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Use the navbar to navigate throughout this demo.
These reports were generated, or imported, using Spark jobs that aggregated historical time series data stored in Cassandra in 1-minute increments. The Spark job then performed daily and monthly roll-ups of 1440 data points per day. Each time a report is selected, the final query happens using the DataStax Python-Driver for Apache Cassandra to read from the Cassandra tables that were used to house the results of the aggregate Spark jobs run through DataStax Enterprise. You will notice that the monthly reports are quicker to respond since the request only selects 12 data points a year as opposed to 365 data points per year that still need to be graphed. However, running on a dedicated machine will increase throughput while running on even a small cluster will increase throughput linearly.
This page allows Spark SQL queries to be run against a Spark SQL Thriftserver against data stored in Cassandra. The page comes with pre-selected queries to get started, but should accept anything you throw at it. Feel free to tinker around and construct your own queries.
Similar to the Sample Live Queries page, this page allows custom queries to be run. However, this page allows users to dynamically change Spark SQL queries without requiring any knowledge of writing Spark SQL queries.
In some cases, businesses already own vast amounts of data in their own Hadoop clusters. Using DataStax Enterprise's BYOS feature, DataStax Enterprise can easily pull in existing data from a Hadoop instance and run Spark SQL queries that will mix in newer Cassandra data as well. This allows your business to focus on moving forward instead of the tedious process of undergoing data migration to use the next big thing. In order to showcase how you would craft a query to run against existing data, we've created a sample query that runs against DataStax Enterprise's DseFs for demonstration purposes.
The Cassandra Time Series data was generated with the DataStax Java Driver for Apache Cassandra. The source can be found at demos/weather_sensors/src/com/datastax/dse/demos/weather. If you loaded data via the COPY command through CQLsh, you loaded aggregated data that came from rolling up the weather data, from minute increments, into daily and monthly increments using the aggregation Hive script found at resources/aggregates.q.