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Knowledge Base Articles

Linux | ElastiCube Local Files System

Linux | ElastiCube Local files system Sisense application utilizes MonetDB to write raw data into Elasticubes (commonly referred to as EC or cube). Why does this happen? This allows Sisense to query the data from itself rather than sending a query ba...

Google Analytics CDATA Connector

Google Analytics CDATA Connector What You’ll Learn: In this course users will learn how to: Download a CDATA Google Analytics (GA) driver Install a CDATA GA driver Configure a CDATA GA driver How to connect using a CDATA GA driver Prerequisites: This...

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antonvolov by Sisense Team Member
  • 1 kudos

Sisense Data Pipeline Best Practices

Sisense Data Pipeline Best Practices Architecturally, It is important to understand the role of Sisense in your use case and where it will fit best in your data architecture from a high-level perspective. Sisense is an excellent visualization tool th...

Darwin by Sisense Team Member
  • 4 kudos

Resolving JVM Heap Errors During A Cube Build

Resolving JVM Heap Errors During A Cube Build Occasionally when building a cube, you may run into an “Out Of Memory Error: Java Heap Space” error, as seen below.The build logic runs inside a Java Virtual Machine, which utilizes heap space as temporar...

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HunterB by Sisense Team Member
  • 0 kudos

How To Troubleshoot Build Failures (Linux OS)

HOW TO TROUBLESHOOT BUILD FAILURES (Linux OS)Building an ElastiCube imports the data from the data source(s) that have been added. The data is stored on the Sisense instance, where future dashboard queries will be run against it. You must build an El...

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Krutika by Sisense Team Member
  • 4 kudos

Many-to-Many Relationships - Knowing and Avoiding Them

Introduction The following article discussed "Many-to-Many" relationships - Both expected and unexpected. It focuses on how they occur and the best practices which one can implement to avoid them. Table of Contents IntroductionTable of ContentsTable ...

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Planning, Executing, and Monitoring your Dashboard

Introduction The following article discusses a dashboard's (high-level) development cycle. It breaks the process into easy measurable steps that start from the initial KPI planning all the way to maintaining and adjusting your end product. Table of C...

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Choosing the Right Data Model

Introduction Customers often run into the question of which data model they should use (an ElastiCube, a Live model, or a Build-to-Destination). The following article presents some of the aspects you should consider when choosing between them. Sisens...


Modelling Aggregate Facts

Analytical Need There are cases where our data arrives in aggregate form, like in Google Analytics (where the lowest granularity level is daily). If we wish to free up clutter in the dashboard and prevent the user from figuring out which field comes ...

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Build A Flattened Hierarchy From 2 Levels

Analytical Need In many cases a hierarchy appears in the data structured as 2 levels - A parent and a child. The business user would like to see the entire hierarchy and analyze the data according to the different levels, but can't do that since they...

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Calculate Average Time Between Transactions

Introduction Calculating transaction frequency for a given dimension is a common requirement across many verticals. e.g. Average Time Between Purchases by a Customer. Average Time between support tickets etc. While this can be calculated in SQL it ca...

Calculating Values For The Most Recent Date

The point of this article is to provide a method to a value associated with the most recent data in a date selection. Implementation Step 1: in the Elasticube, create a field transforming the date field into an integer using the formula below. Then r...

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