Analytical Engine Overview


Sisense's modeling framework is a set of modeling languages and algorithms for BI analysis. Sisense's Analytical Engine is an algorithm that enables you to generate your query in the SQL dialect of the specific data-source with low-no code. The query is generated according to the widget elements (for example, measures and filters) and the user's data security role. It then executes the SQL query on the data source.

Analytical Engine is based on the Dimensional Modeling methodology, which is a logical design technique that presents the data in a standard, intuitive framework that allows high query execution performance at scale.


Analytical Engine only works in Linux environments.

In addition to supporting ElastiCube connectors, Analytical Engine also supports live providers. For the list of live data sources supported by Analytical Engine see Live Data Source Connectors in Data Source Connectors.

Analytical Engine:

  • Enables customers to perform real-time analytics, directly over the data provider (Live).
  • Leverages the provider's syntax via the open source Apache Calcite (
  • Improves the performance and comprehensibility of the SQL queries.
  • Defines the query in a very structured way, for improved readability and comprehensibility.
  • Validates the query components on widget load and provides actionable error messages when necessary.
  • Returns a null value if the query is invalid (due to filters or measures).
  • Queries table values (not keys), for better null handling and advanced calculations.
  • Exposes the generated SQL query to the dashboard designer, while respecting data security rules.
  • Calculates top ranking filters per widget context, simplifying the implementation of the top ranking functionality for dashboard designers.

Analytical Engine's Extended Query Capabilities

Null Handling

By default, Null values are now displayed, and can be filtered out by the user. Null can also be used as a key. Analytical Engine joins dimension tables when selecting entries for top ranking filters, meaning that null can be a top ranking result.

Top/Bottom Ranking Filter Handling

Analytical Engine handles top/bottom ranking requests as follows:

  • When each item has a unique rank, the results are ordered by the rank.
  • When multiple items share a rank, those items are further sorted according to the other dimensions used for the presentation of the results (within the rank).


Analytical Engine inserts filtering clauses into the innermost SQL statement. This means that filtering is the first operation performed, minimizing the number of rows that need to be scanned in order to process the query. Complex filters, such as top ranking and filter on measure are significantly improved as a result.

Original Data Types


Casting is only applied when necessary, such as when calculating an average or handling division of integers, to maintain the original data type wherever possible. This improves performance and the accuracy of results.

Better Precision for Float Numbers

Float numbers inherit the original datatype from the live source, yielding more accurate results.

Query JOIN Path

Sisense Low-No Code approach for mashing up data and dashboard design can sometimes result in a query that is generated by the Analytical Engine that is not optimized to the specific analytical use case. A big impact on both query execution time and result correctness is the Join Path that is determined by the Analytical Engine, to calculate filters and measures that correspond with the widgets elements.


Analytical Engine uses Dimensional Modeling concepts to generate queries, resulting in improved and consistent path selection - only schema changes affect the JOIN path in the generated query.

User Impact on the Query JOIN Path

ForceScope Filter

By default, Analytical Engine ignores "Include All" filters when selecting the join path. If a dashboard designer wants to control the join path and use the Include All functionality, the following must be added to the filter's Advanced tab (see Creating Dashboard Legacy Filters):

This enforces adding the table to the join path.

Performance Improvements of SQL Queries

Analytical Engine improves the performance of SQL queries by:

  • Calculating measures from the same table simultaneously in one scan.
  • Performing caching per security role, and can be used by all users with the same data security role.
  • Applying filters once for all widget measures.

Comprehensibility of SQL Queries

Analytical Engine displays the generated SQL query to the dashboard designer in a more comprehensible way. It:

  • Generates shorter queries.
  • Applies data security rules to your queries so data that must not be displayed to users is obfuscated with asterisks (***).
  • Uses WHERE IN statements for filters, improving the accuracy of query results.
  • Returns more accurate and predictable results for past period functions, such as Past X, DiffPast X, Growth, Growth Rate, GrowthPast X.

This greatly simplifies query debugging, and ensures that it yields the desired result.


The Jaqline plugin is not supported by the Analytical Engine. To explore the generated query, use the analyze query feature.

Many-to-Many Relationship Handling

Analytical Engine uses WHERE IN statements to:

  • Reduce many-to-many scenarios. It is used in filters, to select data from several tables with higher performance.
  • Maintain the integrity of the original data set total, for pivot table subtotals and grand totals. This improves the performance and simplicity of the SQL query calculations.


Data tables:

1 100
2 200
3 300

1 Classics 1 Comedy
1 Classics 1 Horror
1 Classics 2 Drama
2 Trending in Israel 2 Drama
2 Trending in Israel 3 Comedy
2 Trending in Israel 3 Drama
filter GENRE = Drama
DURATION (measure) => FILMS_Table
GENRE (filter) => CATALOG_Table

Query 1: WHERE IN

Query2: Join - Adding GENRE as Slicer


Median(DURATION) - Join

 SELECT "dTable_aCATALOG_agenre"."value" AS "genre_res"
     FROM (
     SELECT "t2"."genre"
     ,MEDIAN("t2"."duration") AS "median__duration_res"
     FROM (
     SELECT "t1"."genre"
     FROM (
     SELECT "aFILMS"."aid" AS "id"
     ,"aFILMS"."aduration" AS "duration"
     ) AS "t"
     SELECT "aCATALOG"."afilmXwAaid" AS "film_id"
     ,"aCATALOG"."agenre" AS "genre"
     WHERE "aCATALOG"."agenre" IN (
     SELECT "dTable_aCATALOG_agenre"."key"
     FROM "aBLOCKFLIX"."dTable_aCATALOG_agenre"
     WHERE "dTable_aCATALOG_agenre"."value" IN ('drama')
     ) AS "t1" ON "t"."id" = "t1"."film_id"
     ) AS "t2"
     GROUP BY "t2"."genre"
     ) AS "it"
     INNER JOIN "aBLOCKFLIX"."dTable_aCATALOG_agenre" ON "it"."genre" = "dTable_aCATALOG_agenre"."key"
    ORDER BY "dTable_aCATALOG_agenre"."value"

Median(DURATION) - Where In

 SELECT MEDIAN("t4"."duration") AS "median__duration_res"
     FROM (
     SELECT "aFILMS"."aduration" AS "duration"
     WHERE "aFILMS"."aid" IN (
     SELECT "film_id"
     FROM (
     SELECT "aCATALOG"."afilmXwAaid" AS "film_id"
     ,"aCATALOG"."agenre" AS "genre"
     ) AS "t"
     WHERE "t"."genre" IN (
     SELECT "dTable_aCATALOG_agenre"."key"
     FROM "aBLOCKFLIX"."dTable_aCATALOG_agenre"
     WHERE "dTable_aCATALOG_agenre"."value" IN ('drama')
    ) AS "t4"</pre>


In the example below, age_range (from the Ecommerce table) is presented in the box plot and is considered as slicer for the calculation over category_id (from the Category table). The box plot calculation uses a JOIN statement, while the indicators use the WHERE IN statement. Hence, the results are different, but both answers are correct, as they are actually different business questions.

From Ecommerce:

filter: age range = 35-44

Boxplot - analyze category_id by age range

Indicators - analyze category_id

Data Source Function Requirements

Analytical Engine commonly uses the following analytical functionality. This functionality is translated into the listed dependent SQL elements when sent to the data source, so the data source can run the query and retrieve the required result set. If your data source does not support these SQL elements, you will get an error if the SQL element is used. Therefore, it is important that you check whether your data source supports these SQL elements.

Analytical Functionality Description Dependent SQL Element

Using columns from two tables in a single widget

For any reason such as calculation, filter, presentation

JOIN Statement

Using custom import tables

Define the data model by an SQL statement

WITH statement (CTE to return a temp table)

SUM The total sum of a numeric column SUM
AVG The average value of a numeric column AVG
MIN The minimum value of a numeric column MIN
MAX The maximum value of a numeric column MAX
Count The number of members in a column without null values COUNT
Duplicate Count The number of distinct members in a column without null values COUNT + DISTINCT
Query Limit Limiting the number of rows returned in a result set LIMIT + OFFSET
Presenting dates in widget Presenting data according to some date granularity DATE_TRUNC

PastX Functions(for ex. pastYear)

Calculating the same measure during a parallel time period versus the currently presented time period


Date Diff Functions(for ex. Day Difference)

Calculating the difference between end and start times



Calculating the rank of a value among the list of values

ROWNUMBER / ROW & OVER + Partition By (Window Function)

Running Sum/Average

Calculating the running total of the measure by the defined dimension, according to the current sorting order in the widget

OVER + Partition By (Window Function)


The median value of a numeric column



The percentile value of a numeric column



The standard deviation of the given values (Sample)



The standard deviation of the given values (Population)



The variance of the given values (Sample)



The variance of the given values (Population)



The base-e logarithm of the given value



The base-10 logarithm of the given value