Zettar Inc. (Zettar) builds and delivers a deep-tech-powered, simple, scalable, and efficient at-scale data movement manager. The product is ideal for distributed data-intensive engineering and science workloads such as for genomics, life sciences, Oil & Gas, AI, machine learning, transporting data for large scale IoT deployments, autonomous vehicle fleets, smart cities, EDA, Media & Entertainment Studio post-production, light sources (large lasers), accelerators, large telescopes. Note that it is excellent for tackling today’s ever growing edge to core/cloud use cases.
The Zettar team has rich first-hand solution architecture experience in helping tier-1 customers in the biopharmaceutical, Oil & Gas, Media & Entertainment Studios, and supercomputing centers in different countries. As a result, even as a software company, the Zettar engineering team has a deep and comprehensive understanding and expertise of the entire infrastructure stack, storage, computing, networking (including network security).
Furthermore, from the engagement supporting the highly ambitious data movement requirements (>= 1Tbps point-to-point by 2024) of Linac Coherent Light Source II (LCLS-II), a premier U.S. DOE Exascale Computing preparation project hosted at the SLAC National Accelerator Laboratory in Menlo Park, California, all members have gained extensive experience applying the U.S. DOE Exascale Initiative’s “co-design” principle – integrated consideration of storage, computing, networking, and concurrent software for optimal performance. Thus, Zettar is a genuine deep tech startup. Hence, working with Zettar will help your business to gain such valuable experience as well.
Zettar helps enterprises overcome data gravity by providing them the ability of at-scale data movement, on-prem, in the cloud, or any combination thereof, across any distance. We are trusted by the world’s leading companies, well-known national labs and supercomputing centers, and industry-leading partners. You are invited to review the latest Zettar news.
Zettar, DataDirect Networks, and NVIDIA collaborated on a fully optimized at-scale AI data migration solution showcase for SC21
Watch Video Presentation: Accelerating At-Scale AI Data Migration
Dr. Chin Fang, CEO, Zettar Inc.
Monday, December 15, 2021. NVIDIA VIRTUAL THEATER
Zettar presented at Intel SC21 DevHub
Zettar and U.S. DOE Energy Science Network presented at 2021 Rice Oil & Gas High-Performance Computing Conference, Technical Program
Watch Video Presentation: High-Performance Data Movement Services – DTNaaS
Dr. Chin Fang, CEO, Zettar Inc. and Dr. Ezra Kissel, Network Engineer, U.S. DOE Energy Sciences Network (ESnet)
Friday, March 5, 2021. Houston, Texas
Zettar presented at HPC Knowledge Meeting 2021
June 08, 2022 |Intel Corporation
Solution Brief; Health and Life Sciences; Transporting High-Speed Instrument Data — IT teams tasked with moving high data volumes can benefit from Zettar’s scale-out, highly available, petabyte-scale data-transfer software solution, powered by Intel.
November 14, 2021 |HPCwire
Zettar to Showcase Accelerating At-Scale AI Data Migration at SC21
PALO ALTO, Calif., Nov. 14, 2021 — Zettar today announced it will showcase a fully optimized, at-scale, enterprise AI data migration solution, developed in collaboration with DDN and NVIDIA, at Supercomputing 2021 (Nov. 14-19, 2021).
November 14, 2021 |Forbes
NVIDIA Envisions AI For Everything
For SC21 NVIDIA, DDN and a data migration solution company, Zettar collaborated to create a testbed for feeding large-scale AI training on GPU from data at the edge, in the cloud or on-premises storage. Zettar’s at-scale AI data migration solution is production ready, using commercially available hardware and software, and attains full storage and network performance. This Forbes article, written by the respected Silicon Valley based enterprise tech analyst, Dr. Thomas Coughlin, is based on the NVIDIA SC21 Virtual Theater presentation “Accelerating at-scale AI data migration“
March 25, 2021 |HPCWire
DOE Technical Report: When to Use rsync?
The US Department of Energy (DOE) SLAC National Accelerator Laboratory has released a new Technical Report and associated open source testing tools. The report describes and illustrates a rigorous, comprehensive, and fully automated investigation about the highly popular rsync data copying tool. It answers a key question “When to use rsync?” We believe this is the first study at this level and scope, carried out using two expertly designed flexible testbeds: Zettar Inc’s and the U.S. DOE ESnet’s 100G SDN testbed.
First released in 1996, rsync remains the go-to data mover for many IT professionals. Yet the world is facing exponential data growth. So,
- Is rsync still the proper tool to use for almost every data moving task?
- If it is still useful, what are the proper range of operations?
- How about the effectiveness of some rsync-based tools that run multiple rsync instances?
- Are there any alternatives?
The report is available at https://slac.stanford.edu/pubs/slactns/tn06/slac-tn-21-001.pdf.
The testing tools are available at https://github.com/fangchin/test_rsync. They enable any interested parties to use the same methodology to obtain more results in their own environment.
March 25, 2021 |U.S. Department of Energy, Office of Scientific and Technical Information
When to use rsync
February 3, 2021 |InsidHPC
Elbencho – A New Storage Benchmark for AI
February 3, 2021 |HPCWire
Elbencho – A New Storage Benchmark for AI
February 3, 2021 |InsideHPC
Elbencho – A New Storage Benchmark for AI
January 27, 2021 |
Stories from Stanford, Cloud Storage and the Future of Moving Data at Scale & Speed
January 08, 2021 | Forbes
Data Movement Types Impact Storage Requirements
The explosion of machine generated data will require advanced automated and controlled data movement to make optimal use of that data. Data movement tools and digital storage required for making use of this data depends upon the size and type of data and the means and uses for that data. This Forbes article, written by the respected Silicon Valley based storage analyst, Dr. Thomas Coughlin, is based on the SLAC Technical Note, SLAC-TN-20-004 that Dr. Les Cottrell and Dr. Chin Fang co-authored and published on December 30, 2020 on the SLAC SciDoc Website https://stanford.io/3oveo3d.
December 25, 2020 | github.com
Storage sweep tools for elbencho
The storage sweep tools for elbencho empower users to get results for small files, large files and everything in between with a simple command and even including the automatic creation of a nice graph in the end.
December 21, 2020 | U.S. Department of Energy, SLAC National Accelerator Laboratory (SLAC)
Data Movement Categories
December 17, 2020 | U.S. Department of Energy, Energy Sciences Network (ESnet)
Zettar zx Evaluation for ESnet DTNs
ESnet is prototyping a Data Transfer Node as-a-Service (DTNaaS) capability that aims to provide optimized, on-demand data movement tools and endpoints to users of the network. Zettar offers a high-performance data movement solution, zx, that integrates with a number of storage technologies and provides mechanisms for API automation. An evaluation of the solution within the ESnet testbed environment was performed over the duration of approximately 2 months. The performance of disk I/O and network interactions were explored in a containerized software environment.
June 12, 2020 | Forbes
Data Center Infrastructure And Transport
The respected Silicon Valley based storage analyst, Dr. Thomas Coughlin wrote a blog about data center infrastructure and the importance of data transport to the effective use of the infrastructure. Two Zettar zx software’s prized features: predictable site-to-site transfer performance and symmetry were pointed out.
March 4, 2020 | 2020 Rice Oil & Gas High Performance Computing Conference
Moving Massive Amounts of Data across Any Distance Efficiently
In this talk, Zettar shared its intense experience in moving data at scale and speed. Key points: the importance of such endeavors in this age of hybrid and multi-cloud; the current state of the art; common misconceptions and myths to avoid; results from a multi-vendor joint project and a world-leading production trials are used for illustration.
Supercomputing Asia 2019, Data Mover Challenger, Winners Announced!