Abstract: Methods and apparatus are presented for extracting, transforming, and loading data from one database to another database. For example, an extraction, transformation, and loading (ETL) component may access an operational log of a given database in order to detect an update to the database. Upon detecting the update, the ETL component may extract a subset of data from the operational log, where the extraction of the subset of data is based on one or more rules. Ones the subset of data has been extracted, the ETL component may transform the extracted subset of data from the operational log into a format for another, target database, where the data format for the other, target database is different from a data format for the given, source database. The ETL component may then load the subset of data transformed into the data format for the other, target database into the target database.

Abstract: Methods and apparatus are presented for extracting, transforming, and loading data from one database to another database. For example, an extraction, transformation, and loading (ETL) component may access an operational log of a given database in order to detect an update to the database. Upon detecting the update, the ETL component may extract a subset of data from the operational log, where the extraction of the subset of data is based on one or more rules. Ones the subset of data has been extracted, the ETL component may transform the extracted subset of data from the operational log into a format for another, target database, where the data format for the other, target database is different from a data format for the given, source database. The ETL component may then load the subset of data transformed into the data format for the other, target database into the target database.

Abstract: Analytics data for a network-based site may be compressed according to recurring time periods. An analytics service may obtain analytics data for network-based sites to compress into a compressed analytics data stream. To compress the analytics data, the analytic service may identify a particular time period corresponding to each analytic data value and may add the analytic data value to the compressed analytics data stream as either a baseline object for the particular time period or a difference object relative to an existing baseline object for the particular time period. These objects may be interleaved according to a time-based ordering of multiple different recurring time periods. An analytic service may send the compressed analytics data stream to an analytics client. The analytics client may decompress a portion of the compressed analytics trend without decompressing the remaining portions of the compressed analytics data stream.

Abstract: Methods and apparatus are presented for extracting, transforming, and loading data from one database to another database. For example, an extraction, transformation, and loading (ETL) component may access an operational log of a given database in order to detect an update to the database. Upon detecting the update, the ETL component may extract a subset of data from the operational log, where the extraction of the subset of data is based on one or more rules. Once the subset of data has been extracted, the ETL component may transform the extracted subset of data from the operational log into a format for another, target database, where the data format for the other, target database is different from a data format for the given, source database. The ETL component may then load the subset of data transformed into the data format for the other, target database into the target database.

Abstract: Methods and apparatus are presented for aggregating data from disparate sources into an efficiently accessible format. For example, an aggregation tool may receive attribute-based data from one source and metrics-based data from another source. Given this data, the aggregation tool may store attribute data from the attribute-based data into a data object, where the data object includes multiple time slots corresponding to defined time ranges. The aggregation tool may then determine from the metrics-based data, respective metrics data for each of the multiple time slots of the data object, where each time slot is associated with the attribute data. The aggregation tool may store the respective metrics data into each of the multiple time slots of the data object. In this way, the data object may serve to efficiently provide an answer to a query requiring data from multiple data sources.

Abstract: Methods and apparatus are presented for aggregating data from disparate sources into an efficiently accessible format. For example, an aggregation tool may receive attribute-based data from one source and metrics-based data from another source. Given this data, the aggregation tool may store attribute data from the attribute-based data into a data object, where the data object includes multiple time slots corresponding to defined time ranges. The aggregation tool may then determine from the metrics-based data, respective metrics data for each of the multiple time slots of the data object, where each time slot is associated with the attribute data. The aggregation tool may store the respective metrics data into each of the multiple time slots of the data object. In this way, the data object may serve to efficiently provide an answer to a query requiring data from multiple data sources.

Abstract: Methods and apparatus are presented for aggregating data from disparate sources into an efficiently accessible format. For example, an aggregation tool may receive attribute-based data from one source and metrics-based data from another source. Given this data, the aggregation tool may store attribute data from the attribute-based data into a data object, where the data object includes multiple time slots corresponding to defined time ranges. The aggregation tool may then determine from the metrics-based data, respective metrics data for each of the multiple time slots of the data object, where each time slot is associated with the attribute data. The aggregation tool may store the respective metrics data into each of the multiple time slots of the data object. In this way, the data object may serve to efficiently provide an answer to a query requiring data from multiple data sources.

Abstract: Methods and apparatus are presented for extracting, transforming, and loading data from one database to another database. For example, an extraction, transformation, and loading (ETL) component may access an operational log of a given database in order to detect an update to the database. Upon detecting the update, the ETL component may extract a subset of data from the operational log, where the extraction of the subset of data is based on one or more rules. Once the subset of data has been extracted, the ETL component may transform the extracted subset of data from the operational log into a format for another, target database, where the data format for the other, target database is different from a data format for the given, source database. The ETL component may then load the subset of data transformed into the data format for the other, target database into the target database.

Abstract: Analytics data for a network-based site may be compressed according to recurring time periods. An analytics service may obtain analytics data for network-based sites to compress into a compressed analytics data stream. To compress the analytics data, the analytic service may identify a particular time period corresponding to each analytic data value and may add the analytic data value to the compressed analytics data stream as either a baseline object for the particular time period or a difference object relative to an existing baseline object for the particular time period. These objects may be interleaved according to a time-based ordering of multiple different recurring time periods. An analytic service may send the compressed analytics data stream to an analytics client. The analytics client may decompress a portion of the compressed analytics trend without decompressing the remaining portions of the compressed analytics data stream.

Abstract: A server receives a query specifying filter criteria from a client. The server obtains the object identifiers (IDs) for the data objects satisfying the query from one or more object identifier caches. The data objects are retrieved from one or more data sources using lookups based on object identifiers (IDs) obtained from the one or more object identifier (ID) caches. The retrieved data objects are returned to the client in response to the query.