Category: Cloudera

Feed: Cloudera Engineering Blog. Author: Grant Henke. 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 Kudu yourself, without any knowledge of C++, and without any complicated coordination around starting and stopping Kudu clusters for each test. This post describes how the new testing utilities work and how you can use them in your ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Tom Wheeler. 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 reason, there is a need for a solution that allows you to leverage the best features of multiple storage options. This post describes the sliding window pattern using Apache Impala with data stored in Apache Kudu ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. Spark ML is one of the dominant frameworks for many major machine learning algorithms, such as the Alternating Least Squares (ALS) algorithm for recommendation systems, the Principal Component Analysis algorithm, and the Random Forest algorithm. However, the complexity of configuring it optimally  means that frequently, Spark ML is underutilized.. Using native math libraries for Spark ML can help unlock the full potential of Spark ML. This article discusses how to accelerate model training speed by using native libraries for Spark ML. It also discusses why Spark ML benefits from native libraries, how to enable ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. [Editor’s note: This article was originally published on the Hortonworks Community Connection, but reproduced here because CDSW is now available on both Cloudera and Hortonworks platforms.] Using Deployed Models as a Function as a Service    Using Cloudera Data Science Workbench with Apache NiFi, we can easily call functions within our deployed models from Apache NiFi as part of flows. I am working against CDSW on HDP (https://www.cloudera.com/documentation/data-science-workbench/latest/topics/cdsw_hdp.html),  but it will work for all CDSW regardless of install type. In my simple example, I built a Python model that uses TextBlob to run sentiment analysis against ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. 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, you need the following: A moderate-sized CDH cluster that is managed by Cloudera Manager, which configures everything properly so you don’t need to worry about ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. 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. It can now reliably handle concurrent complex queries on large data sets that were not possible before ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. In the last few years, IT security threats to enterprise systems have increased, which has necessitated installing log ingestion and analysis solutions in any enterprise network. This blog post illustrates how Cloudera built its own scalable solution for log ingestion and analytics using Apache Spot and Cloudera Altus. By leveraging transient workloads in the cloud, Cloudera reduced the solution’s operational costs by 50% when compared to traditional, persistent cluster approaches. At Cloudera, the Infosec team needed to build a centralized log ingestion and analytical solution to monitor all key systems in the company ... Read More

Feed: Cloudera Engineering Blog. Author: Tom Wheeler. [Editor’s note: Now that the recent merger is complete, the Cloudera Engineering blog will expand to cover products, such as this, originally developed for the Hortonworks platform. Please stay tuned for future product announcements regarding availability of these products on the Cloudera platform.] Since the release of Streams Messaging Manager (SMM) at the end of last summer, our customers have started to cure the Kafka Blindness within their organizations by using SMM to monitor their Kafka clusters and streaming microservices applications. With the release of SMM 1.2, we have delivered on the top ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Tom Wheeler. 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. Operators that process one row at a time are not very efficient because many virtual function calls ... Read More

Feed: Cloud – Cloudera Engineering Blog. Author: Tom Wheeler. 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. For those new to Altus Director, let’s revisit what it does. On-demand creation and termination of clusters: Altus ... Read More