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A Semi-Relational Causal Consistent Knowledge Base for Distributed Computing and Persistence with Homomorphic Quantum Secure Ledgers and Pods.

A zero-cost embedded database backed by LMDB, PostgresQL, and Prolog-AllegroSQL's imperative append-log interface. Exploiting SIMD: The Battle With accelerating point and range search queries as instances of the more general configurable combined compute and storage capabilities of memristor skews

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    EinsteinDB introduces novel configurations offering exclusive hybrid data structures that use both conventional CMOS processors/cache hierarchies and memristors for compute/storage!

    EinsteinDB is An embedded Datalog-AllegroSQL (locally immutable) in-memory database and Datalog query engine written in Rust, with Multi-Raft Haskell written consensus, MySQL and SQL parsing with Golang via MilevaDB, and TPC-C Benchmarking with Clojure Jepsen-Ready High-Throughput, Low-Latency Artifacts.

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    1000Txn/sec

    $0.020010Gbps for Read-Heavy workloads

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    99% Availability

    QoS on Ingress via Relativistic Sidecars

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    Under millisecond Reads

    Optimal Stochastic OLAP joins.

Our new data structures combine T-trees, B+- trees, and MemCAM to obtain a balance between search time and lifetime by exploiting a heterogeneous computing environment.

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Our History

Inspired by the problems facing Automated Machine Learning on the Cloud and as a service, EinsteinDB sprung out as a persistence layer for Titus, CHAP, Mantis, and CI/CD pipelines with Jenkins, Jira, and Snowflake.

  • You create a database on minkowski spacelike-tuplespace, put some data in it
  • track changes, do queries and forget about it, or place it into the future heap: minkowski lightlike.
  • A persistent, embedded knowledge base with relativistic linearizable via VioletaBFT: A Multi-Raft invariant with HoneyBadger speculative BFT
  • makes it easy for you to grow and accommodate new kinds of data, for data to synchronize between devices seamlessly, decouple lazily for multiple consumers to share/consume data, and even more so for errors' context switch to be fixed and volatile too.

100Gbps at $0.01000 our revolution.

  • designing CPU-efficient remote storage stacks
  • NVMe-over-RDMA
  • NVMe-over- TCP
  • In Kubernetes, Mesos, Docker and Ansible we trust

Who we are

Karl Whitford, Josh Leder, Joe Pollard, and Ligeia Mare are building practical asynchronous BFT protocols, in-memory databases for Computational Genomics, Quantum Encryption and Computing, Online Machine Learning and Multiplayer Online Game-Physics over TCP/IP

  • liveness guarantees without making any timing assumptions
  • We base our solution on a novel atomic broadcast protocol that achieves optimal asymptotic efficiency.
  • optimize set intersections and the associated data layout to be well-suited for SIMD parallelism
  • through a relativistic linearizable high-latency, high-overhead network reqs.
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bannerThe history of FIDel - From IBM to WHTCORPS

Similar to micro-services deployed in a cloud, FIDel (jobs) are deployed on the EinsteinDB platform. FIDel provides the APIs to manage the life cycle of jobs in both MilevaDB and EinsteinDB (like deploy, update, and terminate), manages the underlying resources by containerizing a common pool of servers, and, similar to a traditional micro-service cloud, allows jobs to discover and communicate with other jobs.

  1. 99.9999% availability

    EinsteinDB has been in production at WHTCORPS since 2018. It began at Netflix as project Mantis, grew into a book, and became a business in 2019. EinsteinDB processes trillions of events and peta-bytes of data every day.

    MilevaDB written in go for EinsteinDB written in rust is designed for business-critical database applications that require fast performance, high concurrency, and automatic scaling. You can scale up to millions of queries per second and 100 TB per database cluster with 15 low latency read replicas.

    MilevaDB decouples compute and storage resources, giving it 6 times faster than standard MySQL databases in high concurrency scenarios.

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  2. 100Gbpsper second

    these architectures are well-known to cater for data rates of 100Gbps

    Each MilevaDB instance for EinsteinDB cluster supports to scale up to 100 TB and can be scaled out to up to 16 nodes. Each node can have up to 88 vCPUs.

    The relational nature of Prolog makes its co-habitation with relational database systems an attractive proposition. Not only databases can be viewed and used as external persistent storage devices that store large predicates that do not fit in memory, but it is also the case that Prolog is a natural choice when it comes to selecting an inference engine for database systems. T

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  3. 2014March 24th

    From Stream Processing at Netflix to Bipartite Beyond-Relational Workloads at CloudKitchens.

    For Netflix to be successful, it has to be vigilant in supporting the tens of millions of connected devices that are used by the 40+ million members throughout 40+ countries. These members consume more than one billion hours of content every month and account for nearly a third of the downstream Internet traffic in North America during peak hours in 2014

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  4. 2015November 10th

    Kubernetes

    Kubernetes launched with a great API and CLI that developers love. At Mesosphere, we saw its potential early and invested in bringing it to Mesos.

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Why Choose Us?

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Data for All Your People

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A New Breed of AI

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Analytics Business

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aboutHow It's Work

The Data Science Process

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Frame the Problem

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Collect the Raw Data

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Process the Data

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Explore the Data

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Perform In-depth Analysis

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Communicate Results

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aboutTeam Members

Our Data Scientist

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Merv Adrian

Data Management
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Kirk Borne

Data Scientist
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Carla Gentry

Analytical Solutions
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Marie Curie

Data Scientist
aboutTestimonials

What Our Partners are saying

“The service control platform is the next-gen of traditional API management” Kong CTO and co-founder Marco Palladino

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