Build high-performance applications for less
Hydrolix transforms the economics of log data
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.
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.
Hydrolix comes bundled with Grafana and works out of the box with other popular data visualization tools like 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.
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.
For more information, check out our FAQs
WHAT OUR CLIENTS ARE SAYING
The total cost of ownership of Hydrolix was less than it used to cost me to store all the logs.
– Simon LaRoque
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