Insurance is exposed to a variety of fraudulent schemes: from sharing an insurance plan after divorce to withholding critical information that may influence risk assessment. In this regard, blockchain can address some of the pain points found in the insurance industry. This study explains how the technology helps to improve security, mitigate fraud, automate record sharing between organizations, and cut operational costs. Finally, the paper covers some technical aspects of implementation and highlights scenarios where blockchain fits best.
This 12-page technical report provides guidelines for optimizing Apache Spark and two of its modules: Spark Core and Spark SQL. The study features the performance results of Apache Spark before and after optimization. The paper also includes four comparative diagrams and four descriptive tables, which illustrate performance differences in detail.
This technical report provides an in-depth analysis of the three leading NoSQL systems: Couchbase Server v6.0, MongoDB v4.0, and DataStax Enterprise v6.7 (Cassandra). The comparison evaluates the solutions across 20+ criteria, such as performance, availability, ease of installation and maintenance, data consistency, fault tolerance, replication, recovery, scalability, etc. The paper includes 26 scoring tables and 17 diagrams to support the evaluation results of the three systems.
This technical report compares three popular NoSQL systems: Couchbase Server v5.5, DataStax Enterprise v6 (Cassandra), and MongoDB v3.6. The evaluation includes performance results on 3 different cluster configurations and 4 varying workloads. The paper also provides comparative tables and diagrams which illustrate throughput and latencies measured in greater detail.
This technical report explores the architectural differences between the two popular NoSQL databases: Cassandra 2.1.x (DataStax Enterprise) and Couchbase Server 3.x. In detail, it describes how these discrepancies impact availability, scalability, and performance. The paper provides a detailed overview of topology, data model, partitioning, replication, caching, etc.
This report provides an in-depth analysis of the leading NoSQL systems: DataStax Enterprise v5.0 (Cassandra), MongoDB v3.4, and Couchbase Server v5.0. Approaching the databases from the perspective of architecture, development, and administration, the research compares the solutions across 20+ criteria: ease of installation/configuration, scalability, availability, maintenance, data consistency, fault tolerance, replication, recovery, etc. The benchmark features a scoring framework/template for evaluating and comparing NoSQL data stores for your particular scenario—depending on the weight of each criterion.
This 11-page technical paper describes how to manage secure Cloud Foundry deployments—distributed across multiple data centers. Providing a reference architecture, the guide explores how to create repeatable and secure Cloud Foundry deployments with related services using Vault, Concourse CI, and BOSH. Highlighting each of the components and pipelines involved into the workflow, the document also overviews dependencies between the elements of the architecture.
This 15-page benchmark demonstrates performance results of the most popular Ruby frameworks, template engines, Rack application servers, and Ruby ORM tools. All the Ruby frameworks and tools were tested in the production mode and with disabled logging to enable equal conditions.
This 15-page technical study compares five popular Ruby frameworks in terms of performance. The research paper includes performance results under four workloads: views (Slim), MySQL (Sequel), views and MySQL, and no views/DB. For each framework, similar test applications were built to find out how much time it takes to process 1,000 requests.
This 20-page technical research embodies performance evaluation of distributed training with TensorFlow under two scenarios: a multi-node and multi-GPU infrastructure configuration. The benchmark was carried out using the Inception architecture as a neural network model and the Camelyon16 data as a training set. To test training scalability of distributed TensorFlow running on an Amazon EC2 cluster, the g2.2xlarge and g2.8xlarge instance types were employed.