Abstract: Data is validated as it travels through the different nodes of a data pipeline. Instead of having to wait to validate the data when the data reaches an end of the data pipeline, each node in the pipeline may validate the data. Different methods may be used to validate the data. For example, each node may determine metadata about the received data and/or the transformed data. This metadata may be used to determine if the node is receiving the same amount of data as it usually receives, whether the data is in a same format, and the like. A timing of the data through one or more of the nodes may also be used in determining when the data is valid. When a problem is detected at any of the nodes in the pipeline, a report may be sent to one or more users.

Abstract: Anomalous activity is detected using event information that is received from accounts from within an online service. Generally, anomalous activity is detected by comparing a baseline profile that includes past event information for accounts of the online service with a recent profile that includes recent event information for the accounts. Anomalous activity is detected when the recent profile shows that one or more events are occurring more frequently as compared to the occurrence of the event the associated baseline profile. The events that are recorded and used in the anomaly detection may include all or a portion of events that are monitored by the online service. One or more reports may also be automatically generated and provided to one or more users to show activity that may be considered anomalous activity.

Abstract: Anomalous activity is detected using event information that is received from accounts from within an online service. Generally, anomalous activity is detected by comparing a baseline profile that includes past event information for accounts of the online service with a recent profile that includes recent event information for the accounts. Anomalous activity is detected when the recent profile shows that one or more events are occurring more frequently as compared to the occurrence of the event the associated baseline profile. The events that are recorded and used in the anomaly detection may include all or a portion of events that are monitored by the online service. One or more reports may also be automatically generated and provided to one or more users to show activity that may be considered anomalous activity.

Abstract: Data is validated as it travels through the different nodes of a data pipeline. Instead of having to wait to validate the data when the data reaches an end of the data pipeline, each node in the pipeline may validate the data. Different methods may be used to validate the data. For example, each node may determine metadata about the received data and/or the transformed data. This metadata may be used to determine if the node is receiving the same amount of data as it usually receives, whether the data is in a same format, and the like. A timing of the data through one or more of the nodes may also be used in determining when the data is valid. When a problem is detected at any of the nodes in the pipeline, a report may be sent to one or more users.

Abstract: Anomalous activity is detected using event information that is received from accounts from within an online service. Generally, anomalous activity is detected by comparing a baseline profile that includes past event information for accounts of the online service with a recent profile that includes recent event information for the accounts. Anomalous activity is detected when the recent profile shows that one or more events are occurring more frequently as compared to the occurrence of the event the associated baseline profile. The events that are recorded and used in the anomaly detection may include all or a portion of events that are monitored by the online service. One or more reports may also be automatically generated and provided to one or more users to show activity that may be considered anomalous activity.