Cloudera recently launched CDH 6.3 which includes two new key features from Apache Kudu: Fine-grained authorization with Apache Sentry integration Backup & restore of Kudu data Fine-grained authorization with Sentry integration Kudu is typically deployed as part of an Operations Data Warehouse (DWH) solution (also commonly referred to as an Active DWH and Live DWH). […]
Apache Impala supports fine-grained authorization via Apache Sentry on all of the tables it manages including Apache Kudu tables. Given Impala is a very common way to access the data stored in Kudu, this capability allows users deploying Impala and Kudu to fully secure the Kudu data in multi-tenant clusters even though Kudu does not […]
Although the Kudu server is written in C++ for performance and efficiency, developers can write client applications in C++, Java, or Python. To make it easier for Java developers to create reliable client applications, we’ve added new utilities in Kudu 1.9.0 that allow you to write tests using a Kudu cluster without needing to build […]
When picking a storage option for an application it is common to pick a single storage option which has the most applicable features to your use case. For mutability and real-time analytics workloads you may want to use Apache Kudu, but for massive scalability at a low cost you may want to use HDFS. For that […]
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 […]
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, […]
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 […]
As a warm-up to Spark Summit West in San Francisco (June 6-8), we’ve added a new project to Cloudera Labs that makes building Spark Streaming pipelines considerably easier. Spark Streaming is the go-to engine for stream processing in the Cloudera stack. It allows developers to build stream data pipelines that harness the rich Spark API for […]
Engineers from across the Apache Hadoop community are collaborating to establish Arrow as a de-facto standard for columnar in-memory processing and interchange. Here’s how it works. Apache Arrow is an in-memory data structure specification for use by engineers building data systems. It has several key benefits: A columnar memory-layout permitting O(1) random access. The layout is highly cache-efficient in […]
Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running […]
