Unlock Real-Time Insights with Datorios
Advanced Observability for Apache Flink®
Unlock Real-Time Insights with Datorios
Advanced Observability for Apache Flink®

Datorios is a powerful observability platform tailored for Apache Flink, providing unparalleled visibility into your data streams. Whether you’re running large-scale streaming applications or managing complex event processing, Datorios equips your team with the insights needed to maintain peak performance and reliability.

Key Features
Get Operational Insights
Key Features
Get Operational Insights

Job Insights

Get a detailed overview of your Flink jobs, including session data, statistics, and processed record tracking. This helps identify inefficiencies and optimize job execution.

State Viewing

Monitor state evolution throughout operators event processing. Easily identify state changes occurring during processing.

State & Checkpoints Analysis

Gain deep visibility into Flink states and checkpointing. Understand how state size and checkpoint execution impact job and cluster performance. Uncover the complex interactions between data, code, and infrastructure to enhance problem-solving and optimization.

Performance Analysis

Access real-time insights into resource utilization, throughput, and latency across your Flink job. Identify bottlenecks and optimize execution using custom metrics.

Logs

Capture and store detailed logs for every event processed by Apache Flink. Access historical logs to troubleshoot errors and optimize performance by tracing system behavior across jobs.

BYOC for a Secure Future

Learn more about how BYOC can elevate your Apache Flink observability while keeping your cloud environment secure.

Learn More

Key Features
Deep Dive Into Investigation
Key Features
Deep Dive Into Investigation

Traces

Follow data records as they flow through your Flink jobs. Trace the path from source to sink, uncover transformation points, and quickly identify bottlenecks or errors with precise real-time tracking.

State Investigation

Analyze the evolution of the state within Apache Flink. State Investigation allows you to see how the state changes over time, providing critical insights into operator behavior to refine your data flows with precision.

Time Window Analysis

Visualize various time windows (tumbling, sliding, session) to identify events within each window and detect missing data, offering a comprehensive view of your data.

Data Lineage

Utilize interactive job graphs to trace your data back from sink to source for process validation and bad quality data source causes.


Deployment Options

Deployment Options

SaaS

Leverage our fully managed cloud solution for rapid setup and minimal overhead.

Dedicated SaaS

Enjoy the advantages of a fully managed SaaS solution, dedicated to your organization and data, with VPC-to-VPC connectivity.

Bring Your Own Cloud (BYOC)

Integrate seamlessly with your existing cloud environment, ensuring data security and operational flexibility.


Why Datorios?

Why Datorios?

Optimized for Apache Flink

Datorios is built specifically for Apache Flink, offering unique features and insights that general-purpose observability tools can’t match. Datorios handles the complexities of real-time data processing, ensuring your Flink jobs always run optimally.

Empower Your Data Teams

Equip your data engineers, operations teams, and heads of data with the tools they need to manage Flink jobs effectively. With Datorios, your team can monitor, troubleshoot, and optimize in real-time, reducing downtime and improving overall system reliability.

Actionable Insights for Better Decision-Making

Make informed decisions with the help of real-time data and actionable insights. Datorios provides the visibility you need to understand how your Flink jobs are performing, helping you to optimize resource allocation and improve overall efficiency.

Security at Datorios
We Are SOC 2 Type II Certified
Security at Datorios
We Are SOC 2 Type II Certified

FAQFAQ

Does Datorios have access to my data?

Datorios extracts certain client statistical information. All customer data remains visible only to the customer.

What data is collected by Datorios?

The Datorios client collects record metadata, state data, window details, along with Logs and Metrics produced by Flink.

How is data transferred to Datorios?

The data collected by the Datorios client is either hashed or encrypted based on your preference, and then it is uploaded to Datorios using SSL to our internal backend.

Can I delete my data at any time?

Yes, you can permanently delete any job and its associated data by navigating to the job screen and clicking the trashcan icon. To remove your organization, go to the organization screen and click the wheel cog at the top right corner.

What Flink languages does Datorios support?

Datorios supports Java, Python, and Scala.

What versions of Flink does Datorios support?

Datorios supports Flink version 1.6.x and above. For specific requirements, please contact us.

 

Can I run multiple jobs in parallel with Datorios?

Yes, Datorios allows you to run in either session mode or application mode.

 

What installation types are supported by Datorios?

Datorios supports installation via Docker and Kubernetes. For other installation types, please contact us.

 

Is Datorios operational at all times?

Datorios continuously collects metrics and logs, similar to other observability products like Datadog and Prometheus. Tracing data is collected only when triggered by specified metrics exceeding thresholds.

Can Datorios be installed in Kubernetes?

Yes.

 

Can Datorios be installed using Docker?

Yes.

 

Does storing large amounts of data make Datorios expensive?

No, Datorios uses a cache that continuously overwrites itself until a critical event is recognized. Only three minutes of data before a critical event are stored.

 

Is there a free version of Datorios available?

Yes, click here to get started.

Are there sample jobs available for testing in Datorios?

Yes, please refer to our GitHub and the preloaded jobs available in the application.

 

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