The COVID-19 pandemic is forcing every business to see the world differently. From examining business continuity plans, modernizing workforce plans, or building supply chain resiliency, no facet of business has gone untouched. As organizations combat the economic fallout now and in the coming years, agility has never been more important. The key to remaining agile is a better use of data.
Learning additional languages is a common practice in the Netherlands. In primary school, we learn English and secondary school offers French, German, and a host of other options. Learning a new language and speaking it well is tricky.
This blogpost will cover how customers can migrate clusters and workloads to the new Cloudera Data Platform – Data Center 7.1 (CDP DC 7.1 onwards) plus highlights of this new release. CDP DC 7.1 is the on-premises version of Cloudera Data Platform.
The benefits of building a DevOps culture for software companies are clear. DevOps practices integrate once-siloed teams across the software development lifecycle, from Dev to QA to Ops, resulting in both faster innovation and improved product quality. As a result, most software development teams have deployed tools to enable DevOps practices across their workflow.
This article gives you an overview of Cloudera’s Operational Database (OpDB) performance optimization techniques. Cloudera’s Operational Database can support high-speed transactions of up to 185K/second per table and a high of 440K/second per table. On average, the recorded transaction speed is about 100K-300K/second per node. This article provides you an overview of how you can optimize your OpDB deployment in either Cloudera Data Platform (CDP) Public Cloud or Data Center.
Today, we’re announcing Data QnA, a natural language interface for analytics on BigQuery data, now in private alpha. Data QnA helps enable your business users to get answers to their analytical queries through natural language questions, without burdening business intelligence (BI) teams. This means that a business user like a sales manager can simply ask a question on their company’s dataset, and get results back that same way.
As organizations look to get smarter and more agile in how they gain value and insight from their data, they are now able to take advantage of a fundamental shift in architecture. In the last decade, as an industry, we have gone from monolithic machines with direct-attached storage to VMs to cloud. The main attraction of cloud is due to its separation of compute and storage – a major architectural shift in the infrastructure layer that changes the way data can be stored and processed.
In the lifecycle of a data warehouse in production, there are a variety of tasks that need to be executed on a recurring basis. To name a few concrete examples, scheduled tasks can be related to data ingestion (inserting data from a stream into a transactional table every 10 minutes), query performance (refreshing a materialized view used for BI reporting every hour), or warehouse maintenance (executing replication from one cluster to another on a daily basis).
