Cloudera Data Warehouse offers a powerful combination of flexibility and cost-savings. Using Cloudera Data Warehouse, you can transform and optimize your current traditional data warehouse by moving select workloads to your CDH cluster. This article shows you how to transform your current setup into a modern data warehouse by moving some initial data over to Impala on your CDH cluster.
Prerequisites
To use the following data import scenario,
Key Takeaways
We have significantly improved Impala in CDH 5.15.0 to address some of the scalability bottlenecks in query execution. 64 concurrent streams of TPC-DS queries at 10TB scale in a 135-node cluster now run at 6x query throughput compared to previous releases. In addition to running faster, the query success rate also improved from 73% to 100%. Overall, Impala in CDH 5.15.0 provides massive improvements in throughput and reliability while reducing the resource usage significantly.
Background
Apache Hive is a widely adopted data warehouse engine that runs on Apache Hadoop. Features that improve Hive performance can significantly improve the overall utilization of resources on the cluster. Hive processes data using a chain of operators within the Hive execution engine. These operators are scheduled in the various tasks (for example, MapTask, ReduceTask, or SparkTask) of the query execution plan. Traditionally, these operators are designed to process one row at a time.
Cloudera Altus Director helps you deploy, scale, and manage Cloudera clusters on AWS, Microsoft Azure, or Google Cloud Platform. Altus Director both enables and enforces the best practices of big data deployments and cloud infrastructure. Altus Director’s enterprise-grade features deliver a mechanism for establishing production-ready clusters in the cloud for big data workloads and applications in a simple, reliable, automated fashion. In this post, you will learn about new functionality and changes in release 6.1.
We are pleased to announce the general availability of Cloudera Enterprise 6.1.0, the modern platform for machine learning and analytics optimized for the cloud. This release delivers several new capabilities, improved usability, and better performance.
As usual, the release includes a number of quality enhancements, bug fixes, and other improvements across the stack. Here is a partial list of what’s included (see the Release Notes for a full list):
Data Engineering
Cloudera Enterprise 6.1 now supports Spark Structured Streaming and enables micro-batch processing at ~100ms increments enabling ingest to query latencies in the Cloudera platform measured in seconds.