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How to Split a Domain is written for the system administrator who is responsible for splitting the domain. You can split the domain for the traditional and big data products. This guide assumes you have knowledge of operating systems, relational database concepts, and the database engines, flat files, or mainframe systems in your environment. …
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You can implement Cloudera Altus clusters hosted on Amazon Web Services (AWS) with Informatica® Big Data Management 10.2.1. Create a workflow with a Command task that runs scripts to create the cluster, and Mapping tasks to run mappings on the cluster. You can add another Command task to terminate and delete the cluster when workflow tasks are complete.
Customers of Amazon Web Services (AWS) and Informatica can integrate Big Data Management® 10.2.2 HF1 SP1 with Qubole, the data activation platform. This article describes how to prepare the Qubole and AWS environments and configure Big Data Management to run on Qubole clusters. The integration requires you to apply EBF-16050 to the domain and to clients.
You can tune Informatica Data Engineering Integration for better performance. This article provides sizing recommendations for the Hadoop cluster and the Informatica domain, tuning recommendations for various Data Engineering Integration components, best practices to design efficient mappings, and troubleshooting tips. This article is …
Informatica provides the Informatica container utility to install the Informatica domain quickly. This article describes how to install PowerCenter from the Docker image through the Informatica container utility on Kubernetes.
This article describes how to implement PowerExchange 10.1 Express CDC for Oracle in a non-RAC Oracle environment on UNIX. It identifies the configuration steps that you perform in Oracle, PowerExchange, and PowerCenter to create a working change data capture (CDC) environment. It also includes example configuration files, tips, and best practices.
This article describes the parallel processing capabilities of Data Archive 5.3. To improve archiving performance in Data Archive, you can use parallel processing in SQL statements. You can also process multiple tables simultaneously using java parallelism/threading.