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Apache Storm
Apache Storm

129
115
+ 1
18
Celery
Celery

890
535
+ 1
239
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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 Celery?

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
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    What tools integrate with Apache Storm?
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    What are some alternatives to Apache Storm and Celery?
    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 Celery
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    How developers use Apache Storm and Celery
    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 Kalibrr
    Kalibrr uses CeleryCelery

    All of our background jobs (e.g., image resizing, file uploading, email and SMS sending) are done through Celery (using Redis as its broker). Celery's scheduling and retrying features are especially useful for error-prone tasks, such as email and SMS sending.

    Avatar of Cloudify
    Cloudify uses CeleryCelery

    For orchestrating the creation of the correct number of instances, managing errors and retries, and finally managing the deallocation of resources we use RabbitMQ in conjunction with the Celery Project framework, along with a self-developed workflow engine.

    Avatar of MOKA Analytics
    MOKA Analytics uses CeleryCelery

    We maintain a fork of Celery 3 that adds HTTPS support for Redis brokers. The Winning Model currently uses Celery 3 because Celery 4 dropped support for Windows.

    We plan on migrating to Celery 4 once Azure ASE supports Linux apps

    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 Yaakov Gesher
    Yaakov Gesher uses CeleryCelery

    We used celery, in combination with RabbitMQ and celery-beat, to run periodic tasks, as well as some user-initiated long-running tasks on the server.

    Avatar of Dieter Adriaenssens
    Dieter Adriaenssens uses CeleryCelery

    Using Celery, the web service creates tasks that are executed by a background worker. Celery uses a RabbitMQ instance as a task queue.

    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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