As a member of Cloudera’s Partner Engineering team, I evaluate hardware and cloud computing platforms offered by commercial partners who want to certify their products for use with Cloudera software. One of my primary goals is to make sure that these platforms provide a stable and well-performing base upon which our products will run, a state of operation that a wide variety of customers performing an even wider variety of tasks can appreciate.
For the first time, this new study by Intel software engineers analyzes the performance impact of using Apache HBase on various modern storage technologies.
As more “fast” storage technologies (such as SSD and NVMe SSD) emerge, organizations with big data use cases want to make better use of them to achieve better throughput and latency. But to this point, there have been no detailed analyses published about the true significance of that performance boost, nor about how to best mix fast and “slow”
Learn how to set up a Hadoop cluster in a way that maximizes successful production-ization of Hadoop and minimizes ongoing, long-term adjustments.
Previously, we published some recommendations on selecting new hardware for Apache Hadoop deployments. That post covered some important ideas regarding cluster planning and deployment such as workload profiling and general recommendations for CPU, disk, and memory allocations. In this post, we’ll provide some best practices and guidelines for the next part of the implementation process: configuring the machines once they arrive.
Learn about BigBench, the new industrywide effort to create a sorely needed Big Data benchmark.
Benchmarking Big Data systems is an open problem. To address this concern, numerous hardware and software vendors are working together to create a comprehensive end-to-end big data benchmark suite called BigBench. BigBench builds upon and borrows elements from existing benchmarking efforts in the Big Data space (such as YCSB, TPC-xHS,
Thanks to Alexander Rubin of Percona for allowing us to re-publish the post below!
Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from MySQL to Hadoop, load the data to Cloudera Impala (columnar format),