The Hydrolix Platform
Architected for massive scale without compromises on data quality, query performance, retention, or cost. Learn about the engine that gives you both real-time analytics and long-term insights on petabytes of data.
Real-time analytics. Full-fidelity data. No compromises.
Stream
Stream data in real time, even during peak events generating tens of millions of events per second.
Transform
Transform and optimize data before storage. Includes standardizing, enrichment, and masking.
Store
Store and retain data long-term in object storage. Cost-effective, high-availability data with massive compression and no data tiering.
Search
Search data in real time regardless of age, with all data hot for queries.
Designed for the biggest challenges in data
High-volume streaming ingest
Ingest tens of millions of log lines per second in real time, with enterprises typically ingesting at least 1 billion log lines per day.
Long-term retention
Retain data 15 months by default. High-density compression and object storage allow you to keep petabyte-scale data for a fraction of the cost of other solutions.
Hot data
Query in real time on all your data regardless of age. There are no data tiers to manage or performance limitations for historical data.
Full-fidelity data
Retain full-fidelity datasets, not just aggregations or sampled data.
Cost-optimized
Reduce costs compared to other solutions. Cost-optimized so you can keep all your data without compromises.
Full control
Keep data in your own storage for complete control or bring your own cloud (BYOC) and run Hydrolix fully in your infrastructure.
Fully managed or BYOC
- Choose fully managed or bring your own cloud (BYOC). Store your data in your object storage with no vendor lock-in or data egress.
- Run Hydrolix infrastructure (with BYOC) in your own virtual private cloud (VPC) or in multiple clouds for greater control.
- Compatible with all major clouds.
Integrations and compliance, available on day one
- Use the visualization tools you prefer, including Grafana, Kibana, Superset, and Looker.
- Integrate with the Apache Spark ecosystem, including Databricks, AWS EMR, and Microsoft Fabric.
- SOC 2 and GDPR compliant, with granular role-based access control, row and column control, and strict separation between projects for increased data security.
Platform Benefits
Architected for performance and scale, not skyrocketing costs
Traditionally, enterprises have relied on costly, vertically-scaled hardware to deliver real-time analytics. For big data, this approach is challenging to scale and too expensive. Hydrolix combines the power of modern cloud computing with an engineering approach that maximizes the performance of distributed object storage.
All components are stateless and decoupled, allowing each subsystem to scale independently and without resource contention. For example, ingest scales to handle peak events. Meanwhile, you can scale up query during urgent investigations.
All components use massive parallelism to maximize the benefits of cloud computing. For example, a Hydrolix cluster can scale to hundreds of intake heads, all writing partitions in parallel for major events.
With columnar storage, you can query individual columns, leading to more efficient queries than row-based storage. Columnar storage also makes it possible to compress columns individually for greater compaction.
Advanced algorithms optimize compression for each column individually based on data type and other factors. Compression rates are typically 20x-50x, leading to faster read and write times and lower storage costs.
Partitions are small at ingest time (resulting in faster time to insights). Over time, an automated merge service runs in the background and optimizes partitions for improved compaction and query performance.
All components autoscale individually to meet demand, ensuring your infrastructure remains efficient. You can also manually scale to ensure optimal performance during major events or even scale down to zero to reduce compute and costs during off-peak times.
Data is written to object storage. In addition to being cost-effective, it is highly scalable for big data and long-term retention.
Real-time streaming and data transformation (streaming ETL) optimizes data for storage. This process includes standardizing, compressing, and partitioning data for optimized query performance.
Summary tables store real-time aggregations and metrics separate from raw data tables. Summary tables are updated as data is ingested, ensuring they remain highly accurate.
All data is partitioned by time, using partition pruning to make time-based queries efficient.
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.
Data is immutable and append-only, ensuring data integrity and leading to faster reads and writes.
Customer Story
Real-time analytics at super scale
Hydrolix has been battle-tested during some of the world’s biggest events, including the Super Bowl, Olympics, and Black Friday sales. During the 2025 Super Bowl, Hydrolix provided real-time analytics for FOX, including:
Ingesting and storing ~200 terabytes of event data during the big game
17.4 GB/second peak data ingest rate (equivalent to 1.4 petabyte/day)
55 billion high-fidelity records with real-time transformation and compression
5-10 second time to glass (from event time to analysis)
16x raw data compression rates—a 94% reduction
55,000 queries over the course of the event
0.481 second query response time (50th percentile)
Talk to an expert about your use case.
When you need faster insights on big data, from edge to enterprise. Full-fidelity data, no compromises.