Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: An extract-transform-load (ETL) platform fetches consistent datasets in a batch for a given period of time and provides the ability to rollback that batch. The batch may be fetched for an interval of time, and the ETL platform may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the ETL platform fetches new data, the system will not overwrite existing data, but rather will create new versions so that change history is preserved. For any reason, if businesses would like to rollback data, they could rollback to any previous batch.

Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: An extract-transform-load (ETL) platform fetches consistent datasets in a batch for a given period of time and provides the ability to rollback that batch. The batch may be fetched for an interval of time, and the ETL platform may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the ETL platform fetches new data, the system will not overwrite existing data, but rather will create new versions so that change history is preserved. For any reason, if businesses would like to rollback data, they could rollback to any previous batch.

Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: An extract-transform-load (ETL) platform fetches consistent datasets in a batch for a given period of time and provides the ability to rollback that batch. The batch may be fetched for an interval of time, and the ETL platform may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the ETL platform fetches new data, the system will not overwrite existing data, but rather will create new versions so that change history is preserved. For any reason, if businesses would like to rollback data, they could rollback to any previous batch.

Abstract: A system fetches consistent datasets in batches for a given period of time and provides the ability to retrieve each batch. Batches of data may be fetched for an interval of time. The present system may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the system fetches new data, existing batches of data will not be overwritten. New batches of data are created as new versions so that change history is preserved. Past batches of data for a past time period may be provided to one or more tenants.

Abstract: An extract-transform-load (ETL) platform fetches consistent datasets in a batch for a given period of time and provides the ability to rollback that batch. The batch may be fetched for an interval of time, and the ETL platform may fetch new or changed data from different cloud/on-premise applications. It will store this data in the cloud or on-premise to build data history. As the ETL platform fetches new data, the system will not overwrite existing data, but rather will create new versions so that change history is preserved. For any reason, if businesses would like to rollback data, they could rollback to any previous batch.

Abstract: A system enables older cached data to be kept against the same key while adding in new sets of data in the cache that have the affected dimensional changes. Set membership functions such as intersection and difference may be used on each dimension of the data to derive the correct range for which partition the data must belong to. Each range-based, partitioned set in the cache that is against the same key is mutually exclusive with another range-based, partitioned set for the same key. With ranged-based, partitioned set, a key can be queried to find out which sets are already stored and which sets may need to be stored. This approach allows the caching to be served longer when there are queries that are only interested in subsets of the data.