| Title |
Abstract |
| Implement Data Observability with Databand |
Data observability is needed to improve quality of an organization’s data products. Databand, IBM’s latest acquisition in the Data and AI portfolio, achieves data observability through operational monitoring of data pipelines. In this lab you will learn how to monitor, identify, and resolve data pipeline issues in order to deliver reliable data to consumers. |
| Using Db2Shift to move Db2 databases to a Db2U Container |
Deploying an existing Db2 to OpenShift, Kubernetes, or Cloud Pak for Data usually involves some form of export and import and a lot of work! The new Db2 Shift tool moves your on-premises database directly to the Cloud - with no exporting of data! Db2 Shift can migrate your 10.5, 11.1 and 11.5 database directly into a Db2 container with no additional effort. This hands-on lab will take you through the IBM Db2 Shift program and how it can help modernize your Db2 databases quickly and efficiently! The lab covers the following topics:
- How to install Db2u on a Kubernetes cluster
- An overview of the Db2 Shift program
- Shift Db2 11.5 database to Db2u on Kubernetes
- Shift Db2 11.5 database via clone and deploy functions
- Shift Db2 11.1 Columnar database to a new Db2 Instance
And more! |
| Maximize your Governance Framework with End-to-End Data Lineage |
In this session, you will get hands-on experience with Cloud Pak For Data's data lineage capabilities. We will explore how to easily configure and visualize lineage from a variety of databases, ETL tools, and BI Reporting tools including PostgreSQL, Db2, DataStage, Tableau, and PowerBI. |
| Use AI to Improve the Performance of your Db2 for z/OS Database |
Learn how IBM Db2 AI for z/OS enhances usability, improves operational performance and maintains the health of IBM Db2 for z/OS systems.
You will see how Db2 AI's SQL query optimization, distributed connection control, system assessments and performance insights can be leveraged to improve the performance and stability of your database and free up your DBAs' for more critical tasks. IBM Db2 AI uses machine learning to learn from your unique operating environment to generate recommendations for performance improvements, warn of abnormal system behavior, and optimize SQL access paths. |
| Match 360 Deep Dive - Integration with DataStage NextGen |
This lab will demonstrate how to integrate Match 360 and DataStage Next Gen to bring in data from multiple sources using a default & generic data model. It will also cover working with a generic data model created in Match 360 and setting up matching algorithms to support the generic entity type. |
| Want clean Address Data Quality? Get your hands dirty! |
We all need better address data quality. Whether your new, or an expert, please join us for a hands-on workshop where you can meet IBM’s address data experts.
Ask them anything about address data quality, location, and geocoding. They will help you maximize the benefits you derive from the IBM address verification interface.
If you are brand new to Address data quality, or just need a refresher they can give you a demonstration on the address verification solution, explain how AVI is a great addition to your Data Fabric plans, or even assist in tuning your AVI stage jobs. |
| What's New in Watson Knowledge Catalog? Get hands on with the latest capabilities! |
In this hands-on session, you will explore the newest catalog and data quality features in Watson Knowledge Catalog including: Extending the catalog metadata model from directly within the UI, the new built-in Data Quality dashboard, data quality remediation workflow and more. |
| The power of adaptability and reusability of DataStage Next Generation |
It is important to design flexible, adaptable, and reusable DataStage flows for ETL processes. Such DataStage best practices should:
1. Use reusable components: such as sub-flows and routines across multiple jobs.
2. Use environment variables: to store global values across multiple jobs
3. Use job parameters and parameter sets: to pass values between jobs.
4. Use metadata: to store information about the data sources and targets.
5. Use stage parameters: to pass values between stages within a job.
With these best practices, you can change the ETL process without having to modify the flow design, and ensure consistency, accuracy, and re-usability.
In this session, we will cover job parameters, parameter sets and sub-flows in DataStage flow design. |
| Analyzing Airline Flight Delay Data with Db2 or Netezza and Jupyter Notebooks in IBM Cloud |
Since the return to travel after the COVID-19 pandemic, many people have experienced flight delays. This hands on lab will use data from the United States Department of Transportation that details flight delays for commercial airline flights. This data will be used to determine how flight delays for 2023 compare with the prior 4 years to determine if flight delays have increased or if it is just a misconception. After the table is created in Db2 on Cloud and the data is loaded, a Jupyter notebook will be used to query the data and visualize the data to assist in making assumptions about the data.
Lab attendees will be provided a lab guide, required files, and will need their own device along with an IBM Cloud account. IBM Cloud Lite plans used in the lab are free of charge. |
| DataStage NextGen |
In this hands-on session, you explore the latest features of IBM DataStage NextGen by building flows with various connectors of popular data sources. Incorporate processing power of your existing APIs and applications into the DataStage flow. Integrate your DataStage flow in your existing IT processes. Have insight of data quality observability via Databank integration. |
| Disaster Recovery solution for Netezza Performance Server using Netezza Replication Services 3.0 |
Netezza Replication Service 3.0 is complete re-vamp of older version of NRS 1.6 that allows customers to configure more than one Netezza Performance Server systems for Disaster Recovery or Work load partitioning purposes.
This hands own session will teach users on how to install, setup and add Netezza Performance Server databases for replication to between 2 Nodes using Netezza Replication Services 3.0 (NRS 3.0). And also introduces them to monitoring and troubleshooting tools of NRS 3.0.
The plan is to run 2 VM images on a laptop which will have NPS instances running along with NRS software.
The target audience will be Netezza server DBA as well as CTOs. |
| Data Quality SLA in Watson Knowledge Catalog: The bedrock of a successful Data Monetization strategy |
A data quality Service Level Agreement (SLA) specifies an organization’s expectations for response and remediation for data quality issues. This is important for regulatory reporting and to support data marketplace requirements. In this session you will learn how you can use Watson Knowledge Catalog 4.7 to automatically certify the data quality results of your critical data elements. You will see how to automatically detect critical data elements, analyze data, define data quality SLAs and automate remediation tasks with workflows. |
| Hands-on with the new Db2 Object Storage and Open Data Formats support and watsonx.data integration |
In this hands-on lab session, take control of a Db2 Warehouse on Cloud database, and explore two brand new features. Native object storage support allows for the usage of object storage such as AWS S3 to store native Db2 table data, allowing for lower cost of storing data in your warehouse and increasing price-performance. Open Data Formats support allows for easy integration of enterprise data in a variety of data formats as DATALAKE tables, with implementations such as Apache Parquet support within the Db2 engine. The lab will cover the SQL and Db2WoC console usage of these features, and how Db2 DATALAKE tables are integrated with watsonx.data in a seamless manner. |
| Deep dive into Presto: Unlocking the power of distributed SQL with Presto |
In this lab you’ll get started with the basics of Presto, an open source, high performance SQL query engine now used by companies such Uber, Airbnb, Meta, Alibaba, and more for interactive ad hoc queries, reporting & dashboarding. Topics include:
- What is Presto and getting started
- How to write a Presto query
- What makes Presto fast?
- Best practices for querying with Presto |
| Explore the Lakehouse Developer edition with Presto, Spark, Flink & StepZen |
The IBM Lakehouse Developer edition is an easy to get started environment for you to play and experiment with the Lakehouse, even on your laptop or a VM. You will learn what it means to use Python & Presto to bring data into the Lakehouse, build applications quickly and gain insights from your data. You can even grant access to other users to “share” your Developer instance of the Lakehouse and experiment with finely grained access controls.
As part of this session, you will also learn how to use Flink, Spark to ingest and access data from the Lakehouse. You will also exercise IBM Stepzen, the GraphQL powered Service, to learn how to develop Lakehouse data powered analytics applications |
| Virtualize disparate data sources via a Protected, & Governed View- Enabling Self-Service Analytics |
In this hands-on-lab session you will create a virtualized data set from multiple different database types and locations, thus avoiding the need to move or copy data. With Watson Knowledge Catalog, the lab will leverage automated Data Enrichment to highlight SPI, and PII data types. Data protection rules will be created to specify what types of users can see what types of data. Using 2 different personas you'll witness how user permissions, and data protection rules will allow some users access to PII data and not others - all without doing any explicit coding. Learn how you can enable self-service analytics with a access to 360-degree view of data?that is governed, protected, cataloged and with rich metadata. |
| Deploy and Use two Db2 Warehouse on Cloud with Replication for Continuous Availability |
Setup and Use two Db2 Warehouse on Cloud instances and use them to failover or outages that include failures, migrations, upgrades as well as providing workload load balancing and query offloading for local production system performance.
We address all database and workload requirements; setup connectivity; activate replication; enable replication for the workload; and demonstrate how sites can be used for failover. |
| Bring Db2 and Netezza to the Data Lakehouse Party |
This lab with highlight how to utilize native and external engines to process data stored in watsonx.data. The lab will start with Ahana PrestoDB engine accessing data stored in the lakehouse, next external engines Db2 and Netezza will be used to to access the same data in the lakehouse and join across data native Db2 and Netezza. |
| The Wait Is Over! Unveiling The Next Gen Of Services for Db2 Administration & Development |
In this hand on lab, you will have the opportunity to experience a new way to manage your Db2 systems, execute, tune, stabilize your queries. You will also be exploiting advanced tuning features such as index advisor, access path advisor. The capability to administer your IDAA environment is also part of the exercise. In addition, you will also have chance to experience how you can automate Db2 database changes thru a intelligent web user interface or via a pipeline. All the above is based on APIs enabled within the Unified Management Server.
Bring your on device to experience them all. |
| Getting Started wtih watsonx.data |
In this hands-on lab, you'll learn the basics of using a watsonx.data lakehouse. Your lab work will include: querying lakehouse data from the cli and from third party tools; working with functions; using iceberg features like time travel; and exploring object storage. This session is intended for anyone who wants to be more comfortable with lakehouse concepts and operating in an open lakehouse environment. |