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Today, distributed cloud infrastructure provider Sync Computing emerged from stealth mode to announce it raised $6.1 million in a funding round. The organization also announced the launch of Sync Autotuner for Apache Spark and Sync Orchestrator.
Sync Autotuner for Apache Spark is designed to enable users to upload logs from Spark jobs and provides guidance on optimal settings for performance, balance, and economy, while Sync Orchestrator provides organizations with an accelerated scheduling algorithm that can automatically calculate the best configurations to use in the cloud.
Both tools enable decision makers to calculate the best configurations to use in the cloud to run data and machine learning workloads with optimal performance and cost-efficiency.
The news comes as the company was recently privately awarded a $1 million contact from the Department of Defense (DOD). The funding will enable Sync Computing to enhance its position in the data infrastructure market, a market researchers value at over $66 billion, as more organizations manage workloads in the cloud following the COVID-19 pandemic.
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A new way to manage data pipelines
Sync Computing aims to offer technical decision makers a solution to optimally and cost-efficiently provision cloud pipelines for data and machine learning workloads, so that they can avoid overspending on cloud resources while simultaneously enabling engineers to spend less time on tedious and complex administration tasks.
“Technical decision makers need to be able to take full advantage of the benefits and flexibility afforded by the cloud without falling victim to its inherent complexity, uncontrollable performance, and skyrocketing costs, ” said CEO and cofounder Jeff Chou in an interview.
Unfortunately, many organizations don’t have the in-house expertise to do this. “The problem we are solving is that deploying large distributed computing applications and pipelines (data/ML/scientific workloads) is complex and typically requires a PhD in high performance computing to do well, causing slow performance, poor reliability, and high costs for enterprises. Furthermore, there exists a massive talent shortage for experts in this space. At Sync, our solution allows organizations to instantly allocate cloud resources based on the only things that matter, cost and time — not low level infrastructure.”
It’s a challenge that cofounders Jeff Chou and Suraj Bramhavar are in an ideal position to address, both emerging out of MIT with PhDs and having conducted thorough research on solving complex combinatorial optimization problems.
Hitting the ground running in serverless computing
While Sync Computing is the newest entrant to the data infrastructure market, it’s moving fast, not just due to its recent funding announcement and $1 million DOD contract, but also with Sync Autotuner’s success in helping language education tool Duolingo to reduce its job cluster size fourfold and job costs by twofold.
These early wins position Sync Computing to compete against other established providers in the market.
Perhaps the most significant competitor is Amazon Web Services (AWS) and its serverless computing offering AWS Lambda, a tool that enables enterprises to run code at scale without provisioning infrastructure, and played a key role in driving the organization’s revenue of $4.88 billion last year.
Another prominent competitor is Google Cloud, which recently announced the launch of distributed cloud computing solutions like Google Distributed Cloud Edge and Google Distributed Cloud Hosted at Google Cloud Next ’21 to grow its cloud ecosystem, following a successful year that saw revenue climb to $4.63 billion.
However, Chou says that Sync Computing has an inherent advantage over these products. “Typical serverless products do not focus on performance (runtime and cost), and typically end up costing enterprises more.”
Chou said that Sync is built to system designed to sift through infinite compute resources available in the cloud and find those that are optimally suited directly to the needs of the end user or organization.
The funding round was led by Moore Strategic Ventures and National Grid Partners.
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