Data Engineering Integration

Additional Content

Common Content for Data Engineering 10.2.1

How to Create Cloudera Altus Clusters with a Cluster Workflow in Big Data Management

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.

Integrating Informatica® Big Data Management 10.2.2 HF1 SP1 with Qubole

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.

Optimize and Tune

Tuning and Sizing Guidelines for Data Engineering Integration (10.4.x)

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 …

Install and Upgrade

How to Install PowerCenter 10.4.0 with the Container Utility on Kubernetes

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.

Additional Articles

Implementing PowerExchange Express CDC for Oracle in a Non-RAC Environment

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.

Parallel Processing in Data Archive

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.

Additional Content

Common Content for Data Engineering 10.2.1

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