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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-kylin-implementing-olap-on-the-hadoop-platform/
课程评论：没有评论
**Apache Kylin：在 Hadoop 平台上实现 OLAP** **课程概述：** 本课程是一门综合性的课程，旨在教授如何使用 Apache Kylin 构建和查询 Hadoop 平台上的大数据 OLAP 多维数据集。Apache Kylin 是 Apache 的顶级项目，它将 OLAP（在线分析处理）带入了大数据领域。这意味着您可以对海量数据进行复杂的、多层级的聚合查询，并在秒级甚至毫秒级时间内获得响应。 在传统商业智能中，OLAP 是一个常见概念，但在 Hadoop 平台（许多组织的“数据湖”）上实现 OLAP 却并非易事。数据湖中往往包含数亿甚至数十亿条记录，组织希望从中切片和切块以获取洞察。然而，像 Apache Hive 或 Apache Drill 这样的 Hadoop SQL 技术查询延迟较高，导致许多数据架构师选择将数据迁移回能够实时响应查询的传统系统。Apache Kylin 解决了这一难题。 通过 Apache Kylin，具备相关技能的任何人都可以使用 Web UI 构建 OLAP、ROLAP 或 MOLAP 结构，进行部署，并通过秒级响应时间进行查询。此外，您还可以将您的应用程序或首选可视化工具连接到 Kylin，以集成数据用于系统处理或可视化。 **课程内容将涵盖：** * Kylin 是什么 * Kylin 的工作原理 * 如何以批量和流式模型构建 OLAP 多维数据集 * 如何部署多维数据集 * 如何查询多维数据集 * 如何连接外部工具和应用程序到 Kylin * 以及更多内容 **目标受众：** * 大数据工程师/开发人员 * 数据架构师 * 数据分析师 * 任何希望能够对大型数据集进行简单到复杂聚合查询并期望获得低延迟响应时间的人 **课程要求：** * 需要访问大数据沙箱环境，例如 Cloudera Quickstart VM、Hortonworks HDP Sandbox 或基于云的 Hadoop 环境，至少拥有 10GB RAM。 * 应熟悉 SQL，并能够使用基于 ODBC 或 JDBC 的工具。 * 熟悉 Linux 会有所帮助。 **为获得最佳学习效果，建议了解：** 由于 Kylin 使用其他 Hadoop 项目来实现其设计，因此对 Apache Hive、Apache Kafka、Apache HBase、MapReduce 等项目有一定了解对本课程非常有益。然而，即使不了解这些技术，您也可以使用 Kylin。此外，值得注意的是，查询 Kylin 或在运行报表或数据可视化中使用数据集成，无需预先了解任何大数据技术。
A Comprehensive Course for Learning How to Build and Query Big Data OLAP Cubes Using Apache Kylin. Apache Kylin is an Apache top-level project that bring OLAP to Big data. This simply means that we can now write complex aggregation queries with different levels of aggregation and expect to get a second or micro-seconds response to our query. Online analytical processing (OLAP) has been a common word in traditional business intelligence for years but has not been easy with hadoop platform that has become a data lake solution for many. These data lake often have hundreds of millions and even billions of records that organizations want to slice and dice for insights. However, the high latency of query execution in SQL on Hadoop technologies like Apache Hive or Apache Drill often meant that data architect opted to transfer their data back to traditional systems that allow for real time response to query. Kylin solves all of this. With Apache Kylin, anyone with the skills can now build OLAP, ROLAP or MOLAP structures using a web UI, deploy it and expect to query these structure with second of response time in mind. Also, one can connect their applications or favorite visualization tools to Kylin to integrate data either for system processing or for visualization. In this course, we are going to review What Kylin isHow it worksHow to build OLAP cubes in batch and streaming modelHow to deploy the cubesHow to query cubesHow to connect external tools and applications to Kylin.. and many moreWhat is the target audience? Big Data Engineers/DevelopersData ArchitectsData Analysts.Anyone who wishes to be able to write simple to complex aggregation queries of large dataset and wants a low latency response time.What are the requirements? You need access to a Big Data Sandbox like Cloudera quickstart VM, Hortonworks HDP sandbox or a cloud-based Hadoop environment with a least 10GB of Ram.You should have some familiarity SQL and be able to use ODBC or JDBC based tools.Some familiarity with Linux will be helpfulWhat do I need to know to get the best out of this course? Because Kylin uses other hadoop projects to achieve its design a fair understanding of projects like Apache Hive, Apache Kafka, Apache HBase, MapReduce is great for this course. However, one can still use Kylin without any knowledge of these technologies. It is also worth knowing that no prior knowledge of any big data technology is required to query Kylin or use data integration in running report or data visualizations.
