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Apache Storm
Apache Storm

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Hazelcast
Hazelcast

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What is Apache Storm?

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

What is Hazelcast?

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.
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      What are some alternatives to Apache Storm and Hazelcast?
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      Kafka
      Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
      Amazon Kinesis
      Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
      Apache Flume
      It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
      Apache Flink
      Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
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      Decisions about Apache Storm and Hazelcast
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      How developers use Apache Storm and Hazelcast
      Avatar of Pinterest
      Pinterest uses Apache StormApache Storm

      In addition to batch processing, we also wanted to achieve real-time data processing. For example, to improve the success rate of experiments, we needed to figure out experiment group allocations in real-time once the experiment configuration was pushed out to production. We used Storm to tail Kafka and compute aggregated metrics in real-time to provide crucial stats.

      Avatar of Vital Labs, Inc.
      Vital Labs, Inc. uses HazelcastHazelcast

      HazelCast is the foundation for the distributed system that hosts our APIs and intelligent workflows. We wrap the core HazelCast functions in Clojure protocols to implement micro-services on top of a coherent, single-process instance per virtual node.

      Avatar of Yelp
      Yelp uses Apache StormApache Storm

      Real-time analytics are much better than periodically run batch jobs, so recently we open sourced Pyleus which allows anyone to write Storm topologies using Python.

      Avatar of Blue Kangaroo
      Blue Kangaroo uses Apache StormApache Storm

      Real-time log processing for user profiling

      Avatar of JimmyCode
      JimmyCode uses Apache StormApache Storm

      Batch processing and as recommendation tool.

      Avatar of brenoinojosa
      brenoinojosa uses Apache StormApache Storm

      Tasks in parallel are run by Storm.

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