Get Data - Object Storage

The purpose of this walkthrough is to demonstrate how to ingest data from object storage into a database.

We have provided a weather dataset, hosted on each of the major cloud providers, for use in this walkthrough.

No kdb+ knowledge required

No prior experience with q/kdb+ is required to build this pipeline.

Before you import data, ensure the insights-demo database is created, as described here.

Once pipeline is created, you can the following activities on the pipeline - Review - Save - Deploy

Import Data

The import process creates a Pipeline which is a collection of nodes:

• Readers - read data from its source
• Decoders - define the type of data imported and
• Schema - converts data to a type compatible with a kdb+ database
• Writers - write the data to a kdb Insights Enterprise database

Open the import wizard by selecting 2. Import from the Overview page, as shown below.

Next, you are prompted to select a reader node.

Select a Reader

A reader node stores details of data to import, including any required authentication. Select a cloud providers from one of the following tabs, for settings specific to the reader for each: Google, Microsoft, and AWS.

1. Select from one of the Cloud providers listed:

   

2. Complete the reader properties for the selected cloud provider.

   Google Cloud Storage Microsoft Azure Storage AWS S3

   Properties

   setting	value
   GS URI*	gs://kxevg/weather/temp.csv
   Project ID	kx-evangelism
   Tenant	Not applicable
   File Mode*	Binary
   Offset*	0
   Chunking*	Auto
   Chunk Size*	1MB
   Use Watching	No
   Use Authentication	No

   Properties

   setting	value
   MS URI*	ms://kxevg/temp.csv
   Account*	kxevg
   Tenant	Not applicable
   File Mode*	Binary
   Offset*	0
   Chunking*	Auto
   Chunk Size*	1MB
   Use Watching	Unchecked
   Use Authentication	Unchecked

   Properties

   setting	value
   S3 URI*	s3://kx-ft-public-share/temp.csv
   Region*	us-east-1
   File Mode*	Binary
   Tenant	kxinsights
   Offset*	0
   Chunking*	Auto
   Chunk Size	1MB
   Use Watching	No
   Use Authentication	No

3. Click Next to select a decoder.

Select a Decoder

In this step you select a Decoder node which defines the type of data imported.

1. Select CSV, as shown below, as the weather data is a csv file.

   

2. In the Configure CSV screen keep the default CSV decoder settings.

   

3. Click Next to open the Configure Schema screen.

Configure the Schema

Next, you must configure the schema, which converts data to a type compatible with a kdb+ database. Every imported data table requires a schema; and every data table must have a timestamp key to be compatible with kdb's time series columnar database. The insights-demo has a predefined schema for weather data.

1. Complete the Configure Schema properties as follows:

   setting	value
   Apply a Schema	Enabled
   Data Format	Any
   Schema	Enter each column and its desired type, as described in the next step.

2. Click Load Schema and select the following values

3. Database - Select insights-demo, that is the database you created earlier.

4. Table - Select the weather table.

   

5. Click Load.

6. Click Next to open the Configure Writer screen.

Configure the Writer

Finally, you must add a Writer, which writes transformed data to the kdb Insights Enterprise database.

1. Configure the writer settings as follows:

   setting	value
   Database	insights-demo
   Table	weather
   Write Direct to HDB	Unchecked (Default)
   Deduplicate Stream	Checked (Default)
   Set Timeout Value	Unchecked (Default)

2. Click Open Pipeline to review the pipeline in the pipeline viewer.

Review Pipeline

You can now review the Pipeline that you created to read in, transform and write the weather data to your insights-demo database. This is shown below.

At this stage you are ready to save the pipeline.

Save the Pipeline

Now you can save your Pipeline and then perform a test deploy.

1. Enter a unique name in the top left of the workspace. For example, weather-1.

2. Click Save.

   

3. The weather-1 pipeline is available under Pipelines in the left-hand menu.

   

Test Deploy

Before you deploy your pipeline, you can run a test deploy that previews your pipeline prior to deployment. The test deploy returns a picture of the data at each step along the pipeline, but does not write the data to the database.

1. Click Quick Test. Note that it may take several minutes to run.

A message is displayed at the bottom of the screen to show that the test deploy has been successful.

1. Select a Node in the pipeline and choose the Data Preview tab, in the lower part of the screen, to view the data output from the step.

Deploy the Pipeline

You are now ready to deploy your pipeline. Deploying the pipeline reads the data from its source, transforms it to a kdb+ compatible format, and writes it to a kdb Insights Enterprise database.

1. Click on Save & Deploy, in the top panel, as shown below.

   

2. Check the progress of the pipeline under the Running Pipelines panel of the Overview tab. The data is ready to query when Status equals Finished. Note it may take several minutes for the pipeline to reach a running state.

Pipeline warnings

After the pipeline starts running, you might see some warnings in the Running Pipelines panel under the Overview tab. These warnings are normal and can be safely ignored.

Pipeline Teardown

Once the CSV file has been ingested, the weather pipeline can be torn down. Ingesting this data is a batch ingest operation, rather than an ongoing stream, so it is ok to teardown the pipeline once the data is ingested. Tearing down a pipeline returns resources, so is a good practice when it is no longer needed.

1. Click X in Running Pipelines on the Overview tab to teardown a pipeline.

   

2. Check Clean up resources after teardown as these are no longer required now that the CSV file has been ingested.

   

Troubleshoot Pipelines

If any errors are reported they can be checked against the logs of the deployment process. Click View diagnostics in the Running Pipelines section of the Overview tab to review the status of a deployment.

Next Steps

Now that data has been ingested into the weather table you can:

• Add data from Kafka
• Query the data
• Build a visualization from the data

Further Reading

Use the following links to learn more about specific topics mentioned in this page:

• Import wizard
• Pipelines
• Transform nodes
• Decoder nodes
• Reader nodes
• Writer nodes