In mid-2017, we were working with one of the world’s largest healthcare companies to put a new data application into production. The customer had grown through acquisition and in order to maintain compliance with the FDA, they needed to aggregate data in real-time from dozens of different divisions of the company. The consumers of this application, of course, did not care how we built the data pipeline. However, they cared greatly that if it broke,
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,
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,
Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a more powerful user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows. Leveraging Hue as the SQL development workbench, we continue to see usage of the platform increase and the number of analytic use cases grow –
Successful cluster administration can be very difficult without a real-time view of the state of the cluster. Solr itself does not provide aggregated views about its state or any historical usage data, which is necessary to understand how the service is used and how it is performing. Knowing the throughput and capacities not only helps detect errors and troubleshoot issues, but is also useful for capacity planning.
Questions may arise, such as:
- What is the size of my cluster and each collection?