# Data collection
<a name="data-collection"></a>

 Important steps in the ML lifecycle are to identify the data needed, followed by the evaluation of the various means available for collecting that data to train your model. 

![\[Figure showing the main components of data collection.\]](http://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/images/data-collection-main-components.png)

+  **Label:** *Labeled data* is a group of samples that have been tagged with one or more labels. If labels are missing, then some effort is required to label it (either manual or automated). 
+  **Ingest and aggregate:** Data collection includes ingesting and aggregating data from multiple data sources. 

![\[Figure showing how data sources lead to data ingestion means and then into data technologies.\]](http://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/images/data-sources-ingestion-technologies.png)


 The sub-components of the *ingest and aggregate* component (shown in Figure 8) are as follows: 
+  **Data sources:** Data sources include time-series, events, sensors, IoT devices, and social networks, depending on the nature of the use case. You can enrich your data sources by using the geospatial capability of Amazon SageMaker AI to access a range of geospatial data sources from AWS (for example, Amazon Location Service), open-source datasets (for example, [Open Data on AWS](https://aws.amazon.com/opendata/)), or your own proprietary data including from third-party providers (such as Planet Labs). To learn more about the geospatial capability in Amazon SageMaker AI, visit [Geospatial ML with Amazon SageMaker AI](https://aws.amazon.com/sagemaker/geospatial/). 
+  **Data ingestion:** Data ingestion processes and technologies capture and store data on storage media. Data ingestion can occur in real-time using streaming technologies or historical mode using batch technologies. 
+  **Data technologies:** Data storage technologies vary from transactional (SQL) databases, to data lakes and data warehouses to form a lake house with marketplace governance across teams and partners. Extract, transform, and load (ETL) pipeline technology automates and orchestrates the data movement and transformations across cloud services and resources. A lake house enables storing and analyzing structured and unstructured data.