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Manetu Selects YugabyteDB to Power Its Data Privacy Management Platform

Privacy Management Provider Achieves High Scalability, Low Latency and Support for Multiple Use Cases Including High-Cardinality Time Series Data

Yugabyte, the leader in open source distributed SQL databases, announced that Manetu selected YugabyteDB as its database of choice for its Consumer Privacy Management (CPMⓇ) platform. Manetu chose Yugabyte’s distributed SQL database for its high scalability, flexibility to support multiple use cases and comprehensive multi-region deployment options, which has allowed Manetu to process high-cardinality datasets at low latency while future-proofing its business by enabling privacy management to scale to billions of identities.

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“When building out the Manetu platform, we knew we wanted a distributed database that was both fast and functional given the amount of encrypted data we manage, but databases we previously worked with had limitations. We searched for ways to overcome those limitations and found what we needed in YugabyteDB,” said Greg Haskins, Chief Technology Officer, Manetu. “YugabyteDB offers all the benefits of a distributed database and fixes all the shortcomings that made other solutions difficult to work with. Recently, we leveraged YugabyteDB in a new time-series use case that was not even possible with other databases. In addition to the product capabilities, we’ve had a really positive experience with Yugabyte support. The team is incredibly responsive in providing us with solutions and best practices to maximize YugabyteDB’s capabilities.”

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Manetu’s platform connects consumers directly with the data that enterprises hold on them, replacing traditional data subject access requests with a self-service web portal. To achieve this, Manetu’s platform connects with multiple enterprise systems to aggregate, encrypt and store information, and provide a single view of a consumer’s data footprint. Manetu’s consumer-facing portal gives users control to review permitted use policies and fulfill the data requests while the business-facing portal simplifies enterprise compliance with international privacy laws by consolidating all sources of personally identifiable information (PII) into a single, secure, state-of-the-art privacy vault. This is achieved through a combination of microservices, written in a mix of Clojure and Go on Kubernetes, including an attribute service for PII encryption and storage.

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“Consumers should be able to hold enterprises accountable for appropriately protecting the integrity and privacy of their personal data, and Manetu is helping bridge the transparency gap,” said Karthik Raganathan, Co-Founder and CTO, Yugabyte. “Manetu’s platform enables consumers to interact securely and in real time with the information that enterprises hold about them; building a platform that enables all of this is an engineering feat in and of itself, and it’s imperative that the data tier be able to keep up with the unique data requirements and fast pace of innovation. We are excited to partner with Manetu as they continue to scale and provide data privacy support to consumers and enterprises around the world.”

Manetu’s attribute service requires fine-grained transaction support and secondary indices in order to offer an RDF triple-store to its customers as the basis for securely storing PII while meeting SLAs. Through previous experience with Cassandra, Manetu was aware of Cassandra’s limitations and operational complexity for the attribute service use case. In addition, Manetu implemented a new activity log service that required a time series database. While it initially turned to various time series systems, Manetu quickly discovered that as a result of the high cardinality of data, they couldn’t scale to meet the ingest rate or required latency to generate the requisite graphs for dashboard presentation. Since adopting YugabyteDB to power its services, Manetu has not only achieved global scale and maintained peak performance, but it also enabled new time series use cases that process high-cardinality data.

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