Scale Now or Pay Later

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Choosing a Data Platform Built for Hardware at Scale

Selecting a data platform is like choosing an engine. Unfortunately, many hardware teams don't inspect what’s under the hood of their data platform until performance starts to degrade under inevitable stress.

Hardware engineering teams deal with data defined by relentless complexity: high-frequency data streams, rapidly evolving schemas, deeply nested structures, and the imperative to move fast without sacrificing accuracy. Generic databases like ClickHouse, InfluxDB, TimescaleDB, and other off-the-shelf solutions often buckle under these nuanced demands. Typically optimized for analytics over flattened structures or dashboarding short-term metrics, these databases fall short when telemetry becomes complex and high-frequency.

Sift was purpose-built to address precisely what these generic solutions overlook: nested structures, evolving schemas, extreme cardinality, and consistent performance throughout the entire telemetry lifecycle.

Flat Schemas: Fast until they’re not

Databases like ClickHouse, InfluxDB, and TimescaleDB are engineered around simplified schemas. Flat, denormalized tables accelerate dashboards, but real hardware telemetry is inherently:

Run-based: Every test produces unique outputs.
Asset-oriented: Hardware is defined by identifiers that change and evolve.
Component-driven: Systems aren’t monoliths—they’re assemblies.
Channel-rich: Measurements, signals, and events must be tracked precisely.

Flattening this telemetry at ingestion or query time erodes critical context, burdens pipelines, and undermines long-term fidelity. Generic databases push complexity back onto engineers, forcing tedious reconstruction or convoluted SQL queries.

Sift was designed differently. By natively structuring telemetry into runs, assets, components, and channels, Sift preserves the semantic context of hardware data. Instead of bending engineering data to fit a general-purpose schema, Sift aligns directly with how hardware teams already think and build.

Flexible Scaling: Decoupling storage and compute

Generic databases typically couple compute and storage tightly, creating predictable scaling bottlenecks:

Monolithic resource contention: ClickHouse nodes juggle ingest, storage, merges, and queries simultaneously, leading to over-provisioning and downtime risk.
Inefficient write paths: ClickHouse, InfluxDB, and TimescaleDB recommend batched inserts, mismatching the continuous, small-stream nature of telemetry data and degrading real-time performance.
Operational complexity: Distributed setups of these databases introduce significant fragility and overhead in sharding, replication, and coordination.

Sift decouples compute from storage, leveraging object storage for cost-effective, limitless scalability. Its distributed query layer ensures consistent performance, whether analyzing live streams or historical data.

Built for the hardware telemetry lifecycle

Generic databases focus on “hot data” such as recent logs, quick dashboards, but hardware validation demands continuous anomaly detection, regression analysis, and compliance reporting across extensive histories:

ClickHouse, InfluxDB, and TimescaleDB performance degrades with historical data, pushing engineers into complex archival strategies or costly schema adjustments.‍
Sift delivers sustained performance across real-time ingestion, live visualization, historical queries, automated validation, and continuous reporting, without data silos or schema drift.

Precision and Context Matter

Telemetry isn’t just “data”—it’s structured, precise, and context-rich:

Capability	Sift	ClickHouse / InfluxDB / TimescaleDB	Why it Matters
Schema Flexibility	Data is stored in hardware-native shapes, such as Runs, Assets, Components, Channels	Require engineers to reshape telemetry into flat schemas. Queries depend heavily on SQL or specialized query languages, and recovering hardware context means reconstructing joins across tables.	Store data in the same shape as the hardware, so that context is preserved and engineers can efficiently find and analyze their data.
Data primitives	11+ telemetry-specific types (ENUM, BIT_FIELD, DOUBLE, INT64)	General types (arrays, JSON, integers, floats, timestamps)	Enables data unification into a single source of truth, maintains semantic precision
Time precision	Zeptosecond (10⁻²¹s) precision	Up to nanoseconds (ClickHouse, InfluxDB); microseconds/nanoseconds (TimescaleDB)	Essential for high-performance hardware requiring fine-grained resolution
Storage model	Decoupled compute and object storage	Typically coupled; newer cloud variants decoupled but with complexity	Enables low-cost scaling without performance tradeoffs
Write Efficiency	Optimized for real-time, high-frequency ingestion	Requires batched writes for optimal performance	Matches telemetry data flow, avoiding delays or visibility gaps
Lifecycle Management	Comprehensive, automated	Limited, requiring manual intervention or complex setups	Reduces operational overhead, enhances long-term reliability
Multiple Timestamps	Native support (UTC, mission-elapsed, GPS time, etc.)	Single primary timestamp per record; additional timestamps as fields with limited indexing and performance support	Essential for accurate telemetry analyses requiring multiple precise time references

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Sift’s Difference: Engineering at Scale

Sift eliminates hidden operational costs of generic solutions:

No schema flattening or ETL overhead.
Infinite scalability with decoupled architecture.
Telemetry-native precision and data primitives.
Continuous, comprehensive telemetry lifecycle support.

With Sift, Parallel Systems cut its database footprint by 85% and eliminated the operational overhead of managing local disk-based scaling. Astrolab saved over 5,000 engineering hours by adopting Sift early knowing firsthand how internal tools can buckle under real-world test complexity.

The Verdict: Scale now or pay later

If you wouldn’t pick a generic off-the-shelf engine for your hardware, then don’t settle when it comes to your data platform. Generic solutions quickly hit limitations under real-world telemetry demands.

Sift was built precisely for handling complexity, sustaining performance, and supporting iterative validation.

Waiting to address telemetry challenges only compounds risk. Sift provides the architectural stability needed to keep pace with evolving hardware programs.