Benjamin Perez-Goytia

This is a live presentation that shows the power of the Qlik Data Integration and how business can use it to move data on real-time without impacting their sources. Qlik Data Integration offers tools to create real-time data pipelines to move data into modern targets on-premise or on the cloud.

This presentation - in Spanish - covers an overview of the Qlik accelerators for SAP, specifically, for Order-To-Cash. The Order-to-Cash accelerators are templates within the Qlik Data Integration Platform that allow users to quickly implement an end-to-end data pipe-line architecture to replicate and analyze SAP data.

Esta presentacion muestra las ventajas de utilizar los aceleradores de Qlik para extraer, transformar, y analyzar data de SAP. This presentation illustrates the advantages of using the Qlik accelerators to extract, transform, and analyze the SAP data.

This whitepaper discusses how to convert and consume semi-structured data with the Qlik Data Integration solution.

This article discusses the best practices that users should follow when using Oracle Data Integration Platform Cloud (DIPC) – specifically Oracle Data Integrator (ODI) – to perform incremental updates in the Oracle Autonomous Data Warehouse Cloud Service (ADWC).

This article discusses the best practices that users should follow when using Oracle Data Integration Platform Cloud (DIPC) - specifically Oracle Data Integrator (ODI) - to perform the initial data load of a data warehouse in the Oracle Autonomous Data Warehouse Cloud Service (ADWCS).

In this Oracle OpenWorld 2017 Session, Ayush Ganeriwal and I presented an overview of the Oracle Data Integration Platform for big data technologies. Also, we presented how to use Oracle Data Integrator in various big data cloud services such as Oracle Big Data Cloud Service, Oracle Big Data Cloud Service - Compute Edition, and Amazon Elastic MapReduce.

In this Oracle OpenWorld 2017 Session, Julien Testut and I presented how Oracle Data Integration Platform Cloud plays a vital role in simply, efficiently, and reliably migrating data from on-premises data servers to the cloud.

ODI supports a variety of technologies such as SQL databases, Big Data, Files, Java Messaging Systems (JMSs), and many other technologies. Most of these technologies are now available on both on-premises and on data cloud services. For each of these technologies, a variety of LKMs are available. For instance, ODI offers LKMs for SQL databases such as Oracle, Teradata, MySQL, MS SQL Server, among others. For Big Data, ODI offers LKMs for Spark, Hive, Sqoop, Kafka, and Pig, among… Show more

ODI supports a variety of technologies such as SQL databases, Big Data, Files, Java Messaging Systems (JMSs), and many other technologies. Most of these technologies are now available on both on-premises and on data cloud services. For each of these technologies, a variety of LKMs are available. For instance, ODI offers LKMs for SQL databases such as Oracle, Teradata, MySQL, MS SQL Server, among others. For Big Data, ODI offers LKMs for Spark, Hive, Sqoop, Kafka, and Pig, among others.
LKMs are one of seven categories of Knowledge Modules (KMs) found in ODI. Other categories of KMs include: Reverse-Engineering, Check, Integration, Extract, Journalizing, and Service. This article focuses on the selection and use of LKMs. Show less

This article discusses best practices on using the Reverse Engineering features of Oracle Data Integrator (ODI) on the cloud and on premises. The first part of this article presents the various options available in ODI to reverse-engineer metadata from a data server. Then, the article discusses performance considerations when running and executing reverse-engineering tasks. The last section of this article discusses the ODI reverse-engineering best practices.

This article discusses how to use Loading Knowledge Modules (LKMs) in Oracle Data Integrator (ODI) to upload data into instances of the Oracle Database Cloud Service (DBCS).

This article demonstrates how to configure Oracle Data Integrator (ODI) for the Amazon Elastic MapReduce (EMR) cloud service. Amazon EMR is a big data cloud service, available on the Amazon Web Services (AWS) cloud computing services.
ODI is well documented to run on both the Cloudera and Hortonworks distributions of Hadoop. ODI can also run on the distributions of Hadoop found on the Amazon EMR cloud service.

This article demonstrates how to install Oracle Data Integrator (ODI) on the Amazon Elastic MapReduce (EMR) cloud service. Amazon EMR is a big data cloud service, available on the Amazon Web Services (AWS) cloud computing services.
ODI is well documented to run on both the Cloudera and Hortonworks distributions of Hadoop. ODI can also run on the distributions of Hadoop found on the Amazon EMR cloud service.

This article demonstrates how to prepare the Amazon Elastic MapReduce (EMR) cloud service for the Oracle Data Integrator (ODI) installation. Amazon EMR is a big data cloud service, available on the Amazon Web Services (AWS) cloud computing services.
ODI is well documented to run on both the Cloudera and Hortonworks distributions of Hadoop. ODI can also run on the distributions of Hadoop found on the Amazon EMR cloud service.

This article presents an overview of how to integrate Oracle Data Integrator (ODI) on-premise with cloud services. Cloud computing is now a service or a utility in high demand. Enterprises have a mix of on-premise data sources and cloud services. Oracle Data Integrator (ODI) on-premise can enable the integration of both on-promise data sources and cloud services.

This article describes the steps required to enable Oracle Partition Exchange in Oracle Data Integrator (ODI). The article features a new ODI knowledge module (KM), the IKM Oracle Partition Exchange Load, which uses the Oracle Partition Exchange technology to upload data, by partition, into a partitioned table of an Oracle database.

This article presents a data integration recipe on how to use Oracle Partition Exchange with Oracle Data Integrator (ODI) to upload data very fast into partitioned tables of a large Oracle data warehouse.

This article presents a modified version of the Oracle Data Integrator (ODI) knowledge module called LKM Oracle to Oracle (datapump). The ODI knowledge module presented in this article has been enhanced with additional options and steps to take advantage of the best features of Oracle Data Pump and Oracle External Tables. Some of the enhancements include data compression, server to server file transport, threads control, and the use of Oracle Optimizer Hints. This article shows how to… Show more

This article presents a modified version of the Oracle Data Integrator (ODI) knowledge module called LKM Oracle to Oracle (datapump). The ODI knowledge module presented in this article has been enhanced with additional options and steps to take advantage of the best features of Oracle Data Pump and Oracle External Tables. Some of the enhancements include data compression, server to server file transport, threads control, and the use of Oracle Optimizer Hints. This article shows how to configure and use this knowledge module. Show less

This article illustrates how to implement early arriving facts in Oracle Data Integrator (ODI). This article has two parts. Part I provides an overview of a proof-of-concept created in ODI to implement an early arriving fact. Part II includes step-by-step instructions on how to implement an early arriving fact using Oracle database functions, ODI lookups, ODI user functions, and ODI custom knowledge modules.

This article includes step by step instructions on how to implement an early arriving fact in Oracle Data Integrator (ODI) using database functions, ODI lookups, ODI user functions, and ODI custom knowledge modules.

This blog illustrates how to configure Oracle Data Integrator (ODI) Web Services in Oracle Business Intelligence (OBIEE) to invoke ODI scenarios and ODI load plans.