Rakuten announces launch of its enterprise grade data watchdog, Rakuten
SixthSense Data Observability!
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Organizations have embraced a cloud-native architecture, leveraging container orchestration (e.g., Kubernetes) and microservices to build and deploy applications rapidly. With this evolution, your observability landscape has become increasingly complex, involving multiple tools for monitoring, logging, and tracing. This complexity is affecting your ability to efficiently manage and troubleshoot your cloud-native applications.
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Over time, you have adopted various tools for different observability needs, leading to tool sprawl. This proliferation has made it challenging to maintain, update, and configure each tool independently.
Each tool operates in isolation, resulting in data silos. Correlating metrics, logs, and traces across different tools is time-consuming and often inconclusive.
As your microservices ecosystem grows, you're facing challenges in scaling your existing observability tools to handle the increased data volume and complexity.
Licensing and operational costs for multiple tools have become a significant expense. These costs are exacerbated by the need for additional resources to maintain and scale the tools.
Rakuten SixthSense, an unified observability platform that can effectively address the above challenges and lead to key benefits as listed below.
Single Data Store: Rakuten SixthSense involves centralizing data collection and storage. This means that metrics, logs, traces, and other observability data are ingested into a unified repository.
Normalized Data: Data normalization ensures that metrics, logs, and traces are consistently structured and tagged, making it easier to query and correlate information.
Common Query Language: Supports a standardized query language or query API, allowing engineers and analysts to perform complex queries across different types of data (e.g., metrics and logs) using a common syntax.
Cross-Domain Analysis: Teams can easily create custom dashboards and alerts that span different data sources, enabling more comprehensive analysis.
Data Ingestion Scalability: Designed to handle the high volume of data generated in large, complex environments. Includes features like horizontal scaling and data sharding for efficient data ingestion.
Data Retention Strategies: Can be configured to manage data retention efficiently, ensuring that older data is archived or aggregated to maintain performance.
Advanced Alerting Rules: Provides advanced alerting capabilities, including the ability to create alerts based on complex conditions and correlations across different data types.
Incident Detection: Automatically detect anomalies and patterns of interest, allowing for proactive incident detection and alerting.
Integration Framework: Offers integration capabilities to connect with other systems and tools, enabling automation of workflows and processes.
APIs and Webhooks: APIs and webhook support facilitate custom integrations and automation scripts to interact with observability data and events.
Data Lifecycle Management: Provides features for defining data retention policies, archiving data, and managing compliance requirements.
Data Encryption: Robust data encryption mechanisms are available to ensure data privacy and security.
Real-Time Data Processing: Supports real-time data streaming and processing, enabling immediate analysis and visualization of critical observability data.
Complex Event Processing (CEP): CEP capabilities allow for the creation of real-time alerts and dynamic visualizations based on streaming data.