Difference between revisions of "VT2020-OpenAI GPT-3-Fiche"
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| + | = Appache Pinot = |
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| − | [[Sumary]] |
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| + | |||
| + | == Abstract == |
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| + | «''Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, Azure ADLS, Google Cloud Storage) as well as stream data sources (such as Apache Kafka). |
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| + | Pinot was built by engineers at LinkedIn and Uber and is designed to scale up and out with no upper bound. Performance always remains constant based on the size of your cluster and an expected query per second (QPS) threshold.''» - Documentation officielle de Appache Pinot |
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| + | |||
| + | |||
| + | == Origine == |
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| + | |||
| + | |||
| + | == Présentation des Fonctionnalités == |
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| + | |||
| + | == Avantages == |
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| + | |||
| + | |||
| + | == Limites == |
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| + | = Démonstration = |
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| + | = Sources = |
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| + | https://docs.pinot.apache.org/ |
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| + | https://www.youtube.com/watch?v=cNnwMF0pOJ8 |
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| + | https://www.youtube.com/watch?v=mRkWT_EU99M |
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| + | https://medium.com/@gowthamy/big-data-battle-batch-processing-vs-stream-processing-5d94600d8103 |
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| + | https://github.com/zzhang5/zooinspector |
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| + | https://github.com/npawar/pinot-tutorial |
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| + | https://github.com/apache/incubator-pinot |
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| + | https://pinot.apache.org/ |
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| + | https://docs.pinot.apache.org/basics/getting-started |
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| + | = Veille Technologique 2020 = |
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| + | * Année : [[VT2020]] |
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| + | * Sujet : Appache Pinot |
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| + | * Slides : [[Media:VT2020-AppachePinot-Presentation.pdf|Slides]] |
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| + | * Auteur : RUZAFA Rémy |
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Revision as of 00:29, 2 December 2020
Appache Pinot
Abstract
«Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency. It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, Azure ADLS, Google Cloud Storage) as well as stream data sources (such as Apache Kafka).
Pinot was built by engineers at LinkedIn and Uber and is designed to scale up and out with no upper bound. Performance always remains constant based on the size of your cluster and an expected query per second (QPS) threshold.» - Documentation officielle de Appache Pinot
Origine
Présentation des Fonctionnalités
Avantages
Limites
Démonstration
Sources
https://docs.pinot.apache.org/ https://www.youtube.com/watch?v=cNnwMF0pOJ8 https://www.youtube.com/watch?v=mRkWT_EU99M https://medium.com/@gowthamy/big-data-battle-batch-processing-vs-stream-processing-5d94600d8103 https://github.com/zzhang5/zooinspector https://github.com/npawar/pinot-tutorial https://github.com/apache/incubator-pinot https://pinot.apache.org/ https://docs.pinot.apache.org/basics/getting-started