Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working as a Principal Big Data Consultant at Allstate Insurance, for the guest post below about his experiences with Impala.
It started with a simple request from one of the managers in my group at Allstate to put together a demo of Tableau connecting to Cloudera Impala. I had previously worked on Impala with a large dataset about a year ago while it was still in beta,
More than 300 bug fixes and stable features in Apache Hive 0.13 have already been backported into CDH 5.0.0.
Last week, the Hive community voted to release Hive 0.13. We’re excited about the continued efforts and progress in the project and the latest release — congratulations to all contributors involved!
Furthermore, thanks to continual feedback from customers about their needs, we were able to test and make more than 300 Hive 0.13 fixes and stable features generally available via CDH 5.0.0,
The following post, by Sarah Cannon of Digital Reasoning, was originally published in that company’s blog. Digital Reasoning has graciously permitted us to re-publish here for your convenience.
At the beginning of each release cycle, engineers at Digital Reasoning are given time to explore the latest in Big Data technologies, examining how the frequently changing landscape might be best adapted to serve our mission. As we sat down in the early stages of planning for Synthesys 3.8 one of the biggest issues we faced involved reconciling the tradeoff between flexibility and performance.
The CDH software stack lets you use your tool of choice with the Parquet file format – – offering the benefits of columnar storage at each phase of data processing.
An open source project co-founded by Twitter and Cloudera, Parquet was designed from the ground up as a state-of-the-art, general-purpose, columnar file format for the Apache Hadoop ecosystem. In particular, Parquet has several features that make it highly suited to use with Cloudera Impala for data warehouse-style operations:
- Columnar storage layout: A query can examine and perform calculations on all values for a column while reading only a small fraction of the data from a data file or table.
This quick demo illustrates how easy it is to implement role-based access and control in Impala using Sentry.
Apache Sentry (incubating) is the Apache Hadoop ecosystem tool for role-based access control (RBAC). In this how-to, I will demonstrate how to implement Sentry for RBAC in Impala. I feel this introduction is best motivated by a use case.
Data warehouse optimization is one of the most common Hadoop use cases.