Deploy Cloudera EDH Clusters Like a Boss Revamped – Part 3: Cloud Considerations

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Categories: CDH

The previous two sections have concentrated on infrastructure considerations and services and role layouts for categories of workloads such as Analytic DB and Operational DB. Many of the concepts described therein apply predominantly to on-premise clusters while others apply to clusters deployed on-premise or in the cloud. This section will concentrate predominantly on those considerations that apply to cloud deployments only.

At the time of this writing, Cloudera supports 3 Infrastructure as a Service (IaaS) platforms: Amazon Elastic Compute Cloud (AWS),

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Evaluating Partner Platforms

 (@technmsg)
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Categories: CDH Hardware How-to Performance

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.

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New in Cloudera Enterprise 6.0: Analytic Search

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Categories: CDH Search

It has been a long and patient wait for Apache Hadoop 3.0 to mature. A major new version of the storage layer obviously impacts all our integrated components, including Apache Solr and all our integrations with the rest of the platform, commonly referred to as Cloudera Search. Since our customers’ Search deployments are so often mission critical, we’ve made sure to take time to do extensive integration testing and focus on the upgrade experience.

Now the moment has finally come to announce Solr 7.0 in Cloudera Search and available as of our new major release,

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Scalability of Kafka Messaging using Consumer Groups

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Categories: Data Ingestion Flume Kafka Use Case

Traditional messaging models fall into two categories: Shared Message Queues and Publish-Subscribe models. Both models have their own pros and cons. Neither could successfully handle big data ingestion at scale due to limitations in their design. Apache Kafka implements a publish-subscribe messaging model which provides fault tolerance, scalability to handle large volumes of streaming data for real-time analytics. It was developed at LinkedIn in 2010 to meet its growing data pipeline needs. Apache Kafka bridges the gaps that traditional messaging models failed to achieve.

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Backup and Disaster Recovery for Cloudera Search

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Categories: CDH Search

One of the worst things that can happen in mission-critical production environments is loss of data and another is downtime. For a search service that provides end users with easy access to data using natural language, downtime would mean complete halt for those parts of your organization. Even worse if the search service is fueling your online business, it interrupts your customer access and end user experience.

That is why we designed multiple options of backup and disaster recovery for your data served via Cloudera Search,

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