The Hadoop ecosystem also includes several SQL-on-Hadoop software interfaces including Apache Impala, Apache Hive, Drill and Kudu, Spark SQL, and Presto, which provide convenient ways for the analyst to use BI tools on Hadoop. However, Hadoop is built on a batch processing framework not designed for interactive workloads, so the analytic performance of traditional BI tools on these interfaces tends to be slow and frustrating for the enterprise analyst and business user. Zoomdata solves this performance problem via innovative patented technologies such as micro-query based Data Sharpening, which enables response times in seconds versus minutes or hours.
As we've explained, Zoomdata excels as a Hadoop analytics and data visualization tool and can connect directly to HDFS as well as to SQL-on-Hadoop technologies. Beyond that, as users interact with data visualizations, Zoomdata takes the query to the data using patented Data Sharpening with micro-queries. This is critical to achieving speed of thought performance on Hadoop. Unlike other BI tools, we don't build cubes or move data to another data store inside of or outside of Hadoop.
Of course, a modern data architecture can include traditional sources as well. With Zoomdata Fusion, you can enrich data from Hadoop with reference data from traditional sources such as relational databases or flat files. Zoomdata Fusion combines and integrates data from multiple sources — making it appear as a single source. You can also correlate real-time with historical data. And you can also correlate it with data from cloud sources such as Amazon Redshift or Google BigQuery.