What is Apache Iceberg™? Iceberg is a high-performance format for huge analytic tables. Iceberg brings the reliability and simplicity of SQL tables to big data, while making it possible for engines like Spark, Trino, Flink, Presto, Hive and Impala to safely work with the same tables, at the same time. Expressive SQL Iceberg supports flexible SQL commands to merge new data, update existing rows, an
Kay Ousterhout, University of California, Berkeley; Ryan Rasti, University of California, Berkeley, International Computer Science Institute, and VMware; Sylvia Ratnasamy, University of California, Berkeley; Scott Shenker, University of California, Berkeley, and International Computer Science Institute; Byung-Gon Chun, Seoul National University There has been much research devoted to improving the
Kafka serialisation schemes — playing with AVRO, Protobuf, JSON Schema in Confluent Streaming Platform. The code for these examples available at https://github.com/saubury/kafka-serialization Apache Avro was has been the default Kafka serialisation mechanism for a long time. Confluent just updated their Kafka streaming platform with additional support for serialising data with Protocol buffers (or
AIMichelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model DevelopmentOctober 23, 2018 / Global As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML model
cloudpickle makes it possible to serialize Python constructs not supported by the default pickle module from the Python standard library. cloudpickle is especially useful for cluster computing where Python code is shipped over the network to execute on remote hosts, possibly close to the data. Among other things, cloudpickle supports pickling for lambda functions along with functions and classes d
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皆さんはビッグデータを扱うときどのような形式で保存していますか？ここでいうビッグデータとは数GB～数十GB（笑）のJSONです。MongoDBのようなNoSQLなデータベース使う？素晴らしいと思います。PostgreSQLでJSONを使う？とても良いと思います。 ここでは、データベースという枠組みから外れて、「ファイルシステム」を中心に手軽に**お安く（ここポイント）**ビッグデータを扱うことを考えます。なので、この方法は最速ではありませんし、個人がちょっと遊んでみようというときに気楽にできる”チープ”な物です1。企業でやるならちゃんとしたデータベースを使うべきです。その前提で読んでみてください（ちょっと長いです）。 ファイルシステムは、テキストファイルやZipアーカイブといったただのファイルです。ただのファイルなので、データベースが得意なインデックスも効きませんし、検索や結合も弱いですし
RFC 8949 Concise Binary Object Representation “The Concise Binary Object Representation (CBOR) is a data format whose design goals include the possibility of extremely small code size, fairly small message size, and extensibility without the need for version negotiation.” JSON data model CBOR is based on the wildly successful JSON data model: numbers, strings, arrays, maps (called objects in JSON)
For compression, we put three lossless and widely accepted libraries to the test: Snappy zlib Bzip2 (BZ2) Snappy aims to provide high speeds and reasonable compression. BZ2 trades speed for better compression, and zlib falls somewhere between them. Testing Our goal was to find the combination of encoding protocol and compression algorithm with the most compact result at the highest speed. We teste
This document summarizes a benchmark study of file formats for Hadoop, including Avro, JSON, ORC, and Parquet. It found that ORC with zlib compression generally performed best for full table scans. However, Avro with Snappy compression worked better for datasets with many shared strings. The document recommends experimenting with the benchmarks, as performance can vary based on data characteristic
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert Test Platform OS:Mac OS X JVM:Oracle Corporation 11.0.19 CPU:2.6 GHz 6-Core Intel Core i7 os-arch:Darwin Kernel Version 21.6.0 Cores (incl HT):12 Disclaimer Th
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Introducing SLOG: Cheating the low-latency vs. strict serializability tradeoff
