of data lakes and data warehouses.

Maximize the potential of Databricks

Robust data governance is critical to mitigating risks such as model drift, data breaches, operational inefficiencies, and costly system failures. By partnering with zeb to implement best practices like optimized data ingestion, strong security measures, and proactive cost management, your organization can ensure reliable performance, safeguard critical systems, and drive innovation. With a strong data foundation, we’ll help you leverage Databricks as a powerful, efficient platform for AI and analytics success. To arrange a consultation, contact us at sales@zeb.co.