vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Integrating apps and tools 

You can also connect external applications and tools to Db2 Warehouse on Cloud and use them to further manage or analyze your data. For example:



 Data integration 



* Connect your IBM Cloud® applications that need an analytics database.
* [DataStage](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmdatastage)
* [Informatica](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partyinformatica)
* [Lift](https://www.lift-cli.cloud.ibm.com/docs)
* [InfoSphere Data Replication](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmidr)
* [Segment](https://segment.com/docs/destinations/db2/)
* [Data Studio](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmdata_studio)
* [Data Server Manager](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmdsm)
* [CLPPLUS](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmclpplus)
* [Aginity Workbench to migrate Netezza® data models and data to Db2 Warehouse on Cloud](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partyaginity_wb)
* [InfoSphere Data Architect to design and deploy your database schema](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmida)







 Data visualization/BI
Joining data from different data source by using watsonx.data 

In this tutorial, you learn how to set up two data sources, Db2 and Netezza in your IBM® watsonx.data instance and to analyse the combined data from these sources.

Sample Scenario : You are the Data Engineer for your company. The company stores the sales data in Db2 Warehouse whereas, the customer data resides in Netezza. Your task is to combine and analyze the two data sets and gain knowldege about the customer behaviour. In this scenario, you explore the process of obtaining data from two seperate data sources, combining them, and creating a comprehensive view in watsonx.data instance.



 Objective 



* Setting up Db2 and Netezza data source





* Joining data from Db2 and Netezza



Zoom

![Workflow diagram](https://cloud.ibm.com/docs-content/v1/content/4c3c4670556d1cad9f530a5c8a9edd2e63919b83/watsonxdata/images/ibm_infrastructure.svg)

Figure 1. Workflow diagram





 Before you begin 

This tutorial requires:



* Subscription of watsonx.data on cloud.







 Step 1: Provisioning watsonx.data service instance 

In this section of the tutorial, you are going to deploy a watsonx.data service, create a watsonx.data service instance from the service's catalog page or by specifying the service ID to the command line or to the API.



1. Log in to the [IBM Cloud console](https://cloud.ibm.com).
2. Click Catalog. The list of services appears.
3. To filter the list of services that are displayed, select the Databases category.
4. Click the watsonx.data tile. The service provisioning page opens.
5. Select IBM Cloud® as the cloud platform to deploy watsonx.data.
6. Select a location from the list of available locations for watsonx.data service.
7. Enter the service name. The service name can be any string.
[Backup and restore with SQL Server Management Studio](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewbackup-and-restore-management-studio) Large data migration, full database backup, moving database data to another drive, moving database data between different versions of SQL server. 
 [Transactional replication](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewtransaction-replication) Copying and distributing data and database objects from one database to another, synchronizing between databases to maintain consistency. 
 [Database mirroring](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewdatabase-mirroring) Copying or mirroring of an SQL Server database on a standby server. 
 [Always On availability groups](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewalways-on-availability-groups) Large data migration 
 [Always On distributed availability groups](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewalways-on-distributed-availability-groups) Large data migration 
 [Data Migration Assistant (DMA)](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewdata-migration-assistant) Large data migration, migration assessment, server upgrade 
 [Smart Bulk Copy](https://cloud.ibm.com/docs/cloud-infrastructure?topic=cloud-infrastructure-mssql-db-overviewsmart-bulk-copy) Large data migration, other cloud service providers to IBM Cloud VPC, on-premises to IBM Cloud VPC. 





 Mass Data Migration (MDM) 

IBM Cloud Mass Data Migration (MDM) helps you move terabytes to petabytes of data into IBM Cloud in a fast, simple, and secure way.
Time series functions 

Use time series functions to process time series data.



*  [Artifact creation functions](https://cloud.ibm.com/docs/services/sql-query?topic=sql-query-artifact)
Each of these functions produces, as its output, a time series or some other artifact that some data processing functions require as input.
*  [Data processing functions](https://cloud.ibm.com/docs/services/sql-query?topic=sql-query-data_processing_functions)
Each of these functions evaluates or manipulates input data and produces output data.
* Users with user permissions to work with the Frankfurt Activity Tracker instance cannot see service keys. Therefore, these users cannot use the export API to download data.
* Service keys are only used to export data from an auditing instance by using the Export API.



* Users can request the export of data through the UI. [Learn more](https://cloud.ibm.com/docs/activity-tracker?topic=activity-tracker-export).







 Step 7. Archiving logs 

When you archive logs from the Frankfurt auditing instance to a IBM Cloud Object Storage (COS) bucket, consider the following information:



* When you provision an instance of the COS service, this instance is a global one in your account. It is not region-bound.
* You must configure a bucket that complies with the EU-supported and GDPR regulations. For the list of COS EU-supported endpoints, see [EU-supported endpoints](https://cloud.ibm.com/docs/services/cloud-object-storage?topic=cloud-object-storage-endpointsendpoints-eu-managed).

For example, consider the following scenarios:



* For a bucket with single site resiliency, you can create the bucket in Amsterdam or Milan.
* For a bucket with regional resiliency, you can create the bucket in the EU-DE location to keep the data in Frankfurt.
* For a bucket with cross region resiliency, you can create the bucket in the eu-geo location. Data is kept within the EU geography across datacenters that are located in Milan, Amsterdam, and Frankfurt.



* You must restrict user access to manage archived log files in these buckets.
* Users are responsible for downloading files to EU-supported locations.



To learn how to configure archiving for your auditing instance, see [Archiving logs](https://cloud.ibm.com/docs/services/activity-tracker?topic=activity-tracker-archiving).





 Step 8. Querying archived events with SQL Query service 

Data Engine provides a serverless, no-ETL solution to easily query data stored in COS.
Netezza Performance Server and Kafka 



 Overview 

[Apache Kafka](https://kafka.apache.org/documentation/) is a publish-subscribe messaging system, which you can use to move data between popular applications.

After you integrate your IBM® Netezza® Performance Server for IBM Cloud Pak® for Data as a Service instance with Kafka through the Kafka JDBC connector, you can use Netezza Performance Server as one of the following:



* A data source, which brings data to Kafka.
* A data sink, which reads data from Kafka.







 Using Netezza Performance Server as a data source 

An e-commerce company stores its product listings in a Netezza Performance Server database. To streamline the in-app search experience and to access real time analytics, consumer apps (for example, Elasticsearch and Apache Flink) have access to the listings.

In this case, data is read from Netezza Performance Server through the Kafka JDBC source connector and Kafka streams the data. The consumer apps read from the stream and further process the data.

The following image illustrates the data flow Netezza Performance Server as a data source.

Zoom

![Netezza Performance Server as a data source](https://cloud.ibm.com/docs-content/v1/content/da65f8904a6bd1315f9f9194ca407510f517bed8/netezza/images/nzdatasource.png)

Image 1. The diagram depicts how Kafka reads data from Netezza through the JDBC source connector and enables consumer apps to access it.





 Using Netezza Performance Server as a data sink 

To improve patient outcomes, efficiently identify risk factors, and provide quicker intervention times, a hospital extracts meaningful insights from physiological data. Different data sets from various channels are analyzed as they arrive.

In this case, the incoming data is streamed through Kafka and then computed. The producers are the sources of physiological data that come from different channels.
[Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) 







 Storage services 

America

Asia Pacific

Europe



Table 12. Storage services integration in America locations

 Service Dallas (us-south) Washington (us-east) Toronto (ca-tor) Sao Paulo (br-sao) 

 IBM Cloud Object Storage [] ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) 
 Mass Data Migration !
ibm-lh data-copy --source-data-files s3://cust-bucket/warehouse/a_source_file.parquet --source-s3-creds "AWS_ACCESS_KEY_ID=xxxxxx,AWS_SECRET_ACCESS_KEY=yyyyyy,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --staging-location s3://cust-bucket/warehouse/staging/ --staging-s3-creds "AWS_ACCESS_KEY_ID=zzzzzz,AWS_SECRET_ACCESS_KEY=vvvvvvv,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --target-tables iceberg_target_catalog.ice_schema.cust_tab1 --ingestion-engine-endpoint "hostname=localhost,port=8080" --create-if-not-exist





 Ingest multiple CSV/Parquet files and CSV folders from S3 location by using a command 

To ingest multiple CSV/Parquet files and CSV folders from a S3 location, run the following command:
* [Aginity Workbench to migrate Netezza® data models and data to Db2 Warehouse on Cloud](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partyaginity_wb)
* [InfoSphere Data Architect to design and deploy your database schema](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmida)







 Data visualization/BI 



* [Cognos Analytics to run Business Intelligence reports against your data](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmcognos)
* [Looker](https://docs.looker.com/setup-and-management/connecting-to-db)
* [Tableau](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partytableau)
* [Microsoft Excel](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partyexcel)
* [Esri ArcGIS for Desktop to perform geospatial analytics and map publishing with your data](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partyesri_arcgis)







 Data science 



* [Watson Studio (formerly IBM Data Science Experience)](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmwatson_studio)
* [SPSS Statistics](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_ibmspss_stats)
* [SAS](https://cloud.ibm.com/docs/Db2whc/connecting?topic=Db2whc-connect_3rd_partysas)
Loading data to Netezza Performance Server 

There are different ways in which you can load your data on IBM® Netezza® Performance Server for IBM Cloud Pak® for Data as a Service. Learn how to load data [from your local machine](https://cloud.ibm.com/docs/netezza?topic=netezza-load-dbsload-data-nzsql) or [from S3](https://cloud.ibm.com/docs/netezza?topic=netezza-load-dbsload-data-s3).



 Before you begin 



1. Download the client packages from Fix Central and install them as described in [Installing client packages](https://www.ibm.com/docs/en/netezza?topic=npsda-installing-client-software-packages-2).

One of the easiest ways to upload your data to Netezza Performance Server is by using the nzsql command. nzsql provides an interface that you can use to run SQL commands on the Netezza Performance Server host.

You cannot use the web console to load data.
2. Log in to Netezza Performance Server.

In this example, the [the nzsql command](https://www.ibm.com/docs/en/netezza?topic=sco-nzsql-command) is used, but you can use other clients.

As explained in [Connecting to Netezza Performance Server](https://cloud.ibm.com/docs/netezza?topic=netezza-connecting-overview), you can provision Netezza Performance Server with a private endpoint or public and private endpoints. Each endpoint type provides a set of two hostnames that you can connect to Netezza Performance Server.

To load data, you must be the admin user, or the owner of the database or schema. If you are loading data for the first time and you are not loading data to a preexisting table, you also must have the Create Table privilege.
* [Data Studio](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmdata_studio)
* [Data Server Manager](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmdsm)
* [CLPPLUS](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmclpplus)





* [InfoSphere Data Architect to design and deploy your database schema](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmida)







 Data visualization & BI 



* [Looker](https://docs.looker.com/setup-and-management/connecting-to-db)
* [Tableau](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_3rd_partytableau)
* [Microsoft Excel](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_3rd_partyexcel)







 Data science 



* [Watson Studio (formerly IBM Data Science Experience)](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmwatson_studio)
* [SPSS Statistics](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_ibmspss_stats)
* [SAS](https://cloud.ibm.com/docs/Db2onCloud/connecting?topic=Db2onCloud-connect_3rd_partysas)









 Connecting to another Db2 database 

Db2 data virtualization (also known as federation) is supported by Db2 on Cloud.
><-- <ul> --><br><br> * DoubleArraySegmentTimeSeries<br> * StringSegmentTimeSeries<br> * StringArraySegmentTimeSeries<br><br><-- </ul> --><br> || ArrayTimeSeries | Represent any of the following data types: - DoubleArrayTimeSeries<br><br><-- <ul> --><br><br> * DoubleArraySegmentTimeSeries<br> * StringArrayTimeSeries<br> * StringArraySegmentTimeSeries<br><br><-- </ul> --><br> |<-- </table ""> --><-- <table> -->Table 3. Time reference system data type| Data type | Description || --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- || TRS | A time reference system (TRS)]time reference system _TRS] ! ! ! !
Ingesting data through the command-line interface (CLI) 

An ingestion job in IBM® watsonx.data is run with the ibm-lh tool.

For description of different options and parameters that are supported in ibm-lh tool, see [Options and parameters supported in ibm-lh tool](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-cli_commands).

To run an ingestion job by using commands through the CLI, see [Creating an ingestion job by using commands](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-create_ingestioncli).

To run an ingestion job by using configuration file through the CLI, see [Creating an ingestion job by using the configuration file](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-create_ingestconfig).
[deployment guide icon](https://cloud.ibm.com/media/docs/images/homepage/deployment-guide.svg) [Power Virtual Server for SAP HANA - variation 'SAP ready PowerVS'](https://cloud.ibm.com/docs/deployable-reference-architectures?topic=deployable-reference-architectures-deploy-arch-ibm-pvs-sap-ready-to-go)Power Virtual Server for SAP HANA - variation 'SAP ready PowerVS' Reference architecture

The SAP ready PowerVS variation of the Power Virtual Server for SAP HANA creates a basic and expandable SAP system landscape. The variation builds on the foundation of the VPC landing zone and Power Virtual Server with VPC landing zone. PowerVS instances for SAP HANA, SAP NetWeaver, and optionally for shared SAP files are deployed and preconfigured for SAP installation.

Deployable![ref architecture icon](https://cloud.ibm.com/media/docs/images/homepage/reference-architecture.svg) [Power Virtual Server with VPC landing zone - as new deployment](https://cloud.ibm.com/docs/deployable-reference-architectures?topic=deployable-reference-architectures-deploy-arch-ibm-pvs-inf-full-stack)Power Virtual Server with VPC landing zone - as new deployment Reference architecture

New deployment of the Power Virtual Server with VPC landing zone creates VPC services and a Power Virtual Server workspace and interconnects them.

Deployable![ref architecture icon](https://cloud.ibm.com/media/docs/images/homepage/reference-architecture.svg) [Process big data logs with SQL](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-big-data-log-analytics)Process big data logs with SQL Solution tutorial

In this tutorial, you will build a log analysis pipeline designed to collect, store and analyze log records to support regulatory requirements or aid information discovery. This solution leverages several services available in IBM Cloud: Event Streams, Object Storage, Data Engine (previously SQL Query), Key Protect, and IBM Analytics Engine.
For example:

ibm-lh data-copy --source-data-files s3://cust-bucket/warehouse/a_source_file1.csv, s3://cust-bucket/warehouse/a_source_file2.csv --source-s3-creds "AWS_ACCESS_KEY_ID=xxxxxx,AWS_SECRET_ACCESS_KEY=yyyyyy,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --staging-location s3://cust-bucket/warehouse/staging/ --staging-s3-creds "AWS_ACCESS_KEY_ID=zzzzzz,AWS_SECRET_ACCESS_KEY=vvvvvvv,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --target-tables iceberg_target_catalog.ice_schema.cust_tab1 --ingestion-engine-endpoint "hostname=localhost,port=8080" --create-if-not-exist

ibm-lh data-copy --source-data-files s3://cust-bucket/warehouse/ --source-s3-creds "AWS_ACCESS_KEY_ID=xxxxxx,AWS_SECRET_ACCESS_KEY=yyyyyy,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --staging-location s3://cust-bucket/warehouse/staging/ --staging-s3-creds "AWS_ACCESS_KEY_ID=zzzzzz,AWS_SECRET_ACCESS_KEY=vvvvvvv,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --target-tables iceberg_target_catalog.ice_schema.cust_tab1 --ingestion-engine-endpoint "hostname=localhost,port=8080" --create-if-not-exist





 Ingest all Parquet files in a folder from S3 location by using a command
Streaming data from Log Analysis 

Event Streams can be used to stream data from IBM Cloud® Log Analysis to other corporate tools, such as Security Information and Event Management (SIEM) tools.

When you enable streaming on an IBM Cloud Log Analysis instance, you configure Log Analysis to send data to an Event Streams instance. Then, you can configure Kafka Connect to consume the data and forward it to your destination tool. After the data is persisted within Event Streams, you can configure any application, or service to create a subscription and take action on log data that is streamed.

For more information, see [Streaming data from an IBM Log Analysis instance](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-streaming).

For step-by-step instructions for streaming to Splunk as an example SIEM system, see [Streaming log events from LogDNA to Splunk](https://ibm.github.io/cloud-enterprise-examples/log-streaming/configure-streaming-for-third-party-tools/).
Loading data from your local system 

You can load data in a delimited format file (CSV or TXT) from your local system into IBM® Db2® Warehouse on Cloud. For files less than 500 MB in size, simply drag and drop them onto the Load page in the Db2 Warehouse on Cloud web console.

When loading data through the web console, Db2 Warehouse on Cloud autodetects and infers column types for your file. You can also configure delimiters and date/time formats for your data.

If you want to load larger CSV files on the gigabyte or terabyte scale into Db2 Warehouse on Cloud on IBM Cloud, we recommend using [IBM Lift CLI](https://www.lift-cli.cloud.ibm.com), a free ground-to-cloud data migration tool.

For a tutorial about loading CSV files from a local system into Db2 Warehouse on Cloud, see [How to use IBM Lift CLI to migrate large CSV files to Db2 Warehouse on Cloud](https://www.ibm.com/cloud/garage/dte/tutorial/moving-ibm-db2-warehouse-cloud-using-ibm-lift)
[Checkmark](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker//images/checkmark-icon.svg) [Global and location-based events ]](https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-at-events) 
 [Mass Data Migration](https://cloud.ibm.com/docs/mass-data-migration?topic=mass-data-migration-getting-started-tutorial) mass-data-migration ![Checkmark](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker//images/checkmark-icon.svg) [Events](https://cloud.ibm.com/docs/mass-data-migration?topic=mass-data-migration-at-events) 



[] IBM Cloud Object Storage (COS) generates global, and location-based events.



* By default, bucket management events such as the creation of a bucket are collected automatically.
* Collection of data events in your account is optional. You must configure each bucket to enable data events by selecting the option Track data events.
* Mass Data Migration generates global events only, for each transition of the order state.







 VMware Solutions 

With IBM Cloud for VMware Solutions, you can quickly and seamlessly integrate or migrate your on-premises VMware workloads to the IBM Cloud by using the scalable, secure, and high-performance IBM Cloud infrastructure and the industry-leading VMware hybrid virtualization technology. You can easily deploy your VMware virtual environments and manage the infrastructure resources on IBM Cloud. At the same time, you can still use your familiar native VMware product console to manage the VMware workloads. [Learn more](https://www.ibm.com/cloud/vmware).