Data Warehouse Optimization
Operations and Events Data Warehousing
Research and Discovery Data Warehousing
Data warehouse optimization
optimize legacy data warehouse implementations, by partially or fully migrating difficult workloads from traditional data warehouse infrastructures, to cloudera data warehouse. realize the opportunities presented in modern data warehousing by deploying many new use cases while enjoying significant cost savings on an ongoing basis. battle tested, open source engines such as impala, hive llap, hive on tez and tools such as hue and workload xm provide flexible and fast analytics on structured and unstructured data, together, at scale.
Operations and events data warehousing
sb沙巴体育a new way of looking at analytics and data warehousing - analyzing large amounts of events and time-series data originating from machine logs, sensors, and other devices at the edge. the real time analysis of these very large data sets, that constantly need to scale, typically cannot be done effectively or cost efficiently in traditional infrastructures. cloudera data warehouse harnesses the power of highly scalable engines such as kudu and druid to tackle massive volumes of fast moving data. add cloudera dataflow to harness the power from the edge.
Research and discovery data warehousing
sb沙巴体育a new focus on exploration and discovery of new correlations, patterns and insight inform our businesses on what we can expect from our future. tackling the hard problems of analyzing across textual and relational data, often for exploration and experimentation, means sifting through vast amounts of unstructured data and correlating them with relational data. cloudera data warehouse makes this easy through the power of query engines such as solr, impala, and hive. add cloudera data science workbench to apply machine learning at scale.