Abstract: The present disclosure describes systems, methods, and computer program products for redacting sensitive data within a database. An example method can include sending, to a database, a data query accessing a column of the database, a masking policy identifying a first category of sensitive data and a second category of sensitive data, and in response to the data query, receiving redacted data, wherein the first category of sensitive data is redacted from a first location of the column by a first redaction operation and the second category of sensitive data is redacted from a second location of the column by a second redaction operation.

Abstract: Described herein are techniques for data quality monitoring in a network-based data system. A data metric function used to evaluate data quality can be stored, where the data metric function is defined as schema level object. The data metric function can be attached to a table associated with an account and is evaluated on data associates with the table to generate evaluation results. The evaluation results can be stored in an account-specific central database, from which access is provided to the evaluation results to a user for the account.

Abstract: The present disclosure describes systems, methods, and computer program products for redacting sensitive data within a database. An example method can include sending, to a database, a data query accessing a column of the database, a masking policy identifying a first category of sensitive data and a second category of sensitive data, and in response to the data query, receiving redacted data, wherein the first category of sensitive data is redacted from a first location of the column by a first redaction operation and the second category of sensitive data is redacted from a second location of the column by a second redaction operation.

Abstract: Systems and methods for classifying columns using a model are provided. The systems and methods access a table associated with a column of features and retrieve a list of categories each associated with a different scoring model. The systems and methods, for each category in the list of categories, apply a respective scoring model to the features of the column to generate a respective set of confidence values indicating a likelihood that the column belongs to a respective one of the categories. The systems and methods process the respective sets of confidence values to select a target category from the list of categories and associate the selected target category with the column.

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include receiving a masking policy for a column of a database, the masking policy identifying a category of sensitive data, examining a column of a database to identify a category of sensitive data in a first location of the column, and, in response to a data query accessing the column, the first location of the column exceeding a threshold probability of comprising sensitive data, executing a redaction operation to redact the category of sensitive data from the first location of the column to generate redacted data for a response to the data query.

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for redacting sensitive data within a database. An example method can include examining a first column of a plurality of columns of a database to identify a first category of sensitive data in a first location of the first column and a second category of sensitive data in a second location of the first column, receiving a masking policy for the first column, the masking policy identifying the first category of sensitive data, and, in response to a data query accessing the first column, executing a redaction operation to redact the first category of sensitive data from the first location of the first column to generate redacted data for a response to the data query.

Abstract: Systems, methods, and computer media for efficiently processing user log data are provided. The log data is progressively processed in variable sized windows based on a specified time period. The log data may be anonymized to protect user privacy. A log server processes the windowed log data in phases. The first phase includes fast data like page view log data. Subsequent phases include slow data like session data which may build on the page view data processed in the first phase. The log server identifies metrics based on the log data processed at each phase. Based on the identified metrics, the log server may identify interests across a community of users or for specific users.