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This features allows the user to export desired data from a specific DATAmaestro Lake folder to any pre-existing DATAmaestro project A .dmff file is created with the desired subset of data (period, frequency, variables) stored in the DATAlake and uploaded (export) it to a specific DATAmaestro project or onto the user’s local drive.

Remark: Data from different DATAlake directories can be merged into a single export .dmff file and thus be used simultaneously for analysis in a DATAmaestro project.

To export :

Click Export in the main menu.
Select From, set date followed by hour and minutes.
Select To, set date followed by hour and minutes. For each time step the window changes proposing new choices.
Select Var. Naming to choose the tag name. Options: Tag Name Only, Subpath [sub/tag], Full path [/project/sub/tag]. Examples: Gas flow, Chemicals/Gas flow or /demo/Chemicals/Gas flow.
In Format, select DMFF, CSV or numpy files.
Select Sample time based on to define when the data is sampled. It defines the output timestamps.
- Fixed interval: data is sampled periodically. E.g. every hour, every minute. The time interval between 2 records is defined at the Sampling interval. The Sampling interval is constant.
- Reference tag time: data is sampled at the timestamps of one tag called the Reference tag. The Sampling interval is variable.
- Time from all tags: data is sampled at the timestamps of all sampled tags. The sampling interval is variable. It was previously known as Raw.
Enter Sampling Interval, it defines desired data sampling frequency or data intervals, for example, a database which should have one value every hour should enter “1h” in this field. Other common period examples include:
1. If you select Fixed interval,enter a Sampling interval.
2. If you select Reference tag time, you can select the reference tag time at the list.
3. If you select Time from all tags, data is sampled at the timestamps of all sampled tags, therefore, the Sampling Interval is not displayed.
  
  Range (Shortcuts)
  Description
  1s
  Extract data at 1 second intervals
  5m
  Extract data at 5 minute intervals
  1h
  Extract data at 1 hour intervals
  7d
  Extract data at 7 day intervals

Select Sampling method, options: Average, Interpolation, Longest record, Minimum and Maximum, Last and First values. Snap method is obsolete.

Method	Description	Method type
Average	The average method calculates the weighted arithmetic mean which is based on the value and the duration of each value that spans the given period or time window. For more information on Method average please check below. The formula is: which expands to where w_i are the weights (or durations) and x_i are the values. ^{(1) Source: Average Wikipedia} Use Average for numerical variables like pressures, temperatures and flows.	Forward
Interpolation	Linear interpolation is a type of interpolation which generates new values based on an existing set of values. It is achieved geometrically by drawing a straight line between two adjacent points. If the known points are given by the coordinates (x₀,y₀) and (x₁,y₁) , the linear interpolant is a line between these points. For a value x in the interval (x₀,x₁), the value of y along the straight line is given from the equation of slopes (1). It is useful for continuous values. Moreover the interpolation method is only calculated if there is no recorded value at a time stamp. ^{(1) Source: Wikipedia - Linear Interpolation} Use Interpolate for numerical variables like pressures, temperatures and flows.	Centered
Longest record	Selects the single recorded value or class that has the longest duration for each Period or time window. If the same value or symbol appears twice, the durations are not accumulated. We often use “Longest Record” for symbolic (or numerical) variables like product names or machine states, where it doesn’t make sense to interpolate or average those variables.	Forward
Minimum	Selects the lowest/minimum recorded value for each Period or time window. If there is no record in the time window, last value is returned.	Forward
Maximum	Selects the highest/maximum recorded value for each Period or time window. If there is no record in the time window, last value is returned. You may choose to work with “Min” or “Max” if you are searching for particular events in your data. For example, if you want to know if a valve opened (0 or 1) even once during a time window, you could use “Max”.	Forward
Last	Selects the last recorded value at or before a given time. You may choose “Last” for a lab product quality measurement, given that the last known value for quality was this last lab measurement. You may also choose “Last” for any variables that are measured “on-change” where the last known value is true until a new value is recorded.	Backward
First	Selects the first recorded value at or after a given time.	Forward
Snap (Obsolete)	The snap methods samples by taking the last stored value within each time interval. In case there are no stored values within a given interval the snap method considers the last value stored. An instantaneous value at each time interval will be extracted, no aggregation is provided.	Forward

Time from all tags

Note: If you choose as Sampling time based on as Time from all tags data is sampled at the timestamps of all sampled tags. The sampling interval is variable. In this case, so far, the only Method available is Last.

To select the desired list of variables, firstly choose the Folder where the tags are stored.
Select Tags from the tags’ lists and click the Right arrow to add them to the list for export. To remove a tag from the export list select the Tag and click the Left Arrow.
Repeat steps 7 and 8 to select tags from multiple directories.
Click Download, this downloads a DMFF, CSV or numpy files to the user’s local drive.

		Users		Advanced users
	Sampling time	Limit	Help	Limit
Download csv	Fixed interval	max 128 columns	text before download && button disabled
	Fixed interval	tag names : max 200 kB*		tag names : max 200 kB
	Time from all tags	max 128 columns	text before download && button disabled		popup : file may be very large
	Time from all tags	tag names : max 200 kB*		tag names : max 200 kB
	Reference tag	max 128 columns	text before download && button disabled
Download DMFF	Sampled	tag names : max 200 kB		tag names : max 200 kB
	Time from all tags	max 128 columns	text before download && button disabled
		tag names : max 200 kB*		tag names : max 200 kB
		file max 100 MB	popup before download	file max 100 MB	popup before download
	Reference tag	tag names : max 200 kB		tag names : max 200 kB
Export DMFF	Fixed interval
	Time from all tags	max 128 columns	text before download && button disabled
	Time from all tags	file max 100 MB	popup if error occurs	file max 100 MB	popup if error occurs
	Reference tag
Tags Extract (analytics)	Fixed interval
	Time from all tags	max 128 columns	message if error occurs	max 128 columns	message if error occurs
	Time from all tags	file max 100 MB	message if error occurs	max 128 columns	message if error occurs
	Reference tag

*: very unlikely to happen

Convert unix time to excel time

To convert the timestamp in unix time (ms) to excel time, use this formula:

=((UNIXTIME/60/1000)/60/24)+DATE(1970;1;1)+(2/24) for GMT +02:00

To export a file directly to DATAmaestro Analytics:

Choose the DATAmaestro Folder.
Choose a DATAmaestro Project.
Click Export to DATAmaestro.
1. Note: Pop-ups must be enabled for this website. If pop-ups are blocked, enable pop-ups and then click Export to DATAmaestro.
DATAmaestro Analytics will open in a new Tab/Window. Under Variables Definition, select “Retrieve” and then click “Save” to complete the export to the DATAmaestro Analytics project .
1. Note: For large data sets there may be a delay between clicking export and the project opening in DATAmaestro Analytics.

		User	Advanced user
	Method	Limit	Limit
Export DMFF	Sampled
	Raw	max 128 columns
	Raw	file max 100 MB	file max 100 MB
Tags Extract (Analytics)	Sampled
	Raw	max 128 columns	max 128 columns
	Raw	file max 100 MB	file max 100 MB

Export Method: Average

The average method calculates the weighted arithmetic mean which is based on the value and the duration of each value that spans the given period or time window.

The table below shows how the average exported data is calculated.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Average Values	(Weighted) Average Formula
		01/10/2019 15:37:00	30	=(2530+3625)/55
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	33.167	=(365+4230+22*25)/60
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	24.167	=(225+2330+26*25)/60
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	25.917	=(265+2530+27*25)/60
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	27.083	=(275+2830+26*25)/60
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: Interpolation

Linear interpolation is a type of interpolation which generates new values based on an existing set of values. It is achieved geometrically by drawing a straight line between two adjacent points. It is useful for continuous values. Moreover the interpolation method is only calculated if there is no recorded value at a time stamp.

(1) More information in Wikipedia - Linear Interpolation

The table below shows how the interpolation exported data is calculated.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Interpolated values
		01/10/2019 15:37:00	-
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	41*
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	22.833
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	25.166
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	27.833
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

*At 15:38:00, the interpolation method considers the previous value 36 at 15:37:35 and the next value 42 at 15:38:05. The new interpolated value is 41, calculated as follows: 36 + ((42-36)/30)*25.

Export Method: Longest record

Selects the single recorded value or class that has the longest duration for each Period or time window. If the same value or symbol appears twice, the durations are not accumulated.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Longest record values
		01/10/2019 15:37:00	25
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	42
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	23
01/10/2019 15:39:05	23
01/10/2019 15:39:05	26
		01/10/2019 15:40:00	25
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	28
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: Minimum

Selects the lowest/minimum recorded value for each Period or time window. If there is no record in the time window, last value is returned.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Minimum Values
		01/10/2019 15:37:00	25
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	22
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	22
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	25
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	26
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: Maximum

Selects the highest/maximum recorded value for each Period or time window. If there is no record in the time window, last value is returned.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Maximum values
		01/10/2019 15:37:00	36
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	42
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	26
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	27
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	28
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: Last

Selects the last recorded value at or before a given time.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Last values
		01/10/2019 15:37:00	-
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	36
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	22
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	26
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	27
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: First

Selects the first recorded value at or after a given time.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	First values
		01/10/2019 15:37:00	25
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	42
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	23
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	25
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	28
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Export Method: Snap

The snap methods samples by taking the last stored value within each time interval. In case there are no stored values within a given interval the snap method considers the last value stored. An instantaneous value at each time interval will be extracted, no aggregation is provided.

This method is only available for backward compatibility. It is only useful if a previous extraction have been done with Snap method and the same result is needed. In other cases, Last and First methods should fill the needs and are simpler.

The table below shows how the snap exported data is selected.

Raw Data		Exported Data
DateTime (dd/MM/yyyy HH:mm:ss)	Raw values	DateTime (dd/MM/yyyy HH:mm:ss)	Snap values
		01/10/2019 15:37:00	36
01/10/2019 15:37:05	25
01/10/2019 15:37:35	36
		01/10/2019 15:38:00	22
01/10/2019 15:38:05	42
01/10/2019 15:38:35	22
		01/10/2019 15:39:00	26
01/10/2019 15:39:05	23
01/10/2019 15:39:35	26
		01/10/2019 15:40:00	27
01/10/2019 15:40:05	25
01/10/2019 15:40:35	27
		01/10/2019 15:41:00	26
01/10/2019 15:41:05	28
01/10/2019 15:41:35	26

Combine different sampling methods

Did you know you can combine different sampling methods? Change them by clicking here or above before selecting the variables from the left.

Update data extraction

Now, every time you export data from Lake to Analytics you have at your disposal a Tag Extract task automatically created in your Analytic's project. This task will allow you to modify the extraction without having to redo everything in the Lake Export.

Go to DM Lake, then Export and fill all the information, you must go through all the steps presented previously.
Select the new tags.
Choose the Folder and Project where you want to export the data.
Click on Export to DATAmaestro.
You are directed to the selected project in DM Analytics. Now you have two options:
1. To replace the existing datasource (all tasks will be automatically updated): Click on Load data in without changing the selected file in the drop down (Recommended).
2. To add a new datasource to your project. It is possible to work with multiple datasources in a project, however each task can only use one datasource (no merging provided).

To replace the existing datasource (all tasks will be automatically updated): in DATAmaestro Analytics, click on Load data in without changing the selected file in the drop down (Recommended).

More than one data sources can complexify your project. Before creating a task, first you must select the data source. So, it is advisable to have only one data source in a project.

Go to data sources icon.
Click on Edit of the Cement_Tutorial_XXX DMFF file.
Go to Parameters tab.
Click on the folder icon.

Search and select the file you have just exported.
Click Close.
In Parameters, click on Retrieve to retrieve you tags to the project.
Click on Save.
You have now updated your existing datasource.

To add a new datasource to your project. It is possible to work with multiple datasources in a project, however each task can only use one datasource (no merging provided).

Once your export has finished and you are redirected to DATAmaestro Analytics, click on Retrieve to retrieve your tags in the project.
Click on Save.

You have now added a second datasource to your project. Under the datasource icon, you will see two datasources.

Warning

Note on DMFF file the will replace an existant DMFF both with the same name. This means that if the same file is used in two different Analytics projects (for example, if you create a copy of the project) and if the DMFF is updated in only one project, but both projects have the same file name, the DMFF file will be automatically updated in the other project too.

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