Connection Tool - Programmatically Remove Unused Datasource Connections, and List All Connections
Managing connections within your Sisense environment can become complex over time, if there are a large number of connections, and connections are often added, and replace earlier datasource connections. In some scenarios unused connections can accumulate, potentially cluttering the connection manager UI with no longer relevant connections. Although unused connections typically represent minimal direct security risk, it's considered best practice to maintain a clean, organized list of connections, and in some scenarios it can be desired to remove all unused connections. Sisense prevents the deletion of connections actively used in datasources, safeguarding your dashboards and datasources from disruptions. However, inactive or "orphaned" connections remain after datasources are deleted or a connection is replaced, potentially contributing to unnecessary UI complexity in the connection manager UI. Connections can be of any type Sisense supports, common types include various SQL connections, Excel files, and CSV files, as well as many data providers, such as Big Panda. This tool can also be used to list all connections, with no automatic deletion of unused connections.
BI Solutions: High Level Design
Based on the business requirements summary you've created in the previous step, you can now design the high-level overview of your BI Solution. This step aims to translate the business strategy into operational terms and associate it with the required data. For that, we will use the Dashboard Planning Document. The document will assist you to define the business goals and objectives and break it down into specific KPIs and measures. Then, we will be able to link each measure to its relevant data and formula. The following template will assist you in planning your dashboard. You can download the template at the bottom of the document. Dashboard Hierarchy Creating a dashboard flow chart is recommended while planning any BI project. Dashboard Outline This section describes the dashboard objective, the different types of users that will be using the dashboards and the expected call to action, based on this dashboard. Business Objectives - Main Business Questions A Business Objective is a Sub-goals, the steps to achieve the main goals, Medium or small in size, Concrete Shorter term, SMART. For Example: Increase the number of qualified leads Reduce marketing cost Dashboard Mockup A freestyle sketch of the final dashboard, to illustrate the dashboard layout, the selected visualization for each KPI, and additional information such as filters, plugins, etc. KPIs Architecture Break each objective into KPIs and measures, and specify the formula and data tables that are required for achieving the KPIs. Key performance indicators - Business metrics which support decision making. KPIs are used to evaluate factors which are crucial to achieving the business objectives. Measures - Quantifiable values which can stand alone or when composed with other measures form KPIs. For Example: ElastiCube Design Maps, in detail, the data elements that are required for analyzing the above KPIs. This map will help you to plan the ElastiCube structure and connections between the data elements in your model Implementation Constraints: This is the place to mention any general constraints you have on your analysis and limitation in terms of the data model (such as data security, the frequency of use, special business logic, etc.)GIT Instruction How To
Learn how to seamlessly migrate Sisense assets between environments using GIT with our step-by-step guide. This tutorial covers creating projects, adding and committing assets, and pulling updates to ensure smooth transitions across instances. For additional details, explore our linked resources on Sisense GIT integration.
[Linux] Resolving data model connection errors in Sisense after importing models
This article addresses an issue where data models in Sisense fail to open, producing a "Cannot read properties of undefined (reading 'datasets')" error message. The problem appears during the import of models from one environment to another, accompanied by difficulties in cleaning up datasets using APIs.
Choosing the Right Data Model
This post has become outdated. You can find guidance on choosing a data model on our documentation site here. https://docs.sisense.com/main/SisenseLinux/choosing-the-right-data-model.htm 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. Sisense recommends that you discuss your needs and requirements with Sisense's technical team during the Jumpstart process, so the result will best meet your business expectations. Table of Contents Definitions The ElastiCube Data Model Importing data into an ElastiCube data model allows the customer to pull data from multiple data sources on-demand or at a scheduled time, and create a single source of truth inside Sisense. The imported data can then be transformed and aggregated to meet your business needs. Once imported, the data snapshot is used to generate analytical information. The process of importing the data, known as a "Build", includes the following steps: Extract the data: Query the different data source(s) for data. Load the data: Write the data extracted to Sisense (the local MonetDB). Transform the data: Transform the local MonetDB (using SQL queries). To read more about ElastiCubes, see Introducing ElastiCubes. The Live Data Model Using a Live data model does not require importing data. Only the data's schema needs to be defined. Once configured, analytical information required by the user is queried directly against the backend data source. To read more about Live models, see Introducing Live Models. Determining Factors Refresh Rate One of the most fundamental aspects of determining your data model is your data's refresh rate. The data refresh rate refers to the age of the data in your dashboards: For Live models, the data displayed on your dashboards is near-real-time, as every query is passed directly to the backend database. A good example of using a live model (due to refresh rate requirements) is a dashboard that shows stock prices. For ElastiCubes, the data displayed on your dashboard is current to the last successful build event. Every query is passed to the local database for execution. A good example of using an ElastiCube (due to refresh rate requirements) is a dashboard that shows historical stock prices. In this case, a daily ETL process will provide results that are good enough. To make a choice based on this factor, answer the following questions: How frequently do I need to pull new data from the database? Do all my widgets require the same data refresh frequency? How long does an entire ETL process take? Data Transformation Options The ETL process includes a "Transformation" phase. This transformation phase usually includes: Migrating the data tables into a dim-fact schema Enriching your data Pre-aggregating the data to meet your business needs The amount of data transformation on Sisense helps determine the suitable data model: For Live models, Sisense allows minimal to no data transformation. Data is not imported before a query is issued from the front end. Therefore, data cannot be pre-conditioned or pre-aggregated. Most data sources used by Live models are data warehouses that may perform all data preparations themselves. For ElastiCubes, data is imported before a query is issued from the front end. Therefore, it may be pre-conditioned and pre-aggregated. A user may customize the data model to optimally answer their business questions. To make a choice based on this factor, answer the following questions: Is my data in a fact-dim schema? Does my data require enriching or pre-conditioning? Can my data be pre-aggregated? Operational Database Load Your operational databases do more than serve your analytical system. Any application loading the operational databases should be closely examined: For Live models, Sisense will constantly query information from your operational databases, and feed it into your dashboard widgets. This occurs every time a user loads a dashboard. For ElastiCubes, Sisense highly stresses your operational databases during an ETL process while reading all tables. To make a choice based on this factor, answer the following questions: Does the analytical system stress my operational database(s)? Can the query load be avoided by using a "database replica"? Operational Database Availability Your operational database(s) availability is critical for collecting information for your analytical system. For Live models, all queries are redirected to your data sources. If the data source is not available, widgets will generate errors and not present any data. For ElastiCubes, data source availability is critical during the ETL process. If the data source is not available, the data in your widgets will always be available, but not necessarily be up to date. To make a choice based on this factor, answer the following questions: How frequently are analytical data sources offline? How critical is my analytical system? Is being offline (showing out-of-date information) acceptable? Additional Vendor Costs Various database vendors use a chargeback charging model, meaning that you will be charged by the amount of data you pull from the database or the computational power required to process your data. For Live models, every time a user loads a dashboard, each widget will trigger (at least) one database query. A combination of a chargeback charging model and a large user load may result in high costs. For ElastiCubes, every time the user triggers an ETL process, a large amount of data is queried from the database and loaded into Sisense. To make a choice based on this factor, answer the following questions: What is the number of users using my dashboards / What is my "build" frequency? Which data model will result in lower costs? What is the tipping point? Are you willing to pay more for real-time data? Database Size For ElastiCubes, please refer to these documents: Introducing ElastiCubes Minimum Requirements for Sisense in Linux Environments For Live models, there is no limitation as data is not imported to Sisense, only the data's schema. To make a choice based on this factor, answer the following questions: What is the amount of data I need in my data model? What is the amount of history I need to store? Can I reduce the amount of data (e.g., trimming historical data? reducing the number of columns? etc.) Query Performance Query performance depends on the underlying work required to fetch data and process it. Although every widget generates a query, the underlying data model will determine the work necessary to execute it. For ElastiCubes, every query is handled inside Sisense: The client-side widget sends a JAQL query to the Sisense analytical system. The query Is translated into SQL syntax, and run against an internal database. The query result is transformed back to JAQL syntax and returned to the client-side. For Live models, every query is forwarded to an external database and then processed internally: The client-side widget sends a JAQL query to the Sisense analytical system. The query Is translated into SQL syntax, and run against an external database. Sisense waits for the query to execute. Once returned, the query result is transformed back into JAQL syntax and returned to the client-side. To make a choice based on this factor, answer the following questions: How sensitive is the client to a delay in the query's result? When showing real-time data, is this extra latency acceptable? Connector Availability Sisense supports hundreds of data connectors (see Data Connectors). However, not all connectors are available for live data models. The reasoning behind this has to do with the connector's performance. A "slow connector" or one that requires a significant amount of processing may lead to a bad user experience when using Live models (that is, widgets take a long time to load): For ElastiCubes, Sisense allows the user to utilize all the data connectors. For Live models, Sisense limits the number of data connectors to a few high-performing ones (including most data warehouses and high-performing databases). To make a choice based on this factor, answer the following questions: Does my data source's connector support both data model types? Should I consider moving my data to a different data source to allow live connectivity? Caching Optimization Sisense optimizes performance by caching query results. In other words, query results are stored in memory for easier retrieval, in case they are re-executed. This ability provides a great benefit and improves the end-user experience: For ElastiCubes, Sisense recycles (caches) query results. For Live models, Sisense performs minimal caching to make sure data is near real-time. (Note that caching can be turned off upon request.) To make a choice based on this factor, answer the following questions: Do I want to leverage Sienese's query caching? How long do I want to cache data? Dashboard Design Limitations Specific formulas (such as Mode and Standard Deviation) and widget types (such as Box plots or Whisker plots) may result in "heavy" database queries: For Live models, Sisense limits the use of these functions and visualizations as the results of these formulas and visualizations may take a long time, causing a bad user experience. For ElastiCubes, Sisense allows the user to use them, as processing them is internal to Sisense. To make a choice based on this factor, answer the following questions: Do I need these functions and visualizations? Can I pre-aggregate the data and move these calculations to the data source instead of Sisense? See also Choosing a Data Strategy for Embedded Self-Service.How to avoid query caching in live data models
Query caching is a common mechanism used to enhance performance by storing frequently accessed data in memory. However, in scenarios where data models are continuously updated in real-time, caching can lead to outdated data being displayed. This article guides how to bypass query caching in Sisense Live Data Models to ensure that updated information is always displayed.Resolving "Internal Server Error" When Importing Model with Generic JDBC Connector
This article addresses the issue of encountering an "Internal Server Error" when importing a model from a local desktop to a production environment using the Generic JDBC connector in Sisense. This error often arises due to compatibility issues with older JDBC frameworks. Here, we provide a solution to resolve this error by updating the JDBC connector and connections.Redirect users to different dashboards based on dashboard filters
This article discusses and shares the full code of a dashboard script that redirects users to a different dashboard ID based on the user's filter selections or initial loaded filter state. In the particular example shared in this article, the script checks whether the selected date filter (either from a members filter or a from/to filter range) includes an earlier date than the earliest date in the current dashboard's datasource. If this is the case, the script redirects the user to a specified alternate dashboard, preserving any additional URL segments and query parameters in the URL. Any other type of filter state can also be used to determine on when the script should redirect, including non-date filters using similar scripts.
Creating a Year-Over-Year Chart in Sisense
This article addresses the issue of creating a year-over-year comparing chart in Sisense that shows a Key Performance Indicator (KPI) for the current calendar year against the previous calendar year. The chart should display KPI data for the current year's months that have passed and the full previous year.Pivot and Table Widget Scripting for Replacing Values with Colored Arrows
Sisense table and pivot widgets offer extensive customization options for data presentation, allowing developers to tailor widget content to specific needs, as discussed in detail in previous articles. While the Pivot 2.0 API includes a built-in transformPivot function for data transformation, Table widgets typically require direct DOM manipulation. This article provides a step-by-step guide for replacing numeric (or other) values in both widget types with arrow characters via scripting. It also demonstrates how to assign colors or apply conditional logic to these transformations. Examples include the use of Unicode arrow characters, though any other characters or combinations of characters can of course be utilized.
