Snowflake Chart

Big Data Chart is a chart visualization performing calculations in Snowflake, whereas the in-memory chart uses the QPR ProcessAnalyzer in-memory calculation engine. Snowflake-powered calculation will enable practically unlimited scaling when the amount of data and number of users increase. When creating dashboards, Big Data Chart needs to be chosen when using Snowflake models. Big Data Chart can be added to dashboard by selecting the second item from the tool palette (labelled Big Data Chart).

Differences to in-memory chart

Visualization settings are the same between the Big Data Chart and in-memory chart. The data selection settings, measures and dimensions work differently. Differences are as follows:

• There are different set of analyzed objects, measures and dimensions available.
• Filtering cases and events can be done for each measure and dimension separately. This allows to build most KPI's flexibly without using custom expressions.
• Measures and dimensions have equal lists of available items. The difference is that an aggregation selection needs to be done for measures. Enabled by this, measures can be moved to dimensions and vice versa by clicking the Move to dimensions and Move to measures buttons.
• Custom expressions are written as SQL expressions which differs from the eventlog objects available in the in-memory charts. Note also that measure expressions in Big Data Chart don't include the aggregation logic, and thus the custom measure and dimension expressions are equal.
• Event attribute used as the event type can be set for each Big Data chart separately, to visualize the process flow from different angles. For more information, see chart settings.
• The Any datatype is not supported by the Big Data Chart in case and event attributes. Thus, when importing data, specific datatypes need to be set for each column, for case and event attributes to be available.
• Big Data Chart supports filtering similar to the in-memory chart, i.e., visualizations can be clicked to create filters for the shown data. Big Data Chart does not support expression based filter rules and thus there are some dimensions where filtering is not available. Other types of filter rules are same for Big Data and in-memory charts. Thus same dashboard can contain both types of chart components, and filtering between them works. Note that when an in-memory expression based filter is created from an in-memory chart, Big Data Chart cannot be shown as the filter cannot be calculated.
• Following measure/dimension settings are not available: Calculate measure for, Variable name, Custom aggregation expression, and Adjustment expression.
• Big data chart cannot be used with model using ODBC or expression datasources.

Calculation results are mostly the same between the Big Data Chart and in-memory chart, but there is one exception: If there are cases with events having exactly the same timestamp, in the Big Data Chart the order of events is the alphabetical order of event type names. In the in-memory chart, the order is based on the loaded data in the events datatable. The order of events affects for example the variations and flows the cases are belonging to.

Big Data Chart for SQL Server processing

Big Data Chart can also be used for models with local datatables, performing processing in SQL Server. The benefits are that the model doesn't need to be loaded into memory, consuming less memory in the application server. Also no time need to be spent for the model loading. The disadvantage is that SQL Server is not optimal for analytical queries, meaning in practice insufficient performance in large dataset. Despite the limitation, there are use cases when Big Data Chart is a suitable for models with local datatables:

• Eventlogs are filtered heavily so that the number of remaining cases and events are low (usually maximum of some thousands), maintaining the performance in sufficient level.
• If the model is currently not available in the memory, it's faster to use Big Data Chart comparing to the in-memory chart, when the required time to load the model into memory is taken into account.