New startups are always emerging to address challenges and leverage opportunities in innovative ways. These companies bring fresh approaches to accelerating digital transformation, expanding what’s possible with analytics, breaking down silos, enhancing security, and more. Here are 15 startups DBTA thinks are worth watching in 2022.
Credo AI (credo.ai)—offers a single platform for managing compliance and risks of AI deployment at scale that delivers tools to help companies ensure their AI systems and machine learning models are aligned with ethical, regulatory, and business requirements
Coalesce (https://coalesce.io)—the data transformation company, provides the Coalesce Data Transformation platform to simplify the modeling, cleansing, governance, and documentation of data, while increasing data engineer productivity and insights to tackle today’s data-intensive architectures
DataOps.live (www.dataops.live)—a software company dedicated to helping enterprise companies build and lifecycle manage data applications and data products using Snowflake—enabling them to secure and govern their data, automate testing, orchestrate data pipelines, build full data applications, and deliver new value to stakeholders
Domino Data Lab (dominodatalab.com)—provider of an Enterprise MLOps platform that breaks down silos and increases governance to help companies generate data science breakthroughs at greater velocity and scale, free from ties to one form of infrastructure or tool
Edge Delta (www.edgedelta.com)—is taking a modern approach to observability by giving enterprises the ability to use distributed stream processing and federated machine learning capabilities for intelligent, dynamic, and automated data routing to optimal destinations
Firebolt (www.firebolt.io)—provides a cloud data warehouse for builders of next-gen analytics experiences that was built from the ground up to deliver order-of-magnitude performance gains
Hackolade (https://hackolade.com)—a third-party data modeling tool that started as a MongoDB–specific tool to help visualize and implement MongoDB data models, and has added support for other NoSQL databases, for relational databases that implement JSON support, and even for API definitions
InsightCyber (insightcyber.com)—a cybersecurity provider applying advanced AI to cyber-physical security that, since its founding in 2017, has been working to build an AI-powered managed security service for society’s critical industries and infrastructure
MinIO (https://min.io)—secure, S3 compatible object storage that is native to Kubernetes, and available on every public cloud, every Kubernetes distribution, the private cloud and the edge
OtterTune (https://ottertune.com)—seeks to provide users with a system backed by machine learning (ML) that automatically generates configuration settings to improve database performance and efficiency
Shoreline.io (https://shoreline.io)—the incident automation company, provides a platform designed to improve availability and reduce toil for site reliability engineers (SREs) in their production environments by making it easy to create bots that resolve issues in seconds when an alarm is raised rather than hours using manual processes
SingleStore (www.singlestore.com)—provides a single database that allows customers to forego cobbling together multiple previous-era databases that can’t deliver on the speed, simplicity, or efficiency required to support data-intensive applications.
Styra (www.styra.com)—the founder and maintainer of Open Policy Agent (OPA) enables enterprises to define, enforce, and monitor policy across their cloud-native environments using a combination of open source and commercial products to provide security, operations, and compliance guardrails to protect applications, as well as the infrastructure they run on
Sync Computing (https://synccomputing.com)—a deep tech distributed cloud infrastructure company that harnesses the computational power of physics to find the best way to provision the cloud for data/ML workloads
Wallaroo (www.wallaroo.ai)—provides a platform that makes it fast and simple to get AI algorithms live against production data, and is built on four core components: MLOps, a distributed processing engine, data connectors, and audit and performance metrics