Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL...
Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL / Data Integration tools to extract data from various data sources. Future blog topics will cover extracting from: Oracle SaaS Applications, Oracle on-premise applications (e.g. EBS), and other Oracle applications such as Responsys, Eloqua and third party data sources like Salesforce or Workday. The first blog on...
Co-Author: Saunnie Bell, Technical Manager, Oracle Consulting Background This blog is part of a series covering topics on how to utilize Oracle Analytics / ETL / Data Integration tools to extract...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains of data processing, and perform hyperparameter tuning on them, all at the same time. I ended up using Apache Spark with the CrossValidator and pipeline models. This article will detail the approach. Import statements. Nothing will be detailed here. These are just provided to make running this yourself simple. from...
While exploring natural language processing (NLP) and various ways to classify text data, I wanted a way to test multiple classification algorithms and chains of data processing, and perform...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is used in the natural language processing (NLP) area of artificial intelligence to determine the importance of words in a document and collection of documents, A.K.A. corpus. Various implementations of TF-IDF were tested in python to gauge how they would perform against a large set of data. Tested were sklearn, gensim...
Introduction This post will compare vectorizing word data using term frequency-inverse document frequency (TF-IDF) in several python implementations. TF-IDF is used in the natural language processing...
Background This blog walks though how to configure Oracle Data Integrator (ODI) on Oracle Cloud Marketplace Agents in High Availability (HA) Mode with the...
Background This blog walks though how to configure Oracle Data Integrator (ODI) on Oracle Cloud Marketplace Agents in High Availability (HA) Mode with the Autonomous Transaction Processing (ATP) Database to hold the ODI repository. To allow for a true application HA, the agents configured throughout this blog will be J2EE agents deployed in a WebLogic domain cluster of managed servers . For a reminder on the different types of agents that can be configure with ODI, please...
Background This blog walks though how to configure Oracle Data Integrator (ODI) on Oracle Cloud Marketplace Agents in High Availability (HA) Mode with the Autonomous Transaction Processing (ATP)...
Co-authors: Balaji Krishnan, Product Management Architect, Analytics Apps Ravi Guddanti, Principal Member of Technical Staff, Analytics Customer Excellence ...
Co-authors: Balaji Krishnan, Product Management Architect, Analytics Apps Ravi Guddanti, Principal Member of Technical Staff, Analytics Customer Excellence Background This blog is the third in a series covering topics on how to utilize Oracle ETL / Data Integration tools to extract data from various data sources. Future blog topics will cover extracting from: Oracle SaaS Applications, Oracle on-premise applications (e.g. EBS), and other Oracle applications such as Taleo,...
Co-authors: Balaji Krishnan, Product Management Architect, Analytics Apps Ravi Guddanti, Principal Member of Technical Staff, Analytics Customer Excellence Background This blog is the third in a...
Validated September 30, 2020 Introduction Oracle Analytics for Applications (FAW / OAX) is built on top of Oracle Analytics Cloud (OAC) and is powered by Oracle...
Validated September 30, 2020 Introduction Oracle Analytics for Applications (FAW / OAX) is built on top of Oracle Analytics Cloud (OAC) and is powered by Oracle Autonomous Data Warehouse (ADW). FAW extracts and loads data from your Oracle Fusion Applications Cloud into an instance of ADW and provides a semantic model with prebuilt KPIs, dashboards, and reports in OAC. The Oracle document here lists the currently regions available for provisioning. This post describes the OCI...
Validated September 30, 2020 Introduction Oracle Analytics for Applications (FAW / OAX) is built on top of Oracle Analytics Cloud (OAC) and is powered by Oracle Autonomous Data Warehouse (ADW). FAW...
Reading time: < 12 minutes Introduction Choosing your data preparation approach is a critical task that should not be missed during data migration. In...
Reading time: < 12 minutes Introduction Choosing your data preparation approach is a critical task that should not be missed during data migration. In continuation to my previous blog “Options for migrating clean data to Fusion Applications Cloud”, I would like to expand on the option of leveraging Oracle Enterprise Data Quality tool to analyze, standardize, cleanse, and de-duplicate your source data to cloud. Traditionally, most of the implementation projects will design the...
Reading time: < 12 minutes Introduction Choosing your data preparation approach is a critical task that should not be missed during data migration. In continuation to my previous blog “Options for...
Introduction into existing BICC Automation BI Cloud Connector offers a broad bandwidth of options to automate the extraction of data. In various other blogs...
Introduction into existing BICC Automation BI Cloud Connector offers a broad bandwidth of options to automate the extraction of data. In various other blogs we’ve shared information how to download extraction files, log information or to delete extracts. Those scripts can be scheduled on the client side via the tools of choice. The built-in capabilities in BICC provide more and more automation as the product evolves. Scheduling frequent extraction runs was a feature that...
Introduction into existing BICC Automation BI Cloud Connector offers a broad bandwidth of options to automate the extraction of data. In various other blogs we’ve shared information how to download...
Note: this article was originally written for Oracle Data Integrator Cloud Service and was adapted to cover Oracle Data Integrator Marketplace, the current...
Note: this article was originally written for Oracle Data Integrator Cloud Service and was adapted to cover Oracle Data Integrator Marketplace, the current offering for ODI in the Cloud. The #1 benefit of ODI Marketplace (ODIMP) is that when you provision your ODI image, the product is ready to use: the repository is created for you automatically, you studio is ready to connect to that repository, and your agent is up and running, ready to execute your mappings. The one...
Note: this article was originally written for Oracle Data Integrator Cloud Service and was adapted to cover Oracle Data Integrator Marketplace, the current offering for ODI in the Cloud. The #1...
