Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: The subject technology receives a command to commit a table in a different table format on an external volume. The subject technology generates a first snapshot of the table on internal storage. The subject technology generates a first list of metadata files on the internal storage. The subject technology generates, based on the first list of metadata files, a first set of metadata files on the internal storage. The subject technology generates a second snapshot of the table on the external volume. The subject technology generates a second list of metadata files on the external volume. The subject technology generates, based on the second list of metadata files, a second set of metadata files on the external volume. The subject technology generates a first set of data files in a different file format on the external volume.

Abstract: The subject technology receives a command to commit a table in a different table format on an external volume. The subject technology generates a first snapshot of the table on internal storage. The subject technology generates a first list of metadata files on the internal storage. The subject technology generates, based on the first list of metadata files, a first set of metadata files on the internal storage. The subject technology generates a second snapshot of the table on the external volume. The subject technology generates a second list of metadata files on the external volume. The subject technology generates, based on the second list of metadata files, a second set of metadata files on the external volume. The subject technology generates a first set of data files in a different file format on the external volume.

Abstract: The disclosure relates to compositions comprising 3,6,7-trimethyllumazine. In particular, Manuka honey is an example of a composition comprising 3,6,7-trimethyllumazine. The composition is useful in preventing, ameliorating or treating TG2, JAK, and/or COX-2 associated conditions. These conditions include inflammation, pain, ulcers, Crohn's disease, reflux, gingivitis, schizophrenia, neurodegenerative disorders, Alzheimer's disease, Parkinson's disease, arthritis, cardiovascular diseases and cancers.

Abstract: A user selects a first group of one or more dimension fields that specifies a primary aggregation for a data visualization, where the one or more dimension fields are in a first data source. When one or more first dimension fields in the first group are not dimension fields in a second data source, a client device joins the first data source with the second data source at a first level of detail that is more granular than the primary aggregation to form a single combined data set that includes the one or more dimension fields specified in the first group and one or more measure data fields aggregated according to the first group. The client device rolls up the combined data set, including the one or more measure data fields, to form a final data set and displays the data visualization using the data from the final data.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A method dynamically combines multiple data sources. A user selects a first group of fields that specify primary aggregation for a data visualization. When a field in the first group is not a data field in one of the data sources, the process creates a second group of fields that specifies an intermediate aggregation for joining data sets that is more granular than the primary aggregation. For each data source, the process aggregates measure fields according to the second group to form a respective intermediate data set. The process forms a single combined data set from the intermediate data sets, including the fields specified in the first group, and aggregates each measure field in the combined data set using the primary aggregation to form a final data set based on the fields from the first group. The process then displays the data visualization using the final data set.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A database export system exports data using a plurality of nodes that process the data to generate structured result files that are partitioned by an export parameter in an export request. The database export system distributes the data and merges the files to avoid small file creation and increase processing speed via parallelism. The database export system generates the result files of a specified maximum size in a final format, where the files are processed merged in a temporary file format. The parallel processing is optimized and constrained per the amount of processing nodes, available memory, requested final file sizes, and operation based ordering to complete data exports in a scalable multi-stage approach.

Abstract: A method dynamically combines data from multiple data sources. A visual specification for a data visualization requires data from a primary data source and one or more secondary data sources. The primary data source is independent of the secondary data sources. The visual specification includes a first set of dimension fields that specify a hierarchical level for the data visualization. A second set of dimension fields specify a hierarchical level for joining data sets from the data sources. The hierarchical level for joining data sets is more granular than the hierarchical level for the data visualization. For each data source, a query is generated, and a data set is retrieved from the data source using the query. A single combined data set is formed that includes the dimension fields in the first set. The combined data set is rolled-up and a data visualization is displayed using the rolled-up data.