Data within Datasets is stored in one or more batches. Each batch has an ID, date, timestamp and row count.

When creating or editing a Dataset, choose whether it will contain one or more batches. Choose one of the following:

• Overwrite (default) to create a single-batch Dataset. New data loaded into this Dataset will overwrite any existing data. The latest data will be the only data available, meaning you're using the freshest data.
• Add to create a multi-batch Dataset. New data loaded into this Dataset will be added/appended to existing data. Older batches of data will remain, so this is useful for historical data accumulated over time, such as weekly sales metrics or daily validation results.

A multi-batch Dataset allows you track data vintage and trend changes over time. It also allows older data to be automatically deleted.

Selecting and viewing batches

You can view the batch details within a Dataset from the Datasets list screen using Actions > Show batches.

The Source step in a Workflow provides an option to select All batches (default), last N batches, Batches created in the latest time period or to take a sample batch over a time period.

Deleting batches

To delete manually, go to the batch details within a Dataset in the Datasets list using Actions > Show batches. Select one or more batches then Delete and confirm deletion.

Datasets also have an Allow automatic batch deletion setting. Checking this provides the Delete batches on completion option in the Source step using this Dataset. When the Workflow is executed, it can be set to automatically delete any batches of data that are processed successfully, which will ensure that no batch of data is processed through a Workflow twice.

Automatic batch deletion

Each multi-batch Dataset has an optional limit on the number of batches it can have. When new data is loaded into that Dataset, if the specified number (2-999) of batches would exceed the limit, the oldest batch will be automatically deleted. If this setting is left blank, no automatic deletion of batches will occur.

The metadata for fixed width files (.dat or .txt) is loaded in a .csv file. The following columns are accepted in this csv file. The order of columns do not matter, as long as the headers are labelled correctly.

Data Studio will analyze the file and automatically determine the best parser options to extract the data from the file, but these can be manually amended.

File parser is auto-selected based on the file extension, but a different parser can be selected from the list.

Character set is auto-selected by comparing first 64k characters against a universal library.

Language and region is auto-selected to match the host machine OS locale, unless overridden in the Data Studio installation config files.
The setting controls how date and numeric values are parsed on load (if automatically detecting data types). For example:

• Date: 'English (United Kingdom)' will parse the value "01/02/2020" as 1st February 2020, 'English (United States)' will parse the same value as 2nd January 2020
• Numeric: 'English (United Kingdom)' will parse the value "1,001" as the number 1001, 'German' will parse the same value as "1.001"

Delimiter and Quote character are auto-selected from the first 20 rows of data, but can be selected from the list or a custom value entered.

Import data from row can be used with .csv, .psv, and .txt delimited files to specify a number of rows to skip.

Use first row for column names - is selected by default, unselecting will name columns 'Column 1, Column 2, etc.'

Parse newline characters within quoted sections as new rows - always terminate row at an end of line character. By default newline characters within Quote characters, i.e. paragraphs of text, will be incorporated into the cell data.

Column datatype options:

• Automatically detect the type of data for each column - sets the datatype of each column based on the first 20 rows. This impacts how data is parsed later on using the Language and region setting. Examples: a Numeric column will automatically convert "0123" to "123" and a whitespace will be replaced with null.
• Treat all columns as alphanumeric - will set Alphanumeric for all columns.
• Allow columns to support mixed datatypes - will set Unknown datatype for all columns.

Date only

Date and time

Time only

Some database systems and file formats can store time zone-aware date and time values.

In the following CSV example, row 1 is a timestamp with a timezone +3 hrs. Row 2 is in UTC. Data Studio is not timezone aware, and when timestamps are loaded into Data Studio, values are converted into UTC.

A watershed (also known as a century cutoff) is used to calculate the century that any two digit year is in. The default is a floating watershed of 50 years. This can be configured in Settings > Data handling with the option to use a fixed or floating watershed with configurable values for both.

• A fixed watershed will interpret any year after the given value to be in the last century.
• A floating watershed is similar to the fixed one but it's relative to the current year. The given value is added to the current year and any two digit years above will be considered to be in the last century. For example, if the floating watershed value is 10 and the current year is 2022, the watershed year will be 10 + 22, so any years after 32 will be converted to 19XX and years below it will be converted to 20XX. This is useful when parsing older dates such as date of birth.

To reduce disk space usage (by up to 80%) Data Studio will automatically compress your data as it's being loaded in, but compression may impact Workflow performance. The default compression applied can be updated in Settings > Performance. Each Dataset can have a different compression-level from the default. Any change of compression will be applied to data loaded from that point onwards.

• Column-based compression is best for fast iterations of a single column, such as data validation or transformation where a Workflow is processing every value in a column.
• Row-based compression often takes up least disk space and can be faster to compress the data. It creates a file that indexes each row, making it quick to access individual cells within the row.
• No compression – the Dataset, including the data, will take up more disk space than the size of the file being loaded. However, loading data and processing Workflows will be fastest, so this is the best option for processing data in Data Studio without storing it long-term.