The potential of geospatial data is vast, and its value increases when combined with temporal, textual, or other nonspatial features. However, managing and analyzing geospatial data at scale is inherently challenging due to the computational and storage requirements, especially when additional optimization is required for combined features. While there are numerous solutions for big spatial data management, many struggle to support non-spatial operations effectively, with limited options in the open-source space that excel at handling both spatial and non-spatial queries comprehensively. This seminar explores scalable geospatial data management and analytics, focusing on approaches and techniques that address these challenges. Participants will gain hands-on experience in processing complex queries involving spatial, temporal, and textual features using a real-world Big Data Management System. Through practical examples and exercises, attendees will learn how to tackle the complexities of scalable geospatial analytics in modern data systems.

Couchbase Capella is a scalable document-oriented database service in the cloud. Its existing Capella Operational service is based on a shared-nothing architecture and supports high volumes of low-latency queries and updates for JSON documents. Its new Capella Columnar cloud service complements the Operational service. The Capella Columnar service supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents that can originate from a variety of Couchbase and non-Couchbase data sources and formats and can either be stored and managed by the Capella Columnar service or externally stored and accessed on demand at query time. This paper describes the new Capella Columnar service, looking both over and under the hood.

Enterprises today are becoming ever more reliant on real-time information and analytics for steering and optimizing their businesses. As a result, database system architectures with hybrid data management support - known as HTAP (Hybrid Transactional/ Analytical Processing) or HOAP (Hybrid Operational/Analytical Processing) support - are appearing and increasingly gaining traction in both the commercial and research sectors. Hybrid platforms first appeared in the relational world, and they are often linked in that world to concurrent high-end server technology trends such as columnar storage and mainmemory data management.This paper focuses on hybrid platforms, but in a very different world - in the document data management, or NoSQL, world. In this work, we report on a first effort to characterize the hybrid performance of a scalable document database system that purports to provide what one might call "HOAP for JSON". We have borrowed from and extended the TPC-C benchmark to study the performance of Couchbase Server, a horizontally scalable NoSQL platform that offers HOAP via the combination of its Data/Index/Query and Analytics Services. Our results attest to the importance of architecting a NoSQL platform for HOAP, both in terms of its approach(es) to query processing and its provision of performance isolation for the operational and analytical components of a mixed workload. We share our initial results, the insights that we have gained thus far, and our thoughts on future work related to benchmarking such systems.

Summary Traditional relational database systems handle data by dividing their memory into sections such as a buffer cache and working memory, assigning a memory budget to each section to efficiently manage a limited amount of overall memory. They also assign memory budgets to memory‐intensive operators such as sorts and joins and control the allocation of memory to these operators; each memory‐intensive operator attempts to maximize its memory usage to reduce disk I/O cost. Implementing such memory‐intensive operators requires a careful design and application of appropriate algorithms that properly utilize memory. Today's Big Data management systems need the ability to handle large amounts of data similarly, as it is unrealistic to assume that truly big data will fit into memory. In this article, we share our memory management experiences in Apache AsterixDB, an open‐source Big Data management software platform that scales out horizontally on shared‐nothing commodity computing clusters. We describe the implementation of AsterixDB's memory‐intensive operators and their designs related to memory management. We also discuss memory management at the global (cluster) level. We conducted an experimental study using several synthetic and real datasets to explore the impact of this work. We believe that future Big Data management system builders can benefit from these experiences.

Couchbase Server is a highly scalable document-oriented database management system. With a shared-nothing architecture, it exposes a fast key-value store with a managed cache for sub-millisecond data operations, indexing for fast queries, and a powerful query engine for executing declarative SQL-like queries. Its Query Service debuted several years ago and supports high volumes of low-latency queries and updates for JSON documents. Its recently introduced Analytics Service complements the Query Service. Couchbase Analytics, the focus of this paper, supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents. This paper describes the Analytics Service from the outside in, including its user model, its SQL++ based query language, and its MPP-based storage and query processing architecture. It also briefly touches on the relationship of Couchbase Analytics to Apache AsterixDB, the open source Big Data management system at the core of Couchbase Analytics.