Polaris
Polaris is a fully managed database-as-a-service (DBaaS) built on Apache Druid, offered by Imply.
Publisher review
Polaris is a fully managed database-as-a-service (DBaaS) built on Apache Druid, offered by Imply. It targets developers and organizations that need real-time analytics on streaming and batch data but lack the in-house expertise to operate Druid clusters. Polaris promises deployment in under five minutes, with no infrastructure provisioning or tuning required. The service is available on AWS and Microsoft Azure, and is used by companies including Honeywell, NOPS, Peer39, Redbus, Retailcloud, Tapcart, Truvid, Wallbox, Anzu, Dream, Enova, and Finix. It is designed for use cases like observability, ad-tech, IoT analytics, and customer-facing dashboards where sub-second query response times are critical.
Polaris ingests data via native integrations with Confluent Cloud, Apache Kafka, and Amazon Kinesis, as well as batch ingestion. Queries are written in standard SQL, including support for nested JSON, and setup and configuration can be automated through intuitive APIs. The service includes built-in visualization for embedded analytics, allowing teams or customers to explore data with sub-second latency at any scale. Polaris offers auto-scalable ingestion, high-availability, unlimited data sources, built-in monitoring and alerts, and performance monitoring and alerts. It provides a starter project type limited to 25 GB storage, 4 vCPU, and 16 GB memory, along with series A and series D project types with varying capacities, and custom plans for unlimited project size.
Polaris competes with managed Druid services like those from Rockset (acquired by OpenAI) and self-managed Druid deployments, but its primary differentiator is the fully managed, serverless experience that eliminates Druid operational overhead. The vendor, Imply, was founded in 2015 and is headquartered in Burlington, United States. Polaris is positioned as a cost-efficient alternative to self-managed Druid, claiming proven TCO savings of up to 50% by offsetting data infrastructure costs and freeing developer resources.
While Polaris simplifies Druid operations, it introduces vendor lock-in to Imply’s management layer. The consumption-based pricing can become expensive at high ingestion volumes or deep storage usage. The service is relatively new compared to mature cloud data warehouses like Snowflake or Redshift, and its ecosystem of integrations and third-party tools is smaller. Users must also trust Imply’s committer-led support for uptime and upgrades, which may not suit organizations requiring full control over their analytics stack.
How it works
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Easy to set up
Start extracting insights in less than 5 minutes without procuring, deploying, or managing infrastructure.
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Dev-friendly by design
Write queries in SQL (including nested JSON) and use intuitive APIs to automate setup and configuration.
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Built-in visualization
Customize embedded analytics for teams or customers with sub-second data exploration at any scale.
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Fully managed at any scale
Automated setup, continuous updates, auto-scaling, and committer-led support for an always-on service.
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Secure and resilient
Enterprise-grade security, single sign-on (SSO), full identity and access control, and fault-tolerant design for zero downtime.
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Cost efficient
Managed operational efficiencies lead to proven TCO savings of up to 50% by offsetting data infrastructure costs.
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Native integrations
Easily ingest streaming and batch data from Confluent Cloud, Apache Kafka, and Amazon Kinesis.
Strengths and trade-offs
Strengths
- Deploys in under 5 minutes with no infrastructure management required, as stated on the Imply product page.
- Offers auto-scalable ingestion and high-availability, with a starter project limited to 25 GB storage, 4 vCPU, and 16 GB memory.
- Provides built-in monitoring and alerts, performance monitoring and alerts, and unlimited data sources.
- Claims proven TCO savings of up to 50% compared to self-managed Druid, according to Imply’s marketing materials.
Trade-offs
- Consumption-based pricing can become expensive at high ingestion volumes or deep storage usage, as noted in the billing overview.
- Vendor lock-in to Imply’s management layer may limit flexibility for organizations requiring full control over their analytics stack.
- The service is relatively new compared to mature cloud data warehouses like Snowflake or Redshift, resulting in a smaller ecosystem of integrations.
- Users must rely on Imply’s committer-led support for uptime and upgrades, which may not suit organizations with strict SLA requirements.
Pricing context
Consumption-based: billed per project size, batch and streaming ingestion volumes, file storage, deep storage, and async queries. Starter project: 25 GB storage, 4 vCPU, 16 GB memory. Series A and Series D projects with varying capacities.
Custom plans for unlimited project size. No fixed per-user license fee.
Getting started with Polaris
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Sign up for Polaris
Go to the Imply Polaris website and create an account. Choose a project type—Starter, Series A, Series D, or custom—based on your storage and compute needs. Complete the registration to access the Polaris console.
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Connect a streaming source
In the Polaris console, navigate to the ingestion section. Select a native integration such as Confluent Cloud, Apache Kafka, or Amazon Kinesis. Provide the connection details—broker address, topic name, and credentials—to start streaming data into Polaris.
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Define your data schema
After connecting a source, specify the schema for your data. Use the console or API to define dimensions, metrics, and timestamp columns. Polaris supports nested JSON, so you can map complex fields directly without flattening.
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Run your first SQL query
Open the query editor in the Polaris console. Write a standard SQL query—for example, SELECT COUNT(*) FROM your_table WHERE time > NOW() - INTERVAL '1' HOUR. Execute the query to verify data ingestion and sub-second response times.
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Set up monitoring alerts
In the monitoring section, configure alerts for ingestion lag, query performance, or storage usage. Define thresholds and notification channels (e.g., email or webhook). This ensures you are notified of any issues automatically.
Frequently Asked Questions
What is Polaris and how does it work?
Polaris is a fully managed database-as-a-service built on Apache Druid, offered by Imply. It provides real-time analytics on streaming and batch data without requiring users to manage infrastructure. Deployments take under five minutes, and queries use standard SQL.
What are the main features of Polaris?
Polaris offers easy setup in under five minutes, SQL querying with nested JSON support, built-in visualization for embedded analytics, auto-scalable ingestion, high availability, native integrations with Confluent Cloud and Apache Kafka, and enterprise-grade security with SSO.
How does Polaris pricing work?
Polaris uses consumption-based pricing billed per project size, batch and streaming ingestion volumes, file storage, deep storage, and async queries. Starter projects include 25 GB storage, 4 vCPU, and 16 GB memory. Custom plans are available for unlimited project size.
What are the benefits of using Polaris over self-managed Druid?
Polaris eliminates Druid operational overhead with automated setup, updates, and auto-scaling. It claims up to 50% TCO savings by offsetting data infrastructure costs and freeing developer resources. Deployments take under five minutes with no infrastructure provisioning required.
What are the weaknesses of Polaris?
Polaris introduces vendor lock-in to Imply’s management layer, and consumption-based pricing can become expensive at high ingestion volumes. The service is newer than mature cloud data warehouses like Snowflake, resulting in a smaller ecosystem of integrations and third-party tools.
What use cases is Polaris best suited for?
Polaris is designed for real-time analytics in observability, ad-tech, IoT analytics, and customer-facing dashboards where sub-second query response times are critical. It ingests data from streaming sources like Apache Kafka and Amazon Kinesis, and supports batch ingestion.
Alternatives
How Polaris compares
Direct head-to-head against 2 competitors. Picked by 7wData.
Polaris
- Pricing
- Consumption-based: billed per project size, batch and streaming ingestion volumes, file storage, deep storage, and async queries. Starter project: 25 GB storage, 4 vCPU, 16 GB memory. Series A and Series D projects with varying capacities. Custom plans for unlimited project size. No fixed per-user license fee.
- Target
- Polaris is a fully managed database-as-a-service (DBaaS) built on Apache Druid, offered by Imply.
- Strength
- Deploys in under 5 minutes with no infrastructure management required, as stated on the Imply product page.
- Watch for
- Consumption-based pricing can become expensive at high ingestion volumes or deep storage usage, as noted in the billing overview.
Can-Am Defender
- Pricing
- Starting at $20,399 for HD10
- Target
- Heavy-duty ranch & towing
- Deployment
- On-premises
- Strength
- 82-hp V-twin engine, 69 lb-ft torque
- Watch for
- Higher maintenance costs
Honda Pioneer
- Pricing
- Starting at $18,999 for Pioneer 1000
- Target
- Mixed work & play
- Deployment
- On-premises
- Strength
- 75-hp engine, 76 lb-ft torque
- Watch for
- Limited dealer network
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Sources
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