Monte Carlo is a data observability platform. Using machine learning models, it infers and learns about data, identifying issues and notifying users when they arise. It allows our teams to maintain data quality across ETL pipelines, data lakes, data warehouses and business intelligence (BI) reports. With features such as monitoring dashboards as code, a central data catalog and field-level lineage, our teams find Monte Carlo to be an invaluable tool for overall data.

Monte Carlo’s Data Observability platform helps  teams increase trust in data by eliminating data downtime. It uses machine learning to infer and learn what data looks like, proactively identify data downtime, assess its impact, and notify those who need to know. Automatically and immediately identify the root cause, Then allowing you to collaborate and resolve issues faster.

Data concerns across your data warehouses, data lakes, ETL, and business intelligence are monitored and alerted for by Monte Carlo’s Data Observability Platform, an end-to-end data stack solution. Machine learning is used by the platform to infer and learn from your data, proactively discover data concerns, evaluate their impact, and notify those who need to be informed.

Machine learning is used to understand the data architecture and what smooth operations look like based on five core elements:

• Freshness and recency.
• Volume.
• Schema.
• Distribution including things like fields, duplicates and null values.
• And lineage to understand how data connects upstream and downstream.

Customers don’t have to configure or set thresholds. 

Teams can simply interact and solve issues more quickly by automatically and instantly determining the source of a problem. Along with helping teams comply to stringent data governance guidelines, Monte Carlo also offers automatic, field-level lineage and centralised data cataloguing. These features help teams understand the location, ownership, health, and accessibility of their data assets.

Data Observability Platform from Monte Carlo is the first end-to-end solution to fix damaged data pipelines. The potential of data observability is delivered by Monte Carlo’s solution, enabling data engineering and analytics teams to address the expensive issue of data downtime.

Platform delivers :-

• End-to-end observability
• ML-powered incident monitoring and resolution
• Security-first architecture
• Automated data catalog and metadata management
• No-code onboarding

In order for Monte Carlo to work, the system needs to be able to:

• Collect metadata from your data warehouses and lakes so it can see what data exists and how it is organized
• Ingest query logs from your data warehouse or lake so that it can understand how data is moving through your environments
• Run queries against your data to aggregate/ derive a handful of metrics and statistics used for anomaly detection
• Access metadata from your BI tools to display information about your reports and dashboards, such as titles and authors

The architecture that Monte Carlo employs to achieve these objectives entails installing a data collector in your personal AWS environment. To carry out the aforementioned tasks, the data collector establishes a connection with your systems, and the relevant data is then transmitted back to Monte Carlo to power the dashboard, warnings, and anomaly detection.

Security and privacy practices

The team at Monte Carlo follows all industry best practices to safeguard both the security of its application and the privacy of its users’ personal information. The following are only some of the elements of our security program and system architecture:

• Monte Carlo will only collect metadata, logs, and metrics for the sole purpose of identifying data reliability issues. Your information will only be used to generate your own reports and will not be shared with any external parties.
• Processing is conducted on secure servers hosted on Amazon Web Services. All storage systems are encrypted, and all servers are tightly access controlled and audited. Data is encrypted in-transit at all times.
• In cases where debugging or maintenance work is required, a minimal number of engineers will be permitted to access the data necessary for this purpose. All engineers use encrypted laptops and are required to remove data from their devices when their debugging session is complete. Laptop security policies are enforced using MDM.
• Monte Carlo will access your environment from a single source IP dedicated to you, allowing you to protect access to your data resources at the network level.
• An annual penetration test is performed to validate Monte Carlo’s posture and identify vulnerabilities. 
• Monte Carlo’s service runs on highly available and highly redundant cloud services, mostly on AWS.
• Access to all critical systems and production environments is protected using strong passwords and multi-factor authentication. Where possible, SSO is used for centralized access control. Access is reviewed prior to being granted and then periodically thereafter.

Conclusion :

Monte Carlo’s platform uses machine learning to enable the analytics team to resolve data issues faster. Today, data flows often break and the data science team is often the last to know. With an approach that allows for quick implementations, Monte Carlo aims to show value quickly by solving for the dirty data issue.

 

For more details contact info@vafion.com

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