ScyllaDB NoSQL for Data Analytics

Increase Speed, Scale, and Efficiency for Growing Analytic Workloads

ScyllaDB’s close-to-the-metal architecture extracts the full computing power of modern infrastructure to deliver higher throughput and lower latency at scale – often at far lower cost. Written from the ground up in C++, ScyllaDB doesn’t suffer from Java overhead and its effects on performance. Data is distributed across clustered servers per CPU core and free from resource sharing operations. Capable of 1M+ operations per second per server, ScyllaDB scales linearly and easily – from gigabytes to petabytes – and without interruption.

Proven Results

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Meshify’s Scalability is No Accident

23M Data Points

Servicing the insurance industry, Meshify wireless sensors capture environmental data that’s used to predict and prevent accidents, thereby averting claims. To overcome SQL scalability challenges and by outperforming NoSQL alternatives, Meshify moved to ScyllaDB for multiple time series analytic applications.

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Augury Sees More in Machines

75% Fewer Breakdowns

By delivering real-time insights and historical analytics about the condition of machines on the manufacturing floor, Augury helps their customers perform the right maintenance at the right time. In addition to leveraging ScyllaDB for millisecond queries of time series data, Augury performs trend analysis of IoT data with sub-second batch reads.

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Sharechat Times Things Perfectly

2.5B Posts per Month

Sharechat is India’s top social media platform with well over one hundred million active users. Moving to ScyllaDB to achieve single millisecond latency and one million ops/sec throughput, Sharechat runs multiple clusters spanning chat, ads data management, ML feature store, and real-time notification applications at scale.

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SkyElectric Keeps a Sunny Outlook

300K Data Points/Minute

Providing smart solar energy for the developing world, SkyElectric systems are among the most advanced with built-in AI and battery management. To achieve performance and ease-of-use needs at scale, SkyElectric relies on ScyllaDB for analysis of time series data and with sub-millisecond read latency.

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Technical Advantages of ScyllaDB for Analytics

Identifying conditions. Discovering patterns. Recognizing correlations. Gaining insights. Expert analysis of millions of application transactions and billions of data points provide business lines with valuable information to strengthen customer relations, improve efficiency, and fuel innovation. Yet data volume, complexity, and change present analytic challenges often addressed using both science, art, and specialized databases – especially for time series and concurrent, near-instant analytics.

ScyllaDB databases provide a unique blend of speed, scale and efficiency coupled with flexible NoSQL data modeling to power analytics and make data-driven decisions in real-time.

Built for Speed

Analyze data within a continuous flow, in series, or in batch. ScyllaDB offers wide-column NoSQL data modeling, delivering faster reads over key-value and document stores at high volume. Further boosting query speed are shard-aware drivers that connect client requests directly to the exact CPUs within nodes that are responsible for the data. And ScyllaDB tunes itself automatically to maintain optimal read and write speeds and minimize administrative overhead.

Lower Latency

Run OLTP and OLAP workloads on the same database – while preserving application performance – to reduce TCO. ScyllaDB’s write path follows the LSM-tree structure, delivering fast writes at high volume with immediate reads – ideal for real-time demands. A variety of compaction strategies are designed for different workloads, such as Size-tiered or a unique Incremental Compaction that can reduce storage costs by a third, with compaction statistics provided by the ScyllaDB Monitoring Stack on a cluster and per node basis. Time to Live automatically deletes expired data to help maximize storage efficiency.

High Availability

ScyllaDB’s architecture is also highly fault tolerant with no single point of failure. Data is automatically replicated across multiple nodes with eventual consistency in milliseconds – even between data centers in different geographic regions – to prevent data loss and help ensure round-the-clock application availability.

Related Use Cases

Apache® and Apache Cassandra® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Amazon DynamoDB® and Dynamo Accelerator® are trademarks of Amazon.com, Inc. No endorsements by The Apache Software Foundation or Amazon.com, Inc. are implied by the use of these marks.

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ScyllaDB NoSQL for Data Analytics

Increase Speed, Scale, and Efficiency for Growing Analytic Workloads