Analytical Need A common requirement in data analysis is to be able to view and compare slices of data from different time periods to current date. Business applications for this vary, from identifying trends to predicting future behavior, requir...
How to avoid query caching in live data modelsStep-by-Step Guide: Understanding the Effects of Caching:When data is updated with SQL statements (e.g., updates in Snowflake), Sisense's query caching might not immediately reflect these updates.Widget r...
Reading multiple Excel files into Sisense data model Introduction: If you are migrating from Windows to a Sisense Linux-hosted solution, on the Sisense Windows machine you may use an SFTP client that moves Excel files placed in a remote server's fol...
User Parameters Use Case - Personalized Data Filter Requirements This article explains one of the example use cases of User Parameters laid out in User Parameters - What They Are and What They're For (Example Use Cases). User Parameters can be used ...
Introduction This forum post will demonstrate how to save your ElastiCube .ecdata file which contains your ElastiCube schema and data, and the .dash file of your desired dashboard. Purpose/Benefits The .ecdata file contains both the data and the lo...
User Parameters Use Case - Enforcing Data Isolation / Row-Level Security Rules from Source DatabaseThis article explains two of the example use cases of User Parameters laid out in User Parameters - What They Are and What They're For (Example Use Cas...
Analytical Need For a metric, I need to calculate the month difference between the min and max date of purchase for each brand in my dataset. I want the dates to be dynamic in respect to filtering, etc. I am using the attached .ecdata and .dash f...
Data accumulation is SiSense's method for building ElastiCube data without completely refreshing a table. This post describes the data accumulation functionality and gives a user the ability to customize and optimize their ElastiCube build process.Th...
Analytical Need A key table (click the link to download a demo Data Model) is a central table that stores all the relationships between the primary key values. Modeling Challenge The main advantages of using Key Table: It helps to tidy up a ...
Introduction This post will explain how to denormalize your ElastiCube schema in order to get faster query response in SiSense Web. Purpose/Benefit Denormalization of data is used to reduce the query response time in SiSense Web. It is most useful f...
Source data that is flattened may require some or all of the columns of a source table to be transposed, in order to work seamlessly with SiSense’s functionality. This post will outline the cause and two possible solutions to this issue. The first so...
Both .ecdata and .ecube files are used to make backups of data and transfer data between ElastiCube Servers. Your data model or schema is saved in an ecube file. ecube files contain the schema and the data connection settings required for building yo...
Part of having a successful BI tool is having your data refreshed on the time you need it. To ensure your builds are working as expected, you can follow the steps provided below. Step 1 - Setting Up a Build Alert Follow the documentation of Sise...
IntroductionThis script exports to CSV a complete list of the front-end dependencies for each ElastiCube. It provides information about whether an ElastiCube is used for data security, hierarchies, build alerts, pulse KPIs, or starred formulas. It al...
Introduction This article describes how to enable an alert* when there is a risk of Many-To-Many relationship (M2M) between two or more tables. * This implementation involve the use of Pulse and it's supported in Sisense v6.5 and above. Motivation A...
Note: Sisense's default ElastiCube Server Console configuration is optimally tuned for ElastiCube and dashboard performance. It is generally not recommended to change these settings (except for Default server data folder and Rserve).This article iden...
Sisense supports floating-point numbers (IEEE 754 standard) and allows you to perform an arithmetic calculation on these numbers. Floating-point numbers suffer from a loss of precision when represented with a fixed number of bits (e.g., 32-bit or 64-...
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 com...
Analytical NeedThere are many cases where our data comes in with different granularity levels. For instance, Actual vs. Target where the actuals are at the product level and the targets are per sub category (higher hierarchy of the product). How can...
Analytical Need We have multiple sources for our measures and multiple sources to slice & dice these measures. Modeling Challenge How do we build a schema that will enable us to perform our needed analysis? We will need to connect all of our t...
