Announcing Oracle Transaction Manager for Microservices Free

Oracle is pleased to announce the availability of Oracle Transaction Manager for Microservices Free. This new product provides distributed transaction coordinator services for a variety of transaction protocols. Using this product, microservices developers can easily ensure the consistency of data across their microservices, even in the presence of failures. The coordinator itself is a microservice and readily deployed into a service mesh framework such as Istio/Envoy with Kubernetes.

As application architecture moves from monoliths, through SOA, to today’s microservices, issues crop up that monoliths and some SOA based applications don’t need to worry about. One area in particular is around data consistency. In a monolithic application, typically all data is stored in a single database. Consistency across tables is managed by local transactions to ensure the data remains consistent, i.e., an update to two tables either both succeed or neither of them succeed. Moving to microservices where each microservice maintains its own database, local transactions are no longer sufficient to provide consistency. This is where distributed transactions become a requirement.

Distributed Transactions

To help ensure data consistency across microservices, a distributed transaction is often used. Distributed transactions try to move a system from one consistent state to another consistent state. They are often utilized to handle the various failure scenarios that can occur in distributed systems, ideally without burdening the application developer with too much work. One of the earliest distributed transaction protocols is the XA two phase commit protocol defined by The Open Group. Using XA, applications can ensure that the updates to multiple data sources can be done while still adhering to the ACID guarantees of a transaction.

Multiple Supported Distributed Transaction Protocols

The initial release of the product is being offered for free and intended to allow developers to start leveraging distributed transactions in their microservice based applications. Support is provided for XA based distributed transactions, Sagas in the form of Eclipse MicroProfile Long Running Actions, and the Try-Confirm/Cancel transaction protocol. Microservice developers are free to choose the distributed transaction protocol best suited for their application based upon their application’s consistency requirements.

This initial free release provides support for microservices developed in:

◉ Java using Jax-RS in Helidon, WebLogic Server, and Spring Boot

◉ Typescript using Express.js

◉ PL/SQL using Oracle Application Express and Oracle REST Data Services

◉ C/C++/COBOL running in Tuxedo and exposed as REST services using SALT

◉ Oracle Blockchain Platform smart contracts

with more languages and platforms to come.

Microservice Based Transaction Coordinator

Transaction Manager for Microservices consists of a microservice based transaction coordinator that can be deployed into a containerized environment such as Kubernetes or Docker Swarm. Provided as well is a set of language specific client libraries that provide APIs and CDI annotations to access the transaction coordinator’s services. These libraries also include request and response filters to automatically propagate transaction context between microservices and enlist called microservices in the transaction.

Focused on Ease of Development

Using the supplied client libraries, applications can easily be extended to support their consistency requirements with a minimal amount of effort. In many cases with a few lines of code modified or added to an existing microservice it will be able to initiate or participate in a Transaction Manager for Microservices managed transaction. The included samples cover different use cases such as financial transactions requiring strong consistency, to making travel reservations with looser consistency requirements. Provided helm charts for Kubernetes, minikube, and Docker Swarm make deploying Transaction Manager for Microservices a simple task taking just minutes. 

Source: oracle.com

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New Autonomous Data Warehouse Enhancements to Streamline Data Preparation and Sharing for Analytics

Oracle Autonomous Data Warehouse (ADW) introduces new capabilities that allow organizations to do more with their data. Do more collaboration by sharing data using open-source Delta Sharing protocol and business models using in-database Analytic Views. Do more data integration using enhanced, analyst-friendly Database Tools. Do more discovery across ADW and data lakes using Excel and Oracle SQL. And, do more timely analytics with accelerators for E-Business Suite, Fusion, and NetSuite that provide ready-to-use data models, KPIs and data integration. These new ADW capabilities help businesses do smarter analytics, including enhancements for:

Business Analysts

Business analysts can set up data marts using Autonomous Data Warehouse quickly without help from IT. ADW provides an end-to-end experience for loading data, transforming data, creating business models, and doing analysis. Business models are supported via ADW's innovative Analytic Views which help to standardize semantics and enable better collaboration across teams. New and recent capabilities include:

◉ Improved data loading with built-in Transforms

◉ Analyze data with a new Excel add-in 

◉ Improved built-in analytics for analysis and visualization within ADW

◉ Tighter integration with Oracle Analytics and Tableau 

Autonomous Data Warehouse’s built-in Data Transform tool makes it easier to work with data.

User can run queries on Autonomous Data Warehouse directly from Excel with new add-in.Caption

Oracle Applications Customers

Oracle Application Accelerators offer comprehensive analytical and reporting capabilities with self-service data discovery and pre-built KPI metrics to enhance existing applications. New enhancements include:

◉ Oracle E-Business Suite Accelerators offers comprehensive analytical and reporting capabilities with pre-built ETL to enable customers to quickly create data warehouses from Oracle E-Business Suite data.

◉ Oracle Fusion Analytics Warehouse and NetSuite Analytic Warehouse, both built on Autonomous Database, provide end-to-end cloud data warehouse solutions for Fusion applications and NetSuite SaaS respectively.

◉ For bespoke data warehouses, Autonomous Data Warehouse's built-in Transforms includes connectors for all leading Oracle and third-party applications

Data Lake Users

Autonomous Data Warehouse is an ideal platform for a data lakehouse. Data scientists and data engineers can work with any data in their data lake using the full power of Autonomous Data Warehouse’s auto scale, industry-leading SQL language, and integration with 3rd party tools and applications. Autonomous Data Warehouse now provides integrated security and access not only to OCI Object Storage, but also to AWS S3, Azure Blob Storage and GCP Cloud Storage.  

All customers with new collaboration capabilities

Autonomous Data Warehouse announces enhancements to data sharing, so that companies can easily share data both within their company, and to outside data consumers. Unlike other cloud data warehouse data sharing, Autonomous Data Warehouse implements the open-source Delta Sharing protocol so that data can be easily shared and consumed across different technology platforms.

Key highlights:

Share data between Autonomous Data Warehouse

◉ Share across companies and across regions / data centers

◉ Secure and governed data sharing: grant, revoke, audit, track

◉ No replication required

Open-standards based data sharing with Delta Sharing API

◉ Autonomous Data Warehouse as Data Provider—share data with any compliant Data Share Data Recipient

◉ Autonomous Data Warehouse as Data Recipient—consume any Delta Share from any compliant provider

◉ Data share access through external tables

Source: oracle.com

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Make Rich Data Even Richer – Use Functions for Custom Data Enrichment in Oracle Analytics Cloud

Oracle Analytics Cloud (OAC) is an enterprise cloud analytics platform built on Oracle Cloud Infrastructure (OCI) that offers built-in capabilities for data enrichment.  In addition, you can perform custom enrichment by using scripts, which takes the native capabilities a step further and allows you to work with data in a familiar coding language. You can use custom scripts to combine columns in your data set, create new columns based on existing data, or bring in additional data from an external source to enhance your analysis – which is the is the use case explored in the attached guide, Custom Data Enrichment with Oracle Functions on Oracle Analytics Cloud.

This guide gives step-by-step instructions for how to build Functions (using the Python SDK) and integrate them with OAC so you can perform custom data enrichment. Specifically, the use case explored here involves adding external weather data to a sales data set. Weather can affect business operations across industries causing disruptions such as facility closures, production delay, demand reduction, and supply chain upset. These disruptions can lead to higher operational and capital costs, and ultimately lower profit. By considering weather when analyzing a company’s data, one can better understand the impact weather patterns in different locations may have on the variability of certain cash flows, and ultimately better plan for future revenue, expenses, and demand.

Here’s a quick preview of the guide to get you started:

1. Prerequisites.

Learn everything you need to know to do this, including creating the ancillary OCI resources, implementing proper Identity and Access Management (IAM) policies, and retrieving the external enrichment data.

2. Creating the function.

Walk through the key steps to create and deploy the function, with step-by-step click paths and images to help guide you.  You’ll be provided with code snippets to test you Function along the way, as well as direct links to a GitHub repository that houses all the code used in this project. 

3. Registering the function

You will be guided step-by-step through the process of connecting your Oracle Analytics instance to your Functions service and registering your Function on OAC. 

4. Invoking the function.

Lastly, go through the final steps necessary to invoke your registered Function directly in an OAC Data Flow to enrich your dataset with custom external data.

5. Troubleshooting.

Helpful tips on how to enable logging to debug possible errors with you code and solutions to a couple common errors you may run in to.

Source: oracle.com

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Oracle Analytics Data Flow Cheat Sheet: Making Sense of Large Amounts of Data

Data Flows in Oracle Analytics enable you to customize, organize, and integrate your data to produce a curated dataset that your users can glean insights from right away. And because it’s a richly functional cloud service, it helps you do this automatically and repeatably so you can streamline your work at the same time that you increase value for your organization. Win-win!

Let’s take a classic example: creating a meaningful view of sales data. To do that, you might merge two datasets that contain quarterly sales data, strip out columns that you don't need, aggregate the value of your sales, and save the results in a new dataset named "Quarterly Sales Summary." But a Data Flow offers much more powerful functionality than merely stripping out unnecessary columns. You can add, join, prepare, and transform data, create machine learning models, apply database analytics, and much more.

"One of our strategic goals is to empower analysts to make large data stores meaningful," says Pravin Janardanam, Director, Product Management, Oracle Analytics. "Oracle Analytics achieves this with a comprehensive set of data preparation, data enrichment, and advanced data transformation capabilities built into Oracle Analytics Data Flow. Data Flow makes it easy to bring together data from various sources and lay a foundation for discovering insights."

With Data Flows in Oracle Analytics, you can run your analyses with no infrastructure to deploy or manage and no required prior knowledge of scripting or tools such as Spark. You can simply use your existing connections and Oracle Analytics fully leverages the power of your backend system automatically. Data Flows make running these transformations easy, repeatable, secure, and simple to share across your enterprise.

Data Flows use a variety of connectors and operations. There are four groups of functions at the heart of Data Flows in Oracle Analytics:

1. Data Ingestion, to aggregate data for analytics.

2. Data Preparation, to organize data in meaningful ways.

3. Machine Learning, to automatically detect meaning in data using Machine Learning algorithms.

4. Database Analytics, to present data in numerical and visual ways that provide new insights.

Each of these groups provides a variety of connectors and operations for handling large data stores. Looking more closely at each group, you see that each provides a number of functions to extract the most value from your data.

1. Data Ingestion

◉ Add Data - Add from hundreds of data source types

◉ Join - Join datasets using matching or all rows

◉ Union Rows - Union all, unique or common rows

◉ Filter - Filter a dataset or add an expression filter

◉ Aggregate - Aggregate and group by columns

◉ Save Datasets - Save data to object storage or the database

◉ Create Essbase Cube - Save data to a new Essbase Cube

2. Data Preparation

◉ Add Columns - Add new columns using one of over 100 functions

◉ Select Columns - Select columns to keep in the dataset

◉ Rename Columns - Rename all columns at once

◉ Transform Columns - Transform columns using one of over 100 functions

◉ Merge Columns - Merge multiple columns using delimiters

◉ Split Columns - Split columns using delimiters and parts

◉ Bin - Create bins for a measure

◉ Group - Group values in a dimension

◉ Branch - Branch the dataset into multiple datasets

◉ Cumulative Value - Calculate cumulative values by measure

◉ Time Series Forecast - Forecast data using ETS, Arima, S Arima

◉ Analyze Sentiment - Analyze emotion from text data

3. Machine Learning

Train Numeric Prediction - Train a model using four algorithm scripts:

◉ CART

◉ Linear Regression

◉ Elastic Net Linear Regression

◉ Random Forest

Train Multi-Classifier - Train a model using five algorithm scripts:

◉ SVM

◉ Neural Network

◉ Naive Bayes

◉ Random Forest

◉ CART

Train Clustering - Train a model using two algorithm scripts:

◉ K-Means

◉ Hierarchical Clustering

Train Binary Classifier - Train a model using six algorithm scripts:

◉ SVM

◉ Neural Network

◉ Naive Bayes

◉ Logistic Regression

◉ Random Forest

◉ CART

Apply Model - Apply an Analytics or Database model.

4. Database Analytics includes both Oracle Database Analytics and Graph functions:

Oracle Database Analytics Functions:

◉ Dynamic Clustering

◉ Time Series

◉ Sampling Data

◉ Un-pivoting Data

◉ Dynamic Anomaly Detection

◉ Frequent Itemsets

◉ Text Tokenization

Oracle Database Graph Functions, including:

◉ Sub Graph

◉ Clustering

◉ Shortest Path

◉ Node Ranking

As you can see, there's great power and functionality that can take you from data collection through data analytics. We're adding more functions to Data Flows to expand our anomaly detection offerings and much more. Many of these features are powered by the intelligence of Oracle Machine Learning algorithms, which is the subject of another blog.

Source: oracle.com

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Oracle 1Z0-1077-22 Certification Benefits: Be an Oracle Certified Professional

If you dread taking your Oracle 1Z0-1077-22 exam or your Oracle Order Management Cloud Order to Cash 2022 Implementation Professional certification, you are not alone.

The Oracle Order Management Cloud Order to Cash 2022 Implementation Professional 1Z0-1077-22 exam validates your knowledge in Order Management Cloud projects and using Order entry functionalities. This exam opens the path to becoming an Oracle Order Management Cloud Order to Cash Implementation Professional.

Industry-recognized 1Z0-1077-22 certification from Oracle helps you gain a competitive edge over other candidates. Moreover, earning a professional certification improves your employability and demonstrates your capability. You need to study and prepare well for this 1Z0-1077-22 exam, and this guide is undoubtedly your way to crack the 1Z0-1077-22 exam and pass it with flying colors.

What Are the Benefits of Acquiring An Oracle 1Z0-1077-22 Certification?

Oracle is one of the world’s largest enterprise software companies that offer the certification course for the most lucrative career in the IT domain. Oracle 1Z0-1077-22 certification is a valid and demanded credential that gets worldwide recognition. With the rise in aspiring candidates applying for jobs in IT industries, a certification from the Oracle Corporation enhances your opportunities of getting recognized and selected and confirms your entry into esteemed companies.

With the intense competition in the job market, certification is mandatory for career growth that enhances your productivity and improves credibility, signifying a benchmark of proficiency, competence, and experience. An Oracle 1Z0-1077-22 Certification is undoubtedly worth acquiring and would greatly value a professional’s career.

Some of the reasons why you might want to consider pursuing one of these certifications are:

1. Better Employability

Considering the widespread acceptance of Oracle 1Z0-1077-22 Certification, it would undoubtedly make you more employable. Depending on the certification you obtain, you will become eligible for a variety of job openings. Moreover, the certificate would validate your skills, showing the employers that you could be an excellent addition to their team.

2. Development of Skills

An Oracle 1Z0-1077-22 Certification course would give you an in-depth understanding of various concepts regarding the technology stacks. It would not only help you develop a strong foundation in this field, but you would also be able to grow your skills to a great extent. Ultimately, it would make you a much more skilled professional with significant expertise.

3. Growth Opportunities with Oracle 1Z0-1077-22 Certification

Better skills automatically result in higher growth opportunities for professionals. You would be able to progress through your career quickly, earning promotions for high-skill roles. Valuable certifications such as these also increase the chances of securing promotions by setting you apart from your colleagues.

4. Recognised with Oracle 1Z0-1077-22 Certification

Oracle offers a digital badge to candidates successfully obtaining the certifications. You may put these badges on your social media profiles, including those you might use for professional purposes. The badges display the skills you have acquired, thus helping you stand out from the crowd. Ultimately, an Oracle 1Z0-1077-22 Certification would help you gain recognition among the community of IT professionals.

5. Better Remuneration

Developing additional skills and validating them through such certifications make you more valuable to a company. Thus, your employer would be willing to pay you more to keep you as a part of the organization. Pursuing an Oracle 1Z0-1077-22 Certification course can help you earn a better salary and more benefits.

6. A Validation of Soft Skills

The very purpose of a certification is to validate your skills and knowledge on the specified topic. However, in addition to the hard skills, it also validates various soft skills you would need to join leading companies. For instance, when a professional with an Oracle Order Management Cloud Order to Cash Implementation Professional certification applies for a job, the employer would know that the candidate is hard-working and willing to learn.

Conclusion

An Oracle 1Z0-1077-22 Certification Exam thus evaluates and challenges your technological abilities, thus; enhancing and accelerating them for a bright career prospect, job stability, and high salary. Oracle products, software, and services are highly scalable and used in large-scale and medium-scale organizations.

Most Oracle products are critical for a business and thus need to be managed and handled by only professionals with the right expertise in the said field. Compared to other certifications, Oracle Database-related jobs are many, and people receive a good salary once appointed. Apart from these, other technological streams wherein Oracle certification proves beneficial.

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DNS in multicloud disaster recovery architectures

Multicloud adoption is rapidly increasing, which means enterprises can take advantage of the best services from each cloud. But at the same time, it can add complexity and new challenges when designing disaster recovery architectures.

A disaster recovery region is a common business requirement to guarantee business continuity in the event of a major disaster. Traditional disaster recovery requires deploying, operating, and maintaining infrastructure in a different geographical region, it is highly complex and expensive. The public cloud’s shared management and pay-as-you-go model is a cost-effective way to implement disaster recovery.

In this blog, we cover the key considerations of domain name system (DNS) design in multicloud environments. We also go over the options to trigger a disaster recovery and provide an overview of the DNS services currently available in Oracle Cloud Infrastructure (OCI).

Multicloud DNS architecture

When defining the multicloud DNS architecture, assessing the impact of the DNS resolution time on the overall application performance or user experience is critical. When a DNS request is performed, unless the DNS resolver has already received and cached the request, the DNS resolution process takes time.

Transactional workloads are particularly sensitive, and in some cases milliseconds of latency can be noticeable. So, setting up mechanisms that provide the lowest latency is important. These mechanisms are conditioned by the underlying DNS architecture, which can be different for public and private DNS zones.

Public zones

The internet DNS architecture is based on a global, decentralized system that performs delegation, from the internet root zone to top level domain to finally authoritative servers where the public zones are hosted. The internet DNS architecture is built on an anycast network, which helps provide lower latency during the delegation process, a basic level of load balancing, and resilience. With the delegation optimization included in the DNS public architecture, OCI offers traffic management steering policies, which contain rules that help serve intelligent responses to DNS queries.

You can use traffic management steering policies to steer traffic to OCI regions, but also to any publicly exposed resources, including other cloud providers and on-premises data centers. Disaster recovery architecture is one of the use cases that steering policies can cover.

Triggering a disaster recovery must be a decision taken by top management, and it isn’t expected to be automatic in high-availability architectures. Regardless, using traffic steering policies simplifies this process because a simple manual failover changes the DNS response of a public domain from an IP in the primary region to an IP in the disaster recovery region.

In addition to the failover steering policy, the following options are available in OCI:

◉ Load balancer: Distributes traffic over several servers to optimize performance

◉ Geolocation steering: Dynamically routes traffic requests based on originating geographic conditions

◉ ASN steering: Dynamically routes traffic requests based on the originating autonomous system number (ASN)

◉ IP prefix steering: Dynamically routes traffic requests based on originating IP prefix

Private zones

A private DNS zone contains data only accessible internally and isn’t exposed to the public internet. Private zones aren’t integrated with the public internet root zone.

Large global enterprises deploy DNS architectures based on a private root zone with domain delegation for their private zones. Enterprises get all the benefits of a decentralized system for their internal services. To achieve this goal, they deploy a private root zone and modify the DNS local resolvers to use this new private root zone, instead of the default internet root zone.

In a multicloud scenario, this point represents a challenge because the DNS services offered by cloud providers allow hosting of internal zones but restrict changing the root zone. By using the native DNS services of public cloud, you can’t define a multicloud DNS architecture based on a private root zone. So, the region or cloud where the private zone is hosted is critical to achieving lower latency and better performance required by modern services. Ideally, the private zone exists in the same cloud where the resources exist and as close as possible to the requester. In a multicloud deployment, each cloud commonly has private zones.

The only option available for resolving the private zones hosted in different cloud is to configure DNS forwarding rules. This static configuration must be configured in the DNS servers of each cloud. Another consideration when defining a disaster recovery architecture is to have separate DNS private zones for the primary and disaster recovery region. This structure permits DNS resolution between the primary and disaster recovery regions and allows end-to-end connectivity tests without impacting production.

The following example shows private zones in a multicloud disaster recovery architecture:

◉ Database region A: region-a.oci.corp

◉ Example type A tecord: test. region-a.oci.corp > 10.0.0.1

◉ Database region B: region-b.oci.corp

◉ Example type B record: test. region-b.oci.corp > 20.0.0.1

◉ Frontend region A: oci-dns.corp

◉ Example CNAME record (Production): test.oci-dns.corp > test. region-a.oci.corp

◉ Frontend region B: oci-dns.corp

◉ Example CNAME record (Disaster recovery): test.oci-dns.corp > test. region-b.oci.corp

The objective of defining a production private zone (oci-dns.corp) in a disaster recovery architecture is to be agnostic to the applications.

Example multicloud disaster recovery scenario

A common multicloud split-stack scenario is where Amazon Web Services (AWS) hosts the frontend and OCI hosts the database layer. For this scenario, we have three main configurations.

Disaster recovery regions for the frontend and database layers

From a technical point of view, this configuration is ideal. The frontend cloud region only has network connectivity and private DNS resolution with the OCI paired region, such as AWS production frontend with OCI production database and AWS disaster recovery frontend to OC disaster recovery database. Each AWS frontend region has a production private zone with CNAME records pointing to the paired region.

To trigger the disaster recovery from a DNS standpoint, you only need to steer the traffic to the relevant frontend layer. In frontend public zones, you can use a manual failover in the DNS traffic steering policy.

Disaster recovery region for the frontend layer

This disaster recovery architecture isn’t ideal but can happen for different technical and commercial reasons.

The diagram depicts the configuration with two regions for the frontend layer and a single region for the database layer. This configuration has important latency concerns because frontend region B can be a long distance from the database cloud region. For this design, you need network connectivity and DNS resolution from both frontend cloud regions to the database region.

To trigger the disaster recovery from a DNS standpoint, you only need to steer the traffic to the relevant frontend region. In frontend public zones, you can do a manual failover in the DNS traffic steering policy.

Disaster recovery region for the database layer

Like with the previous configuration, this disaster recovery architecture isn’t ideal.

The diagram depicts the configuration with one frontend cloud region and a primary and disaster recovery region for the database layer. This configuration has important latency concerns because the frontend region can be a long distance from the database cloud region B. For this design, you need network connectivity and DNS resolution from the frontend region to both database regions.

To trigger the disaster recovery, you must change the CNAME records defined in the frontend production private zone to resolve the records of the database region B.

Source: oracle.com

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Connect Oracle Analytics to Data Sources with Rest APIs

View of the REST API connector in Oracle Analytics

Oracle Analytics allows any user to connect to a REST API Endpoint very easily! This is a game changer for data and analytics users, and this article explains how to use it.

1. What is a REST API?

Wikipedia defines API as "An application programming interface (API) is a way for two or more computer programs to communicate with each other. It is a type of software interface, offering a service to other pieces of software.". The REST definition is this: "Representational state transfer (REST) is a software architectural style that describes a uniform interface between physically separate components."

In summary, the REST API connector in Oracle Analytics allows a user to connect to any website that allows sharing of data through a REST API endpoint.

Some of the most popular API data sources and examples are:

Skyscanner Flight Search

Open Weather Map

Yahoo Finance

2. Create a REST API connection

You start by creating the connection. Open Oracle Analytics, click Create, then select Connection and REST API.

Create a new REST API Connection in Oracle Analytics

Click Import File and specify a JSON file. You can find examples of REST API Connections to download here: Oracle REST API Downloads.

You can also manually enter the information using these steps, which give an example for the REST API for the popular financial website called Financial Modeling.

Prerequisite: Register for a free key at this URL:  https://site.financialmodelingprep.com/developer/docs/#ETF-Holders

A. Enter a connection name: Financial Modeling PSY

B. Enter a description: Financial Modeling

C. Enter the REST base URL: https://financialmodelingprep.com/api/v3/

D. Click the button Add endpoint

E. Rename it from endpoint_xxxx to SPY

F. Replace the relative URL like so: etf-holder/SPY?apikey=XXXXXXXXXX

Important: You must register for a free key on Financial Modeling and replace it after the apikey=XXXXXXXXXX

Nota Bene: You can add as many endpoints as you need. A connection can have multiple API calls and requests.

After specifying the appropriate values, click Save.

Alpha API connection using manual input with an endpoint

3. Create a dataset

Create a dataset by clicking the connection that you just created (named Financial Modeling Spy in our example and available in the data menu).

As shown in this image, click Financial Modeling, then Schemas, then AUTOREST, and finally on the "SPY" table. The REST API data is automatically loaded in a table.

Create a dataset from a REST API connection

Now that Oracle Analytics made an API call, you can retrieve the data in real-time from the REST API endpoint. In this example, all the stocks are part of the S&P500!

You can also add other datasources such as Microsoft Excel files, SQL Server tables, and more. You can join them directly in this interface and prepare your data type and metadata.

4. Create your workbook

Now that you've finished preparing your data, you can name your dataset, click Save, and click Create Workbook at the top right.

You can now drag and drop your attributes and measures directly onto the canvas or use Auto Insights to discover new data visualizations using Machine Learning and AI:

Auto Insights on a REST API Data Source

Remember that thousands of REST APIs are available on the public internet! You can use them to create amazing data visualizations.

Find more information about REST API connections for Oracle Analytics here: https://docs.oracle.com/en/cloud/paas/analytics-cloud/acubi/create-dataset-rest-connection.html

Source: oracle.com

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How to leverage custom scripts in your Oracle Analytics Cloud data flow

The data flow capability in Oracle Analytics Cloud (OAC) offers a variety of tools that allow end-users to ingest data, perform data preparation, and produce curated datasets in standardized workflows. Business analysts and end-users often want greater control when performing data preparation tasks. In these situations, the custom script feature within data flow gives you greater control and flexibility over specific data processing needs.

Custom Script Overview

A custom script is another term used to describe an Oracle Cloud Infrastructure (OCI) Function. An OCI Function is a fully managed Functions-as-a-Service (FaaS) platform that allows developers to write code in Java, Python, Node, Go, Ruby, and C#. OAC offers a way to leverage these functions in data flows to customize your data preparation workflows. There are many use cases where a custom script in a data flow could be very powerful. For example, you could call an API to add new information to an existing dataset, perform custom date formatting, or implement a custom data transformation.

Read More: 1Z0-083: Oracle Database Administration II

The remainder of this blog highlights a custom script use case which involves imputing, or replacing, missing values with new values. Dealing with missing values is one of the most important steps in preparing data for machine learning and reporting. There are many ways to address the issue of missing values, but for the purposes of this blog, you'll learn the high-level steps of using a missing-value imputation script that replaces the missing values in our data with values it infers from existing values. 

Custom Script Use Case

To begin, create a FaaS script that contains the logic to fill in missing values with the column mean. There are a number of requirements to follow when writing the script to ensure that it's compatible with OAC. For example, the script must use a variable called 'funcMode.' In short, Oracle Analytics sends a request to register the function when funcMode=describeFunction, and it sends a request to invoke the function when funcMode=executeFunction. For a more in-depth description of how to use the funcMode variable in your function code, refer to this link. 

Once you've created the script, create an application within OCI Developer Services, and deploy the function within the application. The image below shows an example of what you should see once you have deployed the function within the application.

For an OCI function to be compatible with OAC, it must contain an oac-compatible tag. Add the tag to the function directly from the OCI console, as shown in the image below.

Once you've deployed the function within the application with the proper formatting requirements and have ensured it contains the oac-compatible tag, you must register the script in OAC to use it in a data preparation step in a data flow. Follow the steps in this link to first create a connection to your OCI Tenancy, and then to register the function in OAC. Verify that the function has successfully been registered in OAC by navigating to the Scripts tab within the Machine Learning section of OAC.

After successfully registering the function in OAC, invoke the function in a data flow. Create a data flow, supply input data that works with the parameters that you've specified in the function, optionally add more transformations, and save the output. This example shows a 2-column input dataset with a date column and a revenue column, with missing revenue values. The image below shows a sample of the input data and the missing records, as indicated by the red arrows. Note that not all of the records are visible in the image.

Next, add the Apply Custom Script step to the data flow, select the script that you registered in OAC, and specify the parameters that you want to send to the function. In this example, the revenue column was the column that contained missing values, so revenue was included as the parameter. Based on the function created, a new column was returned with the missing values filled in.

Optionally specify formatting changes or data transformations to fit your preparation requirements, and save the output dataset. Finally, save and run the data flow to create a clean dataset ready for machine learning and reporting!

Source: oracle.com

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Oracle Fusion Analytics Warehouse makes Payroll Analytics available in Beta

Note: Payroll is in Beta, which is by invitation for Fusion Analytics customers. If you would like to participate, please reach out to us on the Fusion Analytics Forum on Cloud Customer Connect, or contact your Oracle account manager.

Human Capital Management (HCM) accounts for a good chunk of operating costs for most organizations. Between salaries, bonuses, and benefits packages, the cost of keeping a workforce healthy, motivated, and rested can easily surpass the cost of other overhead. Understanding the impact of payroll on a company's balance sheet is enormously important to its success. The ability to access and analyze payroll information can have great benefits for identifying trends and needs, ultimately paving the way for running a lean and effective business.

Many customers have approached us to request support for further analysis of payroll data and we've built some features into Oracle Fusion Analytics Warehouse (FAW) to do just that. With the December release of FAW (21.R3), you can access more than 40 prebuilt measures that are based on payroll run balances that support time dimensions and don't rely solely on pay-period based reporting.

Payroll can account for as much as 70 percent of an organization's HCM data and in large organizations, these data stores can be both vast and disparate. Organizations must also account for hugely different laws across the globe. The challenge of creating powerful ways of reporting on such large volumes of complex data both quickly and efficiently is one that we take very seriously, and one which we find FAW increasingly well-equipped to handle. Oracle Cloud Payroll customers might be familiar with using OTBI for reporting. FAW complements transactional reporting and extends it, offering prebuilt functionality, improved performance, and streamlined analytics. 

The key differentiators of subject areas for payroll management in FAW over Oracle Transactional Business Intelligence (OTBI) are:

1. Payroll Activity reports generated after every payroll process require significant manual effort to segregate reporting for different balances. Also, reporting across multiple pay periods is very difficult due to performance issues faced on running real-time queries on a database. In FAW payroll subject area, the performance issue is addressed by virtue of the Oracle Autonomous Data Warehouse architecture and support for time dimension. Over 40 prebuilt measures in the FAW Payroll subject area resolve aggregate reporting across any time attributes and other dimensions.

2. The anchor date in the subject area is considered the payroll process date, rather than the pay-period end date as used in OTBI. As such, all assignment attributes on the process date are considered for any reporting. Any changes in worker assignments effective after the payroll process date and before the pay-period end date don't alter reporting and support “as-was” reporting. However, back-dated or retrospective changes effective before the payroll process date are supported for "as-was" reporting to a certain extent. In this scenario, when the warehouse is refreshed completely, “as-was” reporting isn't supported. This happens because all assignment snapshots are re-created, and source Oracle Cloud HCM assignment data doesn't have details at the time of actual payroll processing.

3. FAW payroll subject areas provide the ability to report on the reversal process and impacted balances, which isn't supported in OTBI.

Key points about FAW payroll subject areas:

1. With the first FAW payroll subject area, all prebuilt and derived measures are supported for US legislation only. However, any other legislation-based customers can still use the subject area. You can use the base measure of the balance value to write various formulas.

2. All period-to-date measures are based on the Gregorian calendar only. To use any period-to-date measure, you must pull the time dimension into the query; otherwise it results in blank (that is, null) values.

3. Certain attributes (such as Area Code (1,2,3,4 ) and Context Code (1,2,3,4)) aren't supported in FAW in this release. As such, reporting on these attributes isn't possible.

4. For using the “Balance Value” base measure, be aware of the base-category and base-dimension values. Otherwise, the balance value gives you an aggregate value across all base-category and base-dimension values. 

1. FAW job group and subject area duty role details are described in the following table:

Duty Role Name	OA4F_HCM_PAYROLL_ANALYSIS_DUTY
Duty Role Code Name	Payroll Analysis Duty
OOTB Mapping to Groups	Payroll Administrator Payroll Manager
Data Role	FAW HCM View All Data Role

2. Job groups grant “all data” access. For securing payroll user access by any other than the prebuilt contexts (business unit, department, legal entity, or country), you must set up a custom job group or update the default job group data role mapping.

3. Line Manager security and Line manager hierarchy aren't supported in the FAW payroll subject areas.

4. Find details about prebuilt measure in this Customer Connect post: FAW HCM — Workforce Rewards — Payroll Management — Preview Of Prebuilt Measures.

Default cumulative balance measures by base category and period-to-date balance measures

The tiles at the top of Figure 1 are some of the default measures that correlate to FAW subject areas. In regard to balances, OTBI provides only pay-period based reporting because of its lack of time-dimension support in its Payroll Balances subject area. In contrast, with this FAW payroll offering, you can report on data on year-to-date, month-to-date, and quarter-to-date balances across any time period and any dimension. Our tests with these dashboards have shown them to load quickly even when accessing large data stores.

Figure 1.

Figure 1a.

These tiles can display your spread of earning — across regular employees and temporary employees or even different payrolls — in just a glance. The same analysis can also be performed across any other dimensions.

This provides quarter-to-date, year-to-date, and month-to-date data, easily selectable in a self-service way. Suppose that you have monthly payroll customers and you want to see your quarter-to-date balance. You don't have to perform multiple inline selects as you would in Oracle Business Intelligence Publisher or OTBI. You can prepare a report in just about one mouse click. 

Suppose that you have biweekly payrolls and you want to see the aggregate value on a monthly basis. In Figure 2, the payroll dates were July 1st and 31st. The fifth column (Supplemental Earnings (MTD)) automatically calculates the total payroll cost for supplemental earnings in the month of July. 

Figure 2.

With time-dimension support in FAW, you can create an analysis to view trends across any time period. Figure 1 shows Total Standard Earning , Supplemental Earnings for a selected Year , across all pay periods.

For example, you might ask the question: "What are the earning trends across different departments or any other dimension within a specific year?" You can easily get answers to such questions with visualizations. (See Figure 3a.)

Figure 3a.

If you're a payroll analyst, you might not be interested in looking at trends, but instead at how your cost is increasing across pay periods. You might want to pinpoint any anomalies (such as if somebody is getting paid more overtime and why). These types of visualizations allow you to discover such insights quickly and then to drill deeper into them to guide your business. For example, this visualization shows the growth rate and variance for supplemental earnings across years and months.

Figure 4.  

Support for multiple tax legislations

Reporting and analyses aren't limited to US payroll legislations. Figure 4 shows the page in FAW for non-US legislations. In this example, you can see employee data listed for Mexico.

Figure 5

With recent FAW release ,  As payroll balances brings a huge volume of data , now customers will be required to setup Balance Group in Cloud HCM environment and FAW pipeline will only fetch balances data as per setup present at balance group. Requried balance group name is - ' Global BI Balance Group'. Sample setup details have been shared via customer connect post : FAW- Payroll - Customer Connect Post Reference

After 1st time pipeline is run and source payroll data is synced to FAW then customer can use Payroll Metric config UI to add additional legislations in OOTB measures and also modify the combination of the formula.

Steps to Use - Payroll Metric Configuration

1. Sign in to your FAW Instance.

2. In Oracle Fusion Analytics Warehouse, open the Navigator menu, click Console, and then click Data Configuration under Application Administration.

3. On the Data Configuration page, under Global Configurations, click Reporting Configurations.

4. On the Reporting Configurations page, under the Advanced tab, click Payroll Metric Configuration.

5. From the Payroll metrics, select a metric, and click Add Legislation to specify a Legislation, click the dropdown arrow to select the Balance Category, and click the Add Dimension icon to select a Balance Dimension.

6. Click Save.

Oracle Fusion Analytics helps create detailed reports, customized for each user, long after payroll processing is completed. It provides a quick look into deeply stored data, saving you from diving into the massive reporting files of yesterday. Now you can have the answers to your specific questions delivered to your browser to guide you in your most critical business decisions. 

Source: oracle.com

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