Palo Alto, CA, USA
SMM 1.2 Released with Powerful New Alerting and Topic Lifecycle Management Features with Schema Registry Integration
Feb 15, 2019 | By George Vetticaden
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 three most requested features in SMM.
Feb 13, 2019 | By Zuling Kang
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.
Feb 11, 2019 | By Timothy Spann
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.
Feb 5, 2019 | By Alan Choi
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.
Jan 30, 2019 | By Michael Ho
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.
Jun 28, 2018 | By Cloudera
Enterprises require fast, cost-efficient solutions to the familiar challenges of engaging customers, reducing risk, and improving operational excellence to stay competitive. The cloud is playing a key role in accelerating time to benefit from new insights. Managed cloud services that automate provisioning, operation, and patching will be critical for enterprises to leverage the full promise of the cloud when it comes to time to value and agility.
Jun 26, 2018 | By Cloudera
The adoption of cloud computing in the financial services sector has grown substantially in the past three years on a global basis. Diversification of risk is always a key concern for financial institutions and the seeming safety of having a single cloud provider is not being properly measured from a systemic risk and operational risk perspective.
Jun 12, 2018 | By Cloudera
This white paper provides a reference architecture for running Enterprise Data Hub on Oracle Cloud Infrastructure. Topics include installation automation, automated configuration and tuning, and best practices for deployment and topology to support security and high availability.
May 17, 2018 | By Cloudera
A cloud-based analytics platform needs to be easy, unified, and enterprise-grade to meet the demands of your business. This white paper covers how Cloudera's machine learning and analytics platform complements popular cloud services like Amazon Web Services (AWS) and Microsoft Azure, and enables customers to organize, process, analyze, and store data at large scale...anywhere.
Feb 13, 2019 | By Cloudera
Sushant Rao, Director of Product Marketing for Cloud, shares how Cloudera’s data analytics and Spark platform empowers teams to develop and deploy data warehouse and machine learning in the cloud.
Dec 20, 2018 | By Cloudera
In this video, you'll see how a company is able to migrate their data to the cloud, onboard and transform new data (data engineering), and then analyze it in conjunction with existing data (data warehouse) to drive business value.