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Bigtable adalah layanan database Big Data NoSQL dari Google. Cloud Bigtable merupakan database yang sama yang mendukung berbagai layanan inti milik Google, termasuk Penelusuran, Analytics, Maps, dan Gmail. Bigtable dirancang untuk menangani beban kerja besar dengan latensi rendah yang konsisten dan throughput yang tinggi. Oleh karena itu, Bigtable adalah pilihan yang tepat untuk aplikasi operasional dan analitik, termasuk IoT, analisis pengguna, dan analisis data keuangan.
Bigtable adalah opsi yang sangat baik untuk penggunaan Apache Spark atau Hadoop yang memerlukan Apache HBase.
Bigtable mendukung API Apache HBase 1.0+ dan 2.0+, serta menawarkan klien Bigtable HBase di Maven untuk menggunakan Bigtable dengan Dataproc.
Menggunakan konektor Bigtable Spark
Konektor Bigtable Spark memungkinkan Anda membaca dan menulis data dari dan ke Bigtable. Anda dapat membaca data dari dalam aplikasi Spark menggunakan Spark SQL dan DataFrame. Untuk tugas hanya baca, Anda dapat menggunakan
komputasi serverless Data Boost,
Data Boost dirancang untuk persyaratan performa tugas dan kueri dengan throughput tinggi
dan memungkinkan Anda menghindari dampak pada traffic cluster penayangan aplikasi
saat tugas dan kueri tersebut dijalankan.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[[["\u003cp\u003eBigtable is Google's NoSQL Big Data database service, powering core Google services like Search, Analytics, Maps, and Gmail.\u003c/p\u003e\n"],["\u003cp\u003eBigtable is designed for massive workloads with consistent low latency and high throughput, making it suitable for operational and analytical applications.\u003c/p\u003e\n"],["\u003cp\u003eBigtable supports Apache HBase 1.0+ and 2.0+ APIs and offers a Bigtable HBase client in Maven for use with Dataproc.\u003c/p\u003e\n"],["\u003cp\u003eThe Bigtable Spark connector allows reading and writing data from and to Bigtable within Spark applications, using Spark SQL and DataFrames.\u003c/p\u003e\n"],["\u003cp\u003eData Boost serverless compute is available for high-throughput read-only jobs and queries, preventing impact on application-serving cluster traffic.\u003c/p\u003e\n"]]],[],null,["[Bigtable](/bigtable) is Google's NoSQL Big Data database\nservice. It's the same database that powers many core Google services, including\nSearch, Analytics, Maps, and Gmail. Bigtable is designed to\nhandle massive workloads at consistent low latency and high throughput, so it's\na great choice for both operational and analytical applications, including IoT,\nuser analytics, and financial data analysis.\n\nBigtable is an excellent option for any Apache Spark or Hadoop\nuses that require [Apache HBase](https://hbase.apache.org/).\nBigtable supports the [Apache HBase](https://hbase.apache.org/)\n1.0+ and 2.0+ APIs, and offers a [Bigtable HBase client in\nMaven](/bigtable/docs/using-maven) to use Bigtable with\nDataproc.\n\nUse the Bigtable Spark connector\n\nThe Bigtable Spark connector lets you read and write data from\nand to Bigtable. You can read data from within your Spark\napplication using Spark SQL and DataFrames. For read-only jobs, you can use\n[Data Boost serverless compute](/bigtable/docs/data-boost-overview),\nData Boost is designed for the performance requirements of high-throughput jobs\nand queries, and it lets you avoid impacting your application-serving cluster\ntraffic when those jobs and queries are run.\n\nFor more information, see [Use the Bigtable Spark\nconnector](/bigtable/docs/use-bigtable-spark-connector).\n\nWhat's next\n\n- For more information about using Bigtable, see the [Bigtable](/bigtable/docs/overview) documentation."]]
