Search Results for "real"

...Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science.

...Its asynchronous, non-blocking engine (backed by Netty) can drive very high concurrency from a single injector, reducing the need for large injector farms. Gatling supports HTTP out of the box as well as WebSocket, Server-Sent Events, and JMS, so you can exercise modern, real-time systems end to end. Rich HTML reports visualize percentiles, response time distributions, errors, and throughput, making bottlenecks and regressions easy to spot. With injection profiles (ramp, constant, spikes) and pass/fail gates, you can automate performance thresholds in CI and promote builds with confidence.

...Lift apps are resistant to common vulnerabilities including many of the OWASP Top 10. Lift apps are fast to build, concise and easy to maintain. Lift apps are high-performance and scale in the real world to handle insane traffic levels. Lift's Comet support is unparalled and Lift's ajax support is super-easy and very secure. Because Lift applications are written in Scala, an elegant JVM language, you can still use your favorite Java libraries and deploy to your favorite Servlet Container and app server. Use the code you've already written and deploy to the container you've already configured! ...

...It acts as a “backend-as-a-service / framework” that lets you define your data model via GraphQL SDL (Schema Definition Language), and in turn generates a GraphQL CRUD API, supports nested mutations, filtering, pagination, and real-time subscriptions. Graphcool separates the business logic from stateful storage components, allowing the stateful parts (database, subscription engine) to scale independently and giving flexibility in how you compose your system. Users could deploy Graphcool either locally (e.g. via Docker) or on a managed cloud offering. The framework also provided features like schema evolution, migrations, data loaders for performance, and built-in tooling to manage endpoints and deployments.

...Summingbird manages consistency and merging between the real-time and batch paths to avoid double-counting or data loss.

Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management.