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This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability by running across multiple availability zones

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high concurrency.

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed stream using Confluent Cloud, ingests these JSON events, using a native connection, into Imply Polaris and visualizes the event data in a dashboard.

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service has helped customers build real-time analytics applications faster, cheaper, and with less effort.

For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires a new type of database.

How to ingest data into Imply Polaris from Confluent Cloud and from Apache Kafka

You can easily do data transformations and manage JSON data with Imply Polaris, both using SQL.

When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction in accuracy. We further explain how to use these algorithms in Imply Polaris - Imply’s SaaS Platform