后续步骤What’s next

如果已完成本指南的每个部分,则可在 Azure Databricks 上构建自己的 Apache Spark 应用程序。If you’ve worked through each section of this guide, you are well on your way to building your own Apache Spark applications on Azure Databricks.

在后续步骤中,第一步应该是 Spark:明确指南Your first next step should be Spark: The Definitive Guide. 这份全面的指南由开源群集计算框架的创建者编写,教你如何使用、部署和维护 Apache Spark。Written by the creator of the open-source cluster-computing framework, this comprehensive guide teaches you how to use, deploy, and maintain Apache Spark. 里面重点介绍 Spark 2.0 中的改进和新增功能,作者 Bill Chambers 和 Matei Zaharia 将 Spark 文章分解为不同的部分,每个部分都有各自的目标。With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark articles into distinct sections, each with unique goals. 本指南中的笔记本可在 GitHub 上获取,数据集可在 DBFS 文件夹 /databricks-datasets/definitive-guide 中获取。The notebooks from the guide are available on GitHub and the datasets are available in the DBFS folder /databricks-datasets/definitive-guide.

Azure Databricks 文档和 Databricks 网站中还有许多其他资源;建议你闲暇时进行查看:There are many more resources within the Azure Databricks documentation and the Databricks website; we recommend that you check these out at your leisure:

培训Training

Databricks 提供了许多培训选项,包括自定进度式和讲师引导式。Databricks offers many training options, both self paced and instructor led.

博客Blog

Apache Spark 在不断发展。Apache Spark is constantly evolving. 有关最新的开发,请参阅 Databricks 工程博客For the newest developments, see the Databricks Engineering blog.

社交媒体Social Media

若要及时了解创建 Apache Spark 的团队的最新改进和提示,请在 TwitterFacebook 上关注 Databricks。To stay up to date on the latest improvements and tips from the team that created Apache Spark, follow Databricks on Twitter and Facebook.

论坛Forums

Databricks 论坛是一种很好的资源,可在这里就 Apache Spark 和 Azure Databricks 产品提问。The Databricks forum is a great resource to ask questions about Apache Spark and the Azure Databricks product. 任何人都可注册和参与讨论。Anyone can sign up and participate in discussions there.

新闻稿Newsletter

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文档Documentation

有关不同 Apache Spark API 的深入讲解文档,请参阅:For in-depth documentation on various Apache Spark APIs, see: