Simplifying Real-Time Telemetry Insights with Grainite
What are Telemetry insights?
Telemetry insights are critical to any organization that needs to monitor the health, performance, availability, and security of its products and solutions deployed within a network environment. These insights enable administrators to respond quickly and proactively resolve issues in real-time.
Data platforms that extract telemetry insights require components to be stitched together from multiple vendors and are often delayed by several seconds or minutes. Such an architecture makes it challenging to be deployed in a multi-cloud and on-premises environment. Changes to the traffic volumes require rearchitecting the data pipeline.
Telemetry data pipelines typically require several capabilities, including:
- Data collection from batch and streaming sources
- Data preparation
- Data aggregation
- Data analysis
- Data storage and management
- Monitoring to detect anomalies
- Integration with other systems (e.g. ticketing and incident management)
What are the challenges with building a Telemetry data pipeline?
Some of the challenges in building a telemetry data pipeline are:
- Requires multi-vendor strategy including components such as Apache Kafka (ingestion), Apache Flink (transformations), Cassandra (database), Connectors (integrations), and monitoring tools. This drives higher operational costs and complexity.
- Data Volume and Velocity: Telemetry pipelines can generate large volumes of data, and the velocity of the data can be high as well. This can pose challenges in terms of data storage, processing, and analysis.
- Data Quality and Consistency: Telemetry data is often generated by a variety of sources, which can lead to inconsistencies in the data. There can also be issues with missing or incomplete data, data duplication, and other data quality issues.
- Data Integration: Telemetry data often needs to be integrated with other data sources (e.g. logs, historical behavior) to provide meaningful insights. This can be challenging due to differences in data formats, data structures, and data semantics.
- Scalability: Telemetry pipelines need to be scalable to handle increasing data volumes and evolving data sources. This requires a flexible architecture that can adapt to changing requirements.
- Monitoring and Maintenance: Telemetry pipelines require ongoing monitoring and maintenance to ensure that they are functioning properly and that data is being processed correctly. This can be a time-consuming and resource-intensive task.
Simplifying Telemetry Data Processing and Insights with Grainite
Grainite is an integrated event processing platform that provides all the necessary capabilities to build a telemetry insights platform. It combines stream ingestion, processing, storage, and more into a single platform.
How does Grainite Simplify the Telemetry Data Pipeline
The first step is to ingest the telemetry data generated from various sources into the platform. Grainite can pull data from various sources via Tasks or data can be pushed to Grainite using our gRPC/REST APIs. Once the data is ingested
- Grainite action handlers (business logic written by developers) transform the telemetry data to compute meaningful metrics (e.g averages, dispersion, and shape) and aggregations at various time intervals.
- Materialized views are saved inside Grainite tables and can be accessed at millisecond latencies to enable additional aggregations when required.
- Anomalies in telemetry data can be easily identified using rules specified by the developers or business analysts. Historical data can also be queried in real time.
- Grainite integrates directly with 3rd party components and can be programmed to integrate with existing ticketing and incident management solutions.
Benefits for any streaming application with Grainite
- Developers can build their applications on their laptops and deploy them unchanged to clusters in any cloud environment.
- Grainite can integrate with both stream sources using APIs and pull data from batch sources using Tasks. Data can be normalized before being stored in Grainite. Unlike Apache Kafka, Grainite does not require setting up partitions.
- External microservices can query Grainite using provided APIs to derive additional real-time insights.
- Application and System level monitoring is built into Grainite. Dashboards and reports can be directly generated from Grainite.
- Grainite delivers high resiliency and scalability to process a very high volume of telemetry data, and developers do not need to program for this.
- Grainite protects data in motion and data in storage using industry-standard security protocols and role-based access controls.
Founded in 2019 by leaders from Google and Citrix, Grainite is backed by top-tier VCs. The team has over 200+ years of collective experience in building large-scale enterprise applications at leading data and storage companies. Reach us at firstname.lastname@example.org to learn more and to schedule a demo.