Imagine what you can do
with all that data

Collect
Query
RetaiN
EVERYTHING
Dynamic hero image showcasing the high-performance capabilities of the Hydrolix platform, emphasizing efficient log data management and cost-effective solution
Product Features

Build high-performance applications for less

Hydrolix is a streaming data lake. It combines real-time stream processing, decoupled storage, and low-latency indexed search to create a high performance yet cost-efficient log management system.

These features make Hydrolix the ideal data platform to power log data-intensive applications.

Collect

Stream processing for high volume ingest and enrichment

Hydrolix can ingest data from multiple sources, indexing and applying enrichment and transforms on  incoming streams. 

Support for Apache Kafka, AWS Kinesis, using the HTTP streaming API or via batch upload, including autoingest – batch loads

Transforms are write schemas used to configure how you index, enrich, standardize, normalize, and store incoming data from one or more sources to a given table. 

Extract value from your streamed data on ingest. Apply multiple transforms to each data stream to create multiple tables or transform to multiple streams, all writing to the same table.

Stream data into summary tables to store aggregations including min, max, count, and sum. Hydrolix ensures summaries and raw data remain in sync for late-arriving data. 

Query

Index search for fast, efficient queries across years worth of data

Per-column indexes mean queries read only relevant byte-ranges within each column.  Queries execute fast and avoid costly full table and full column scans associated with serverless databases and data lake technology. 

Massively parallel processing, a unique approach to indexing at the block level, and predicate pushdown combine for low-latency queries,returning only the data you need, block by block.

For maximum flexibility, Hydrolix is designed to work out of the box with popular data visualization tools like Grafana, Redash, Superset, and Looker.

Query using ANSI-compliant SQL, Spark, JDBC, HTTP API and native interfaces. The SQL API uses standard features from the SQL engine of Clickhouse, including interface API.

Create separate, independently scalable query pools for different groups of users and assign resources to each according to business needs.

Retain

Decoupled storage with advanced compression, reducing data storage costs 4x

Decoupled storage allows you to manage the cost and operations of storage separately from compute. Scale storage without incurring additional costs for query and ingest.

Industry-leading compression reduces your data footprint as much as 50x without sacrificing query performance. Save more data for longer and stop compromising data quality for budgetary reasons.

Decoupled storage, indexing, and compression mean all data is hot; no need to manage warm and cold storage tiers. Stop sampling or discarding data. Keep it hot for as long as you need it.

Hydrolix continuously optimizes stored data using a merge service for compaction and an age service that deletes data based on per-table TTL settings.

Ready to Start?

Cut data retention costs by 75%

Give Hydrolix a try or get in touch with us to learn more

For more information, check out our FAQs

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Ready to start? Try us out!

Looking to cut your log data retention costs by 75%?
Give Hydrolix a try or get in touch with us to learn more!