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How does Hydrolix compare with other database and data lake solutions?

As a streaming data lake, Hydrolix brings a unique design to the challenges of real-time processing of high volumes of event data. Hydrolix combines stream processing, indexed search, advanced compression techniques and decoupled storage into a single, stateless architecture, a design that delivers a combination of high-performance, longer hot data retention, and low cost. The…

Tony Falco

Published:

Oct 30, 2023

1 minute read

As a streaming data lake, Hydrolix brings a unique design to the challenges of real-time processing of high volumes of event data. Hydrolix combines stream processing, indexed search, advanced compression techniques and decoupled storage into a single, stateless architecture, a design that delivers a combination of high-performance, longer hot data retention, and low cost.

The ingest system can handle both event streams (via Kafka, Kinesis, and HTTP streaming) and batch loading.

The query system scales independently and specific workloads can be partitioned into query pools to avoid resource contention and to allow teams to assign compute resources in accordance to their own specific budget and performance goals.

Hydrolix uses object storage (rather than block storage) to keep log storage costs low and extend hot data retention windows. Patented compression techniques further reduce your data footprint and log storage costs.

Hydrolix’s approach to partitioning data plus it’s default of indexing every column, together with customizable query pools, deliver SSD performance at block storage prices.

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As a streaming data lake, Hydrolix brings a unique design to the challenges of real-time processing of high volumes of event data. Hydrolix combines stream processing, indexed search, advanced compression techniques and decoupled storage into a single, stateless architecture, a design that delivers a combination of high-performance, longer hot data retention, and low cost.

The ingest system can handle both event streams (via Kafka, Kinesis, and HTTP streaming) and batch loading.

The query system scales independently and specific workloads can be partitioned into query pools to avoid resource contention and to allow teams to assign compute resources in accordance to their own specific budget and performance goals.

Hydrolix uses object storage (rather than block storage) to keep log storage costs low and extend hot data retention windows. Patented compression techniques further reduce your data footprint and log storage costs.

Hydrolix’s approach to partitioning data plus it’s default of indexing every column, together with customizable query pools, deliver SSD performance at block storage prices.