Category Archives: Kudu

Next Generation Data Warehousing at Santander UK

Categories: CDH HBase HDFS Kafka Kudu Use Case

Timely data is crucial to businesses in the Big Data age: This blog post outlines how Santander UK utilises the latest Cloudera technologies and superior software development capability to create the next generation of data warehousing and streaming analytics to support intelligence that can improve relationships with customers and follow the mantra of ‘we want to help people grow and prosper.

Santander UK’s big data journey started around four years ago.

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implyr: R Interface for Apache Impala

Categories: CDH Data Science HBase HDFS Impala Kudu Tools

New R package implyr enables R users to query Impala using dplyr.

Apache Impala (incubating) enables low-latency interactive SQL queries on data stored in HDFS, Amazon S3, Apache Kudu, and Apache HBase. With the availability of the R package implyr on CRAN and GitHub, it’s now possible to query Impala from R using the popular package dplyr.

dplyr provides a grammar of data manipulation,

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Cloudera Enterprise 5.12 is Now Available

Categories: Altus CDH Cloud Cloudera Manager Cloudera Navigator Data Science Hue Impala Kafka Kudu

Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.

As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack.

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Bi-temporal data modeling with Envelope

Categories: CDH Data Ingestion Impala Kudu Spark

One of the most fundamental aspects a data model can convey is how something changes over time. This makes sense when considering that we build data models to capture what is happening in the real world, and the real world is constantly changing. The challenge is that it’s not just that new things are occurring, it’s that existing things are changing too, and if in our data models we overwrite the old state of an entity with the new state then we have lost information about the change.

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Apache Kudu Read & Write Paths

Categories: CDH Kudu

Analytical and operational access patterns are very different and until now the Hadoop ecosystem has not had a single storage engine that could support both. As a result, engineers have been forced to implement complex architectures that stitch multiple systems together in order to provide these capabilities. On one hand immutable data on HDFS offers superior analytic performance, while mutable data in Apache HBase is best for operational workloads. Apache Kudu bridges this gap.  

Kudu’s architecture is shaped towards the ability to provide very good analytical performance,

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