Storage engine architecture
Evaluate or design storage internals around the latency, throughput, memory, durability, and cost profile your application requires.
18+ years in data systems
Data systems.
Dependable.
Independent expertise in storage engines and data stores—performance, tunability, self-tuning, and indexing aligned with application needs.
ExploreThe core idea
A data store succeeds when its internal tradeoffs match the workload above it, the hardware below it, and the guarantees it must provide throughout the data lifecycle.
Where we go deep
Systems expertise.
Applied precisely.
Performance & tunability
Benchmark, diagnose, and tune systems while making their workload-dependent tradeoffs and control knobs explicit.
Indexing & access methods
Choose and shape LSM trees, B+ trees, bitmap indexes, Bloom filters, and data layouts to serve real access patterns.
Privacy & observability
Design retention and deletion guarantees into the storage layer—and the instrumentation needed to measure, audit, and verify system behavior.
Adaptive data systems
Design self-tuning capabilities that respond deliberately to changing workloads, data properties, and hardware.
Our approach
Rigorous thinking.
Practical delivery.
01
Workload before mechanism
We begin with the application, its access patterns, and its constraints—not a favorite technology.
02
Tradeoffs, measured
We quantify the cost of reads, writes, memory, space, and maintenance before recommending a design.
03
Adaptation where it pays
We add tunability and self-tuning only where changing conditions make it valuable.
04
Make guarantees observable
Performance, retention, and deletion behavior should be measurable in operation—not assumed from design.
Research-backed practice
Research behind
the practice.
Storage engines, data structures, data stewardship, and hardware/software co-design
Multiple NSF awards and industry research grants
Academic and industry research across Boston University, Harvard, EPFL, Meta, and IBM
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