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Encyclopedia of Big Data Technologies pp 1522–1531Cite as

SnappyData

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Introduction

An increasing number of enterprise applications, particularly those in financial trading and IoT (Internet of Things), produce mixed workloads with all of the following: (1) continuous stream processing, (2) online transaction processing (OLTP), and (3) online analytical processing (OLAP). These applications need to simultaneously consume high-velocity streams to trigger real-time alerts, ingest them into a write-optimized transactional store, and perform analytics to derive deep insight quickly. Despite a flurry of data management solutions designed for one or two of these tasks, there is no single solution that is apt for all three.

SQL-on-Hadoop solutions (e.g., Hive, Impala/Kudu and SparkSQL) use OLAP-style optimizations and columnar formats to run OLAP queries over massive volumes of static data. While apt for batch processing, these systems are not designed as real-time operational databases, as they lack the ability to mutate data with transactional consistency, to...

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Notes

  1. 1.

    Although IndexedRDD (Indexedrdd for apache spark) offers an updatable key-value store (Indexedrdd for apache spark), it does not support colocation for high-rate ingestions or distributed transactions. It is also unsuitable for HA, as it relies on disk-based checkpoints for fault tolerance.

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Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Barzan Mozafari

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  1. Barzan Mozafari
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Correspondence to Barzan Mozafari .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Mozafari, B. (2019). SnappyData. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_258

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