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But because Hana is columnar database too (and in BI environments the column store will be used anyway) my findings and rest of this article will apply to Hana in the same way as to IQ. Until now I did not have the chance to implement a similar scenario with Hana.
#LOAD BULK DATA INTO SEQUEL PRO UPDATE#
Instead, in columnar databases you need to update the whole data set with one batch update statement.
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Likewise, you should not update thousands or millions of records by issuing onr UPDATE SQL statement per record within a loop. With respect to performance, bulk loads are simply the only feasible way to load large amounts of data into IQ. Columnar databases like IQ are not designed for such kind of transactional processing and performance will decrease dramatically (I actually had the impression that the system hangs when I implemented the first version of my load without the bulk load option). If you are not using the bulk load option for target tables Data Services will send a single INSERT SQL statement for each record to IQ. I needed to implement a special workaround due to the fact that Data Services cannot use bulk loads in such a scenario because in SCD 2 the previous versions of changed dimension records need to be updated (the columns IsValid and ValidTo need to be updated.)īulk loading is a MUST in IQ if you are loading large amounts of data (let’s say everything above 100.000 records). Use geoprocessing tools to import source shapefiles, feature classes, tables, rasters, or XML workspace documents that you moved to AWS.įor more information about getting files and other source data onto an Amazon Web Services instance, see Strategies for data transfer to Amazon Web Services.In a POC last year I implemented a Slowly Changing Dimension (type 2, SCD 2) load into a Sybase IQ data warehouse (see SCD for more information on slowly changing dimensions).Create an empty feature class or table and use a source XML record set document that you uploaded to AWS to load data to the feature class or table.
#LOAD BULK DATA INTO SEQUEL PRO PRO#
See Copy feature datasets, feature classes, and tables to a geodatabase in the ArcGIS Pro help for more information. Copy data from a source file you transferred to AWS, such as a file geodatabase, and paste the data into an enterprise geodatabase on AWS.Once your source data has been moved to AWS, you need to sign in to an Amazon Elastic Compute Cloud (EC2) instance where ArcGIS Pro is installed to connect to the source data and the geodatabase on AWS, andįrom ArcGIS Pro, you can do the following: You can also physically move data to AWS and load it into the geodatabases in your deployment.įirst, move the data to Amazon Web Services. This allows you to publish hosted scene layers to the portal, and the scene caches will be copied to the tile cache data store. When you use a CloudFormation template provided by Esri to deploy ArcGIS Enterprise, a tile cache data store is also created. When you publish hosted feature layers to the deployment, data is copied to the relational data store. The relational data store configured through ArcGIS Data Store acts as the hosting server site's managed database. When you delete the feature or WFS-T service, the corresponding data is deleted from the managed database.ĪrcGIS Enterprise deployments include ArcGIS Data Store. When you publish a feature or WFS-T service, a copy of the data will be moved to the managed database. When you create a stand-alone ArcGIS Server site using an Esri 10.7 or a 10.6 or older Amazon Web Services CloudFormation template provided by Esri, a geodatabase is created (egdb) and registered as the ArcGIS Server site's managed database.
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You can connect to an ArcGIS Server site on AWS from an installation of ArcGIS Pro on your on-premises machine and register both your local enterprise geodatabase and an enterprise geodatabase on AWS, publish a geodata service of the geodatabase on AWS, and use the geodata service to replicate data from the local geodatabase to the geodatabase on AWS. When your client, data source, and ArcGIS Enterprise are not deployed in the same location, you will likely experience degraded performance when data is sent between an on-premises component and the cloud.