Understanding the subscription value meters

Calculating and managing Data for Analysis volume

Understanding how Data for Analysis is calculated can help you make the most of your Data for Analysis capacity in Qlik Cloud. In this section, we'll take a closer look at how monthly peak, data loading, app reloads, and data creation are measured, as well as look at data management best practices.

Monthly peak

The daily peak represents the total Data for Analysis volume for a given day. The maximum daily peak for the month is your monthly peak, which is measured against your purchased Data for Analysis capacity.

The daily peak is calculated as the sum of all file sizes for formats like QVD, CSV, or text files plus the maximum bytes ingested from external sources for app reloads on that day.

Let's look at the following example:

Throughout the month, daily data usage varies. On day 1, it reaches a peak of 3 GB, while on day 2, it’s 6 GB, and so on until the end of the month. The highest daily usage, recorded on day 2, was 6 GB. This gives us a monthly peak of 6 GB. On days where there is no app reload, as seen on day 3 in our example, the value from the previous day is carried forward.

Loading data into Qlik Cloud

Data loaded into Qlik Cloud from external sources is counted towards the daily peak. When you load data into a tenant, it is counted once and can be analyzed and used multiple times. Data loaded into multiple tenants is counted multiple times.

Data contributing to the daily peak is measured as follows:

• File-based data loaded via a Qlik Sense app is measured by its file size.

• App reloads using queries or connectors are counted as the maximum bytes ingested from the data source. When there are multiple reloads happening on the same day, the largest app size is the one that will be counted towards the daily peak. For example, if an app is reloaded during a day with 0.75 GB, 1.25 GB, and 1 GB, respectively, the value used for that day would be 1.25 GB.

  As long as an app exists in the Qlik Cloud tenant, the maximum bytes ingested are evaluated for the app.

• Data loaded into Qlik Cloud via Qlik Data Gateway - Data Movement is measured by the size of the resulting QVD file.

• Apps that are uploaded or loaded via file import, either in the Analytics activity center or using qlik-cli, are measured by the app's static byte size.

• Uploaded QVD files are measured by their file size.

In the following situations, data is not included in the calculation of the daily peak:

• Data loaded into a user's personal space is not counted, as long as it is restricted to that space. If the user moves the data to a shared space to collaborate with others, it will be counted.

• If a reload fails, the bytes ingested are not counted. However, any resulting QVD files are counted.

• When you load an app with data that already resides in Qlik Cloud, the data load is not counted. For example, copied or binary loaded apps (loading data from another Qlik Sense app) do not impact the daily peak, provided they’re not reloaded from an external source.

Measuring bytes ingested for app reloads

The following applies when you reload a Qlik Sense app from an external source:

• You can reload an app multiple times from the same source dataset without affecting the daily peak, as long as the data volume remains unchanged.

• If the source dataset increases in size, the daily peak is affected. Each additional GB of data added to the dataset contributes an equivalent amount to the data ingested during the reload.

• Conversely, if the source dataset decreases in size, this reduction is also reflected in the daily peak. For example, if the dataset size is reduced by 0.25 GB, the reload size decreases by the same amount. However, if a 1 GB reload occurred earlier in the day, the daily peak for that day would be 1 GB. The reduction would only be reflected in the daily peak for the next day.

• Changes in the content of the source dataset, without altering its size, do not impact the daily peak. The daily peak is only affected by the data volume.

• If you query the same dataset multiple times within a single load script, all those queries are counted separately, and their data volumes are summed up. For example, if you have a load script that includes three queries of 1 GB each from the same dataset, all three of those queries are counted individually. So, the total data counted towards your daily peak is 3 GB.

• Loading an app and subsequently dropping the table does not reduce the daily peak, as the daily peak is based on the maximum app reload size for the day.

• If you load an app and then delete it on the same day, it will still contribute to the daily peak for that day. However, it reduces the daily peak for the following day when the app no longer exists.

Measuring data loaded into QVD files with Qlik Talend Data Integration

The following applies when you load data into a QVD file from an external source using Qlik Data Gateway - Data Movement:

• You can upload, import, or generate a dataset multiple times without affecting the daily peak, as long as the data volume remains unchanged.

• If the source dataset increases in size, the daily peak is affected. Each additional GB of data added to the dataset contributes an equivalent amount to the size of the resulting QVD file.

• Conversely, if the source dataset decreases in size, this reduction is also reflected in the daily peak. For example, if the dataset size is reduced by 0.25 GB, the size of the resulting QVD file decreases by the same amount.

• Changes in the content of the source dataset, without altering its size, do not impact the daily peak. The daily peak is only affected by the data volume.

Loading apps from external and internal sources

It is important to understand how data loaded into apps affects the daily peak, depending on the data source. Let's consider the following scenarios where data is loaded from different sources.

• An app is loaded from an external source

  When you load data from an external source into an app, it counts as bytes ingested. For example, if you load 10 GB, the contribution to the daily peak is 10 GB.

• An app is loaded from a QVD in Qlik Cloud

  Loading data into an app from a QVD file residing in Qlik Cloud does not count toward the daily peak. If 10 GB of data is loaded into an app from the QVD, no data is counted because there is no ingestion of external data. The contribution to the daily peak is 0 GB.

• A new QVD file is generated from a QVD in Qlik Cloud

  Data loaded into a QVD generator app from a Qlik Cloud-based QVD is not counted toward the daily peak. However, the resulting QVD file generated from the app does count. For example, if a 10 GB QVD file is transformed into a new 5 GB QVD, the contribution to the daily peak is the sum of the two files, which is 15 GB. As there is no external data ingestion, the load of the QVD generator app (a dedicated app that creates a data model and generates a QVD) is not counted.

• An app is loaded from both external and internal sources

  If an app loads 10 GB from an external source and 5 GB from a QVD within Qlik Cloud, the total contribution to the daily peak from the app is 10 GB, as only the data loaded from the external source is counted.

Creating data in Qlik Cloud

When you create new data in Qlik Cloud, whether by copying data files or deriving it through combining and processing existing raw data, it counts toward the daily peak. Data is measured as the total size of the files generated during the data creation process. The created data is only counted once, regardless of how many apps use it.

Consider these examples of data creation:

• Creating a 1 GB QVD file using the STORE statement adds 1 GB to the daily peak.

• Copying a 1 GB QVD file adds 1 GB to the daily peak, as both copies contribute to the total.

• Creating a 0.5 GB QVD file through transformation adds 0.5 GB to the daily peak. Only the resulting QVD file is counted; the QVD generator app isn't counted as it loads data that is already in Qlik Cloud.

Best practices for managing data

Proper data management improves performance and ensures that you get the most out of your Data for Analysis capacity. This section will show you how to handle data efficiently in Qlik Cloud.

• Create QVD files for data reuse

  When dealing with data that will be used in multiple Qlik Sense apps, consider creating QVD files. QVD files allow you to load data once and reuse it in multiple apps without adding to the daily peak. This can significantly reduce data ingestion and storage costs.

  For example, if you load 10 GB of external data and create a 5 GB QVD file, you have a total of 15 GB of data contributing to the daily peak. Loading the same data directly into two apps results in 20 GB.

  

  In general, creating QVD files for data reuse with Qlik Data Gateway - Data Movement, is more efficient than reloading data directly via apps.

• Use efficient data loading methods

  Take advantage of SQL pushdown transformations to optimize data loading. This technique involves pushing data transformations and operations directly to the data source. By filtering and transforming data at the source, you reduce the volume of data transferred and improve loading efficiency.

  For example, in this pushdown query, the WHERE clause is processed directly on the source data. Only the subset of data that meets specific criteria is transferred, reducing the amount of data loaded into memory.

  Select * from my-external-database-table where my_column = 10

  Note that in the case of loading QVD files, the WHERE clause is processed after the file is read from the source, and the entire file is counted.

• Use On-demand apps for large datasets

  On-demand apps (ODAG) are useful when dealing with large datasets. ODAG allows you to load aggregated data for the parent app and fetch more detailed data only when necessary. Users get aggregate views of big data stores allowing them to identify and load relevant subsets of the data for detailed analysis. For more information, see On-demand apps.

• Handle large datasets with Direct Query and Dynamic Views

  For large datasets, consider using Direct Query and Dynamic Views. With these features, you can query and view relevant subsets of large datasets without the need to import or load all the data into memory. While there are some limitations compared to in-memory apps, it's an efficient way to work with substantial datasets. For more information, see Direct Query apps and Managing data with dynamic views.

• Regularly clean up unused apps and data files

  To optimize resource usage and improve overall site performance, regularly identify and delete unused apps and data files. The following steps can help with the cleanup:

  • Identify unused apps and data files in Catalog by sorting and checking the Last updated, Viewed by, and Used in columns. This lets you see if the item has been opened within the last 28 days and in how many apps a data file is used. For more information, see Viewer and item usage metrics.

  • Impact analysis and Lineage help you find out where a data file is used and which data files that are used in a specific app. For more information, see Analyzing impact analysis for apps, scripts, and datasets and Analyzing lineage for apps, scripts, and datasets.

  • You can delete apps and data files from activity centers. Administrators can also delete apps from the Administration activity center.

Example: Calculating Data Moved volume

In this example we have a dataset for product categories. The dataset has 100 rows and the following columns:

There is a fixed size for each data type:

We can now calculate the estimated row size as the sum of the column sizes: 4 + 7.5 + 200 + 200 = 411.5 bytes. Multiplied by 100 rows, this gives us a total data volume of 41,150 bytes.

Full Users

Users with Full User entitlement can view, edit, and create content in apps, export charts and apps, work with data integration, automations, machine learning, and perform various other tasks, provided their user permissions and space permissions allow it. For more information, see Managing user entitlements.

Basic Users

The free Basic User entitlement is available with Qlik Cloud Analytics Premium and Qlik Cloud Enterprise subscriptions. It is intended for limited, read-only access. Basic Users cannot create or edit apps and other assets, or work with Data Integration. Granting additional permissions automatically promotes them to Full Users. For more information, see Managing user entitlements.