Abstract: A database system receives a database language query specifying a grouping clause and one or more aggregate result values. The database system generates a pre-aggregation table based on the query. The database system generates an ingest query configured to generate data for populating the pre-aggregation table and a completion query configured to processes data stored in the pre-aggregation table. The database system periodically receives a set of input records and executes the ingest query to generate a set of pre-aggregation records. The set of pre-aggregation records are combined with the pre-aggregation table. The database system executes the completion query on the data stored in the pre-aggregation table to generate a result set and sends the generated result set to the requestor of the results.

Abstract: A system stores associates a database query with a name and stores versions of the database query. The system allows applications to execute a specific version of the database query by specifying the name of the database query and a version identifier. For example, the database query may be exposed as an endpoint of a REST API and invoked using calls to the REST API. This allows the applications to be designed without including the query definition within the code of the application. Continuous delivery of database query is performed by creating a new version of the database query that are tested using applications in a test environment. Once the new version of a database query meets test criteria, the new version of the database query is executed by applications running in production environment by sending API requests that identify the query name and the new version.

Abstract: A system stores associates a database query with a name and stores versions of the database query. The system allows applications to execute a specific version of the database query by specifying the name of the database query and a version identifier. For example, the database query may be exposed as an endpoint of a REST API and invoked using calls to the REST API. This allows the applications to be designed without including the query definition within the code of the application. Continuous delivery of database query is performed by creating a new version of the database query that are tested using applications in a test environment. Once the new version of a database query meets test criteria, the new version of the database query is executed by applications running in production environment by sending API requests that identify the query name and the new version.

Abstract: A storage tier manager efficiently creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A real-time analytical database system allows queries on data obtained from a transactional system that is frequently updated by the transactional system. The real-time analytical database system receives change logs from the transactional system. The change logs describe operations performed by the transactional system. The real-time analytical database system stores a plurality of indexes in a key-value store, each index comprising key-value pairs. A key of a key-value pair is obtained by combining field data describing a field of a document. The real-time analytical database system determines modifications to the key-value pairs of the key-value store corresponding to the operations of the transactional system as represented by the received change logs. The real-time analytical database system receives and processes queries using the plurality of indexes.

Abstract: A storage tier manager creates different versions of a dataset backup for different retention periods. Each of the versions is distinctly identifiable despite initially representing a same dataset backup. One version can be referred to as a cached version of the dataset backup and another version can be referred to as a cloud version of the dataset backup. When the retention period expires for the cached version of the dataset backup, the storage tier manager migrates the cloud version of the dataset backup from the caching storage tier to the cloud storage tier. The storage tier manager can then recover storage space occupied by data that has been migrated, as long as that data is not shared with other cached versions of other dataset backups due to deduplication.

Abstract: A storage tier manager creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A storage tier manager efficiently creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A storage tier manager creates different representations of a dataset backup for different retention periods. Each of the representations of the dataset backup is distinctly identifiable despite initially representing a same dataset backup. The representations are structured metadata corresponding to the dataset backup. One representation is a cached backup version of the dataset backup (“cached backup” or “cached representation”) provided for low latency access while residing at a storage tier of the backup appliance for a relatively short retention period according to a lifecycle management policy. The other representation is a cloud backup version of the dataset backup (“cloud backup” or “cloud representation”) provided for persisting into cloud storage for a longer retention period according to the lifecycle management policy.

Abstract: A storage tier manager creates different versions of a dataset backup for different retention periods. Each of the versions is distinctly identifiable despite initially representing a same dataset backup. One version can be referred to as a cached version of the dataset backup and another version can be referred to as a cloud version of the dataset backup. When the retention period expires for the cached version of the dataset backup, the storage tier manager migrates the cloud version of the dataset backup from the caching storage tier to the cloud storage tier. The storage tier manager can then recover storage space occupied by data that has been migrated, as long as that data is not shared with other cached versions of other dataset backups due to deduplication.