Kudu offers the powerful combination of fast inserts and updates with efficient columnar scans to enable real-time analytics use cases on a single storage layer. As an alternative, I could have used Spark SQL exclusively, but I also wanted to compare building a regression model using the MADlib libraries in Impala to using Spark MLlib. It is compatible with most of the data processing frameworks in the Hadoop environment. Kudu Source & Sink Plugin: For ingesting and writing data to and from Apache Kudu tables. Every workload is unique, and there is no single schema design that is best for every table. It is designed to complete the Hadoop ecosystem storage layer, enabling fast analytics on fast data. Tables are self-describing. Kudu offers the powerful combination of fast inserts and updates with efficient columnar scans to enable real-time analytics use cases on a single storage layer. Schema design is critical for achieving the best performance and operational stability from Kudu. I used it as a query engine to directly query the data that I had loaded into Kudu to help understand the patterns I could use to build a model. Kudu tables have a structured data model similar to tables in a traditional RDBMS. Data Collector Data Type Kudu Data Type; Boolean: Bool: Byte: Int8: Byte Array: Binary : Decimal: Decimal. Decomposition Storage Model (Columnar) Because Kudu is designed primarily for OLAP queries a Decomposition Storage Model is used. Source table schema might change, or a data discrepancy might be discovered, or a source system would be switched to use a different time zone for date/time fields. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. kudu source sink cdap cdap-plugin apache-kudu cask-marketplace kudu-table kudu-source Updated Oct 8, 2019 Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. Kudu is specially designed for rapidly changing data like time-series, predictive modeling, and reporting applications where end users require immediate access to newly-arrival data. A common challenge in data analysis is one where new data arrives rapidly and constantly, and the same data needs to be available in near real time for reads, scans, and updates. Available in Kudu version 1.7 and later. Kudu's columnar data storage model allows it to avoid unnecessarily reading entire rows for analytical queries. Kudu provides a relational-like table construct for storing data and allows users to insert, update, and delete data, in much the same way that you can with a relational database. Sometimes, there is a need to re-process production data (a process known as a historical data reload, or a backfill). This simple data model makes it easy to port legacy applications or build new ones. This action yields a .zip file that contains the log data, current to their generation time. 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