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: The present system receives streaming raw data and inserts context into the raw data. The context raw data may be partitioned into sub-batches and transmitted to a data receiver and persister. The raw data may include context information as well as child-parent information to assist with persisting data. The context may be used to place the data in buckets without analysis of the data, thereby saving time and resources while storing the data batches.

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: The present system receives streaming raw data and inserts context into the raw data. The context raw data may be partitioned into sub-batches and transmitted to a data receiver and persister. The raw data may include context information as well as child-parent information to assist with persisting data.

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.