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Stream Type — select suitable stream type. Supported types are Kafka and GoldenGate. Provide details for the following fields on the Source Details page and click Next :.

Topic Name — the topic name that receives events you want to analyze. Select one of the mechanisms to define the shape on the Shape page:.

Infer Shape detects the shape automatically from the input data stream. You can also save the auto detected shape and use it later.

Select Existing Shape lets you choose one of the existing shapes from the drop-down list. Manual Shape populates the existing fields and also allows you to add or remove columns from the shape.

You can also update the datatype of the fields. A reference defines a read-only source of reference data to enrich a stream.

A reference currently can only refer to database tables. A reference requires a database connection. Select Reference in the Create New Item menu.

Enable Caching — select this option to enable caching for better performance at the cost of higher memory usage of the Spark applications.

Caching is supported only for single equality join condition. When you enable caching, any update to the reference table does not take effect as the data is fetched from the cache.

Provide details for the following fields on the Shape page and click Save :. When the datatype of the table data is not supported, the table columns do not have auto generated datatype.

Only the following datatypes are supported:. A dashboard is a visualization tool that helps you look at and analyze the data related to a pipeline based on various metrics like slices.

A dashboard is an analytics feature. You can create dashboards in Stream Analytics to have a quick view at the metrics.

After you have created the dashboard, it is just an empty dashboard. You need to start adding details to the dashboard.

Click the Add a new slice to the dashboard icon to see a list of existing slices. Go through the list, select one or more slices and add them to the dashboard.

Click the Specify refresh interval icon to select the refresh frequency for the dashboard. This just a client side setting and is not persisted with the Superset Version 0.

You can also edit the CSS in the live editor. Click the Save icon to save the changes you have made to the dashboard. Within the added slice, click the Explore chart icon to open the chart editor of the slice.

Click Save as to make the following changes to the dashboard:. A cube is a data structure that helps in quickly analyzing the data related to a business problem on multiple dimensions.

Select Target in the Create New Item menu. Provide details for the following fields on the Type Properties page and click Save and Next :.

Target Type — the transport type of the target. Provide details for the following fields on the Target Details page and click Next :.

This is an optional field. Batch processing — select this option to send events in batches and not one by one. Enable this option for high throughput pipelines.

Click Test connection to check if the connection has been established successfully. Testing REST targets is a heuristic process.

It uses proxy settings. Select one of the mechanisms to define the shape on the Shape page and click Save :. Creating Target from Pipeline Editor.

Alternatively, you can also create a target from the pipeline editor. When you click Create in the target stage, you are navigated to the Create Target dialog box.

Provide all the required details and complete the target creation process. When you create a target from the pipeline editor, the shape gets pre-populated with the shape from the last stage.

Geo fences are further classified into two categories: manual geo fence and database-based geo fence. Create a Manual Geo Fence.

The Geo Fence Editor opens. In this editor you can create the geo fence according to your requirement.

Within the Geo Fence Editor , Zoom In or Zoom Out to navigate to the required area using the zoom icons in the toolbar located on the top-left side of the screen.

You can also use the Marquee Zoom tool to move across locations on the map. Click the Polygon Tool and mark the area around a region to create a geo fence.

Enter a name and description, and click Save to save your changes. Update a Manual Geo Fence. Search Within a Manual Geo Fence.

You can search the geo fence based on the country and a region or address. The search field allows you search within the available list of countries.

When you click the search results tile in the left center of the geo fence and select any result, you are automatically zoomed in to that specific area.

Delete a Manual Geo Fence. Click Actions , then select Delete Item to delete the selected geo fence. Create a Database-based Geo Fence.

Click Next and select Connection. Delete a Database-based Geo Fence. Click Actions and then select Delete Item to delete the selected geo fence.

A pipeline is a Spark application where you implement your business logic. It can have multiple stages such as a query, a pattern stage, a business rule, or a query group.

Select Pipeline in the Create New Item menu. You can include simple or complex queries on the data stream without any coding to obtain refined results in the output.

When you create a group by, the live output table shows the group by column alone by default. Visualizations are graphical representation of the streaming data in a pipeline.

You can add visualizations on all stages in the pipeline. Edit Visualization. On the stage that has visualizations, click the Visualizations tab.

Identify the visualization that you want to edit and click the pencil icon next to the visualization name.

In the Edit Visualization dialog box that appears, make the changes you want. You can even change the Y Axis and X Axis selections.

When you change the Y Axis and X Axis values, you will notice a difference in the visualization as the basis on which the graph is plotted has changed.

Change Orientation. Based on the data that you have in the visualization or your requirement, you can change the orientation of the visualization.

You can toggle between horizontal and vertical orientations by clicking the Flip Chart Layout icon in the visualization canvas. Delete Visualization.

You can delete the visualization if you no longer need it in the pipeline. In the visualization canvas, click the Delete icon to delete the visualization from the pipeline.

Be careful while you delete the visualization, as it is deleted with immediate effect and there is no way to restore it once deleted.

In the live output table, right-click columns and click Hide to hide that column from the output.

To unhide the hidden columns, click Columns and then click the eye icon to make the columns visible in the output. Click the Columns link at the top of the output table to view all the columns available.

Use the arrow icons to either select or unselect individual columns or all columns. Only columns you select appear in the output table. Perform Operations on Column Headers.

Hide — hides the column from the output table. Click the Columns link and unhide the hidden columns. Remove from output — removes the column from the output table.

Click the Columns link and select the columns to be included in the output table. Function — captures the column in Expression Builder using which you can perform various operations through the in-built functions.

Add a Timestamp. Reorder the Columns. You can perform calculations on the data streaming in the pipeline using in-built functions of the Expression Builder.

Stream Analytics supports various functions. For a list of supported functions, see Expression Builder Functions.

Adding a Constant Value Column. A constant value is a simple string or number. No calculation is performed on a constant value.

Enter a constant value directly in the expression builder to add it to the live output table. Using Functions.

You can select a CQL Function from the list of available functions and select the input parameters. Click Apply to apply the function to the streaming data.

Patterns are templatized stages. You supply a few parameters for the template and a stage is generated based on the template.

A rule is a set of conditions and actions applied to a stream. A query group stage allows you to use more than one query group to process your data - a stream or a table in memory.

Different query groups process your input in parallel and the results are combined in the query group stage output.

You can also define input filters that process the incoming stream before the query group logic is applied, and result filters that are applied on the combined output of all query groups together.

A query group stage of the stream type applies processing logic to a stream. It is in essence similar to several parallel query stages grouped together for the sake of simplicity.

A query group stage of the table type can be added to a stream containing transactional semantic, such as a change data capture stream produced, to give just one example, by the Oracle Golden Gate Big Data plugin.

The stage of this type will recreate the original database table in memory using the transactional semantics contained in the stream.

You can then apply query groups to this table in memory to run real-time analytics on your transactional data without affecting the performance of your database.

These filters process data before it enters the query group stage. Hence, you can only see fields of the original incoming shape.

These filters process data before it exits the query group stage. Hence, you can see combined set of fields that get produced in the outgoing shape.

Make sure that you use the values that correspond to your change data capture dataset. The default values work for Oracle GoldenGate change data capture dataset.

You can also directly create the target from within the pipeline editor. See Create a Target for the procedure. You can also edit an existing target.

You will quickly see that, if broken down into small pieces, it is not a difficult concept to grasp at all. It is composed of pixels total.

First we divide the initial image in 64 squares of equal surface. We then iterate through each pixel of each of these surfaces, extracting the average R, G and B value from every pixel lying on the same surface.

Here is a poorly made gif that might help you understand the idea, as well as an example of the result obtained with this method.

Link to the sketch. Import the library used to stream the webcam feed and declare a Capture global variable in order to store the frames.

You also need to register a new event listener that will read the new frames incoming from the webcam.

We create a new pixelateImage function that reads a frame from the webcam, resizes it to a square so that it fits the cube, creates a new output image that will be composed following the algorithm aforementioned.

We also create a function that will allow us to display the resulting image in the rendered view for test purposes. We use the functions just created to project the output image pixel's values to the cube's first frame's voxels.

As mentioned, if enable3d is set to true , the past webcam frames are sent to the back of the cube with a delay.

Weather it be for curiosity or testing purposes, it can be useful to try different output resolutions and to toggle between the 2D and 3D view.

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We create a new pixelateImage function that reads a frame from the webcam, resizes it to a square so that it fits the cube, creates a new output image that will be composed following the algorithm aforementioned.

We also create a function that will allow us to display the resulting image in the rendered view for test purposes.

We use the functions just created to project the output image pixel's values to the cube's first frame's voxels.

As mentioned, if enable3d is set to true , the past webcam frames are sent to the back of the cube with a delay. Weather it be for curiosity or testing purposes, it can be useful to try different output resolutions and to toggle between the 2D and 3D view.

In order to do that, we register a new global variable and two event listeners which will toggle the views between the cube and the output image on a right click.

Additionally 0 and 1 can be used to decrease or increase the output image's resolution. Upload the following code to your device.

This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content. The webcam stream could easily be substituted for any video stream if need be.

Leave a Reply Cancel reply. It can have multiple stages such as a query, a pattern stage, a business rule, or a query group. Select Pipeline in the Create New Item menu.

You can include simple or complex queries on the data stream without any coding to obtain refined results in the output. When you create a group by, the live output table shows the group by column alone by default.

Visualizations are graphical representation of the streaming data in a pipeline. You can add visualizations on all stages in the pipeline.

Edit Visualization. On the stage that has visualizations, click the Visualizations tab. Identify the visualization that you want to edit and click the pencil icon next to the visualization name.

In the Edit Visualization dialog box that appears, make the changes you want. You can even change the Y Axis and X Axis selections.

When you change the Y Axis and X Axis values, you will notice a difference in the visualization as the basis on which the graph is plotted has changed.

Change Orientation. Based on the data that you have in the visualization or your requirement, you can change the orientation of the visualization.

You can toggle between horizontal and vertical orientations by clicking the Flip Chart Layout icon in the visualization canvas.

Delete Visualization. You can delete the visualization if you no longer need it in the pipeline. In the visualization canvas, click the Delete icon to delete the visualization from the pipeline.

Be careful while you delete the visualization, as it is deleted with immediate effect and there is no way to restore it once deleted.

In the live output table, right-click columns and click Hide to hide that column from the output. To unhide the hidden columns, click Columns and then click the eye icon to make the columns visible in the output.

Click the Columns link at the top of the output table to view all the columns available. Use the arrow icons to either select or unselect individual columns or all columns.

Only columns you select appear in the output table. Perform Operations on Column Headers. Hide — hides the column from the output table. Click the Columns link and unhide the hidden columns.

Remove from output — removes the column from the output table. Click the Columns link and select the columns to be included in the output table.

Function — captures the column in Expression Builder using which you can perform various operations through the in-built functions.

Add a Timestamp. Reorder the Columns. You can perform calculations on the data streaming in the pipeline using in-built functions of the Expression Builder.

Stream Analytics supports various functions. For a list of supported functions, see Expression Builder Functions. Adding a Constant Value Column.

A constant value is a simple string or number. No calculation is performed on a constant value. Enter a constant value directly in the expression builder to add it to the live output table.

Using Functions. You can select a CQL Function from the list of available functions and select the input parameters.

Click Apply to apply the function to the streaming data. Patterns are templatized stages. You supply a few parameters for the template and a stage is generated based on the template.

A rule is a set of conditions and actions applied to a stream. A query group stage allows you to use more than one query group to process your data - a stream or a table in memory.

Different query groups process your input in parallel and the results are combined in the query group stage output.

You can also define input filters that process the incoming stream before the query group logic is applied, and result filters that are applied on the combined output of all query groups together.

A query group stage of the stream type applies processing logic to a stream. It is in essence similar to several parallel query stages grouped together for the sake of simplicity.

A query group stage of the table type can be added to a stream containing transactional semantic, such as a change data capture stream produced, to give just one example, by the Oracle Golden Gate Big Data plugin.

The stage of this type will recreate the original database table in memory using the transactional semantics contained in the stream.

You can then apply query groups to this table in memory to run real-time analytics on your transactional data without affecting the performance of your database.

These filters process data before it enters the query group stage. Hence, you can only see fields of the original incoming shape. These filters process data before it exits the query group stage.

Hence, you can see combined set of fields that get produced in the outgoing shape. Make sure that you use the values that correspond to your change data capture dataset.

The default values work for Oracle GoldenGate change data capture dataset. You can also directly create the target from within the pipeline editor.

See Create a Target for the procedure. You can also edit an existing target. You must publish a pipeline to make the pipeline available for all users of Stream Analytics and send data to targets.

A published pipeline will continue to run on your Spark cluster after you exit the Pipeline Editor, unlike the draft pipelines, which are undeployed to release resources.

Topology is a graphical representation and illustration of the connected entities and the dependencies between the artifacts. The topology viewer helps you in identifying the dependencies that a selected entity has on other entities.

Understanding the dependencies helps you in being cautious while deleting or undeploying an entity. Select Show topology from the Catalog Actions menu to launch the Topology Viewer for the selected entity.

Click the Show Topology icon at the top-right corner of the editor to open the topology viewer. By default, the topology of the entity from which you launch the Topology Viewer is displayed.

The context of this topology is Immediate Family , which indicates that only the immediate dependencies and connections between the entity and other entities are shown.

You can switch the context of the topology to display the full topology of the entity from which you have launched the Topology Viewer.

The topology in an Extended Family context displays all the dependencies and connections in the topology in a hierarchical manner.

Immediate Family. Immediate Family context displays the dependencies between the selected entity and its child or parent.

The following figure illustrates how a topology looks in the Immediate Family. Extended Family. Extended Family context displays the dependencies between the entities in a full context, that is if an entity has a child entity and a parent entity, and the parent entity has other dependencies, all the dependencies are shown in the Full context.

The following figure illustrates how a topology looks in the Extended Family. Previous Next JavaScript must be enabled to correctly display this content.

About the Catalog The Catalog page is the location where resources including pipelines, streams, references, maps, connections, and targets are listed.

Create a Connection To create a connection:. Usually it is Username — the user name with which you connect to the database Password — the password you use to login to the database.

Create a Stream A stream is a source of events with a given content shape. To create a stream: Navigate to Catalog. Provide details for the following fields on the Type Properties page and click Next : Name — name of the stream Description — description of the stream Tags — tags you want to use for the stream Stream Type — select suitable stream type.

Create a Reference A reference defines a read-only source of reference data to enrich a stream. To create a reference: Navigate to Catalog.

Provide details for the following fields on the Shape page and click Save : Name — name of the database table Remember: Ensure that you do not use any of the CQL reserved words as the column names.

If you use the reserved keywords, you cannot deploy the pipeline. Create a Dashboard A dashboard is a visualization tool that helps you look at and analyze the data related to a pipeline based on various metrics like slices.

To create a dashboard:. The Create Dashboard screen appears. Editing a Dashboard To edit a dashboard: Click the required dashboard in the catalog.

The dashboard opens in the dashboard editor. Click Save as to make the following changes to the dashboard: Overwrite the current slice with a different name Add the slice to an existing dashboard Add the slice to a new dashboard.

Create a Cube A cube is a data structure that helps in quickly analyzing the data related to a business problem on multiple dimensions.

The cube feature works only when you have enabled Analytics. Verify this in System Settings. To create a cube:. Create a Target A target defines a destination for output data coming from a pipeline.

To create a target: Navigate to Catalog. Provide details for the following fields on the Type Properties page and click Save and Next : Name — name of the target Description — description of the target Tags — tags you want to use for the target Target Type — the transport type of the target.

This is a mandatory field. Select one of the mechanisms to define the shape on the Shape page and click Save : Select Existing Shape lets you choose one of the existing shapes from the drop-down list.

Creating Target from Pipeline Editor Alternatively, you can also create a target from the pipeline editor. Create a Geo Fence Geo fences are further classified into two categories: manual geo fence and database-based geo fence.

The Create Geo Fence dialog opens. Enter a suitable name for the Geo Fence. Click Save. Click the name of the geo fence you want to update.

Search Within a Manual Geo Fence You can search the geo fence based on the country and a region or address. Enter a suitable name for the geo fence.

Select Geo Fence from Database as the Type. Click Next. Select the required table to define the shape.

Create a Pipeline A pipeline is a Spark application where you implement your business logic. To create a pipeline: Navigate to Catalog.

Configure a Pipeline You can configure a pipeline to use various stages like query, pattern, rules, query group.

Add a Query Stage You can include simple or complex queries on the data stream without any coding to obtain refined results in the output.

Adding and Correlating Sources and References You can correlate sources and references in a pipeline. To add a correlating source or reference:.

Ensure that the fields you use on one correlation line are of compatible types. The fields that appear in the righ drop-down list depend on the field you select in the left drop-down list.

Adding Filters You can add filters in a pipeline to obtain more accurate streaming data. To add a filter:. Adding Summaries To add a summary:.

Adding Group Bys To add a group by:. You can add multiple group bys as well. Adding Visualizations Visualizations are graphical representation of the streaming data in a pipeline.

To add a visualization:. Updating Visualizations You can perform update operations like edit and delete on the visualizations after you add them.

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