• Developer guide
  • API reference

›Analytics

Getting started

  • Introduction to Imply Polaris
  • Quickstart
  • Navigate the console
  • Key concepts

Ingestion sources

  • Ingestion sources overview
  • Supported data formats
  • Create a connection
  • Ingest from files
  • Ingest from S3
  • Ingest from Kinesis
  • Ingest from Confluent Cloud
  • Kafka Connector for Imply Polaris
  • Push event data

Tables and data

  • Overview
  • Introduction to tables
  • Table schema
  • Create an ingestion job
  • Timestamp expressions
  • Data partitioning
  • Introduction to rollup
  • Approximation algorithms
  • Replace data

Analytics

  • Overview
  • Manage data cubes
  • Visualize data
  • Data cube dimensions
  • Data cube measures
  • Dashboards
  • Create a dashboard
  • Visualizations reference
  • Set up alerts
  • Set up reports
  • Embed visualizations
  • Query data

Monitoring

  • Overview

Management

  • Overview
  • Pause and resume a project

Billing

  • Overview
  • Polaris plans
  • Estimate project costs

Usage

  • Overview

Security

    Polaris access

    • Overview
    • Invite users to your organization
    • Permissions reference
    • Manage user groups
    • Enable SSO
    • SSO settings reference
    • Map IdP groups

    Secure networking

    • Connect to AWS

Developer guide

  • Overview
  • Authentication

    • Overview
    • Authenticate with API keys
    • Authenticate with OAuth
  • Manage users and groups
  • Migrate deprecated resources
  • Create a table
  • Define a schema
  • Upload files
  • Create an ingestion job
  • Ingestion sources

    • Ingest from files
    • Ingest from a table
    • Get ARN for AWS access
    • Ingest from Amazon S3
    • Ingest from Amazon Kinesis
    • Ingest from Confluent Cloud
    • Push event data
    • Kafka Connector for Imply Polaris
    • Kafka Connector reference
  • Filter data to ingest
  • Ingest nested data
  • Ingest and query sketches
  • Query data
  • Update a project
  • Link to BI tools
  • Connect over JDBC
  • Query parameters reference
  • API documentation

    • OpenAPI reference
    • Query API

Product info

  • Release notes
  • Known limitations
  • Druid extensions

Analytics overview

The Quickstart introduces you to visualization features in Imply Polaris. If you are new to Polaris, we recommend you begin there. This topic takes a deeper look at the visualization features, including data cubes and dashboards.

Polaris democratizes data analytics, giving anyone in an organization the ability to explore and analyze data with visually rich, easy-to-use tools. These tools include dashboards, query builders, and data cubes, such as the following data cube:

Default_view_example

Data cubes provide a framework for users to explore data. Typically, someone in the organization with substantial knowledge of the data set, such as a data analyst, sets up the data cubes and dashboards.

Data analysts should ensure that the underlying queries that compose the data cube yield results relevant to the organization use cases. Data cubes also form the basis for dashboards. So similarly, when designing a set of data cubes, data managers should take into consideration the types of dashboards data users will use. While data cubes provide an exploratory interface for a data set, dashboards provide a high-level, comprehensive view of the state of the data.

Learn more

See the following topics to get started with Visualize features:

  • Manage data cubes: Create and edit data cubes.
  • Visualize data: Learn about using visualization features to explore and draw insights from your data.
  • Data cube dimensions: Learn about dimension types and create a custom dimension.
  • Data cube measures: Create and configure measures, and view custom measure examples.
  • Dashboards overview: Create and edit dashboards.
  • Visualizations reference: Learn about visualization types.
  • Query data: Access the SQL editor to query your data.
  • Set up alerts: Configure alerts to track events in your data.
← Replace dataManage data cubes →
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