Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.

Content delivery networks (CDNs) are critical for minimizing access latency in the Web as they efficiently distribute online resources across the globe. But since CDNs can only be enabled on the scope of entire websites (and not for individual users or user groups), the effects of page speed acceleration are often quantified with potentially skewed before-after comparisons rather than statistically sound A/B tests. We introduce the system Beaconnect for collecting and analyzing Web performance data without being subject to these limitations. Our contributions are threefold. First, Beaconnect is natively compatible with A/B testing Web performance as it is built for a custom browser-based acceleration approach and thus does not rely on traditional CDN technology. Second, we present our continuous aggregation pipeline that achieves sub-minute end-to-end latency. Third, we describe and evaluate a scheme for continuous real-time reporting that is especially efficient for large customers and processes data from over 100 million monthly users at Baqend.

The detection of duplicates is an essential task in data cleaning and integration and has steadily gained importance especially for researchers and practitioners that need to process and integrate large volumes of potentially unclean data on a daily basis. To evaluate the quality and performance of duplicate detection algorithms, labeled test data are required that provide information on the contained duplicates. Current approaches for generating test data, however, are either not scalable (and therefore limited to small datasets) or not able to generate realistic data values and errors, especially outdated values. In this paper, we propose a scheme for generating test datasets that addresses both these issues and present a test dataset generated with it. Our approach relies on using historical data from the North Carolina voter register which (1) is realistic as it contains actual voter data and (2) facilitates generating realistic duplicates through the fact that current data values were collected at every election through manually filled out applications. The generated test dataset comprises more than 120 million records with up to 90 attribute values each. To the best of our knowledge, we are the first who providerealistic test data for duplicate detection at this scale.

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach that has been serving customers at the Database-as-a-Service company Baqend since July 2017.

Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today's Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.

Traditional databases are optimized for pull-based queries, i.e. they make information available in direct response to client requests. While this access pattern is adequate for mostly static domains, it requires inefficient and slow workarounds (e.g. periodic polling) when clients need to stay up-to-date. Acknowledging reactive and interactive workloads, modern real-time databases such as Firebase, Meteor, and RethinkDB proactively deliver result updates to their clients through push-based real-time queries. However, current implementations are only of limited practical relevance, since they are incompatible with existing technology stacks, fail under heavy load, or do not support complex queries to begin with. To address these issues, we propose the system design InvaliDB which combines linear read and write scalability for real-time queries with superior query expressiveness and legacy compatibility. We compare InvaliDB against competing system designs to emphasize the benefits of our approach. To validate our claims of linear scalability, we further present an experimental evaluation of the InvaliDB prototype that has been serving customers at the Database-as-a-Service company Baqend since July 2017.

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching.