Hiro Game Development
Hiro is a client-server library built on the Nakama game server to rapidly and flexibly add economy, social, and LiveOps features to your game. It is available as a C# library for Unity Engine, a C++ library for Unreal Engine, and a server package that integrates with the game server. Hiro offers a comprehensive suite of battle-tested metagame features that are quick to implement, allowing developers to focus more on the core gameplay experience. Add base and loot table rewards for players to collect; integrate soft or real currency purchases; define stackable and consumable gameplay items; initialize and manage multiple currencies within the game economy. Adapt store bundles and offer walls based on player behavior; personalize various aspects of the player experience through experiments; schedule live events for players to participate in and earn exclusive rewards. Start and schedule timed or scored events with exclusive rewards; enable players to request and share inventory.
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E-MapReduce
EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface.
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Apache Druid
Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.
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Stackable
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