Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have recently been asked to develop an application that will have to integrate with Sage Line 50 financial software.
I've done some googling and I am surprised at the lack of info on interfacing with Sage from Java or. Is Sage such a black box that you need to sign up to a Sage Developer program before you get any info?
Are there any open source options to allow apps to talk to Sage? Theres a new methodology Sage are moving to called SData. The long term aspiration is that SData will provide full CRUD facilities and simplify integration between different Sage programs of which there are many! However the developer programme does give you free copies of the Sage software for development purposes, so I can see the benefits if your business is Sage integration. I've done quite a bit with Sage Line 50 V9 a couple of versions old, I know.
The driver is however read-only which may or may not be an issue to you. Hope this of some use. I'm not surprised that you need to join the developer program - Sage is a traditional closed source commercial application - it's unlikly to have open source options available for it.
Joining the dev program used to be free for Sage customers, which the people you are working for should be, surely? Learn more. Asked 11 years ago. Active 5 years, 11 months ago.
Viewed 36k times. Any info appreciated.This API is designed for use by developers of image analysis and data mining tools to directly query the public resources of TCIA and retrieve information into their applications.
The API complements the existing web interface but eliminates the need for users to visit the TCIA web pages to select and download images then upload them into their viewing and analysis applications. There is no software that an application developer needs to download in order to use the API.
The application developer can build their own access routines using just the API documentation provided. If you are interested in using the API and have any questions, please contact us at help cancerimagingarchive. Version 2 Documentation for Version 2 can be found here. Returns a set of Patients that have been added to a specified collection since a specified date.
Returns a set of Studies that have been added to a specified collection, and optionally to a patient since a specified date. This information is provided as a JSON document and includes:.
Example: Let us say we wanted metadata for the getPatientStudy query from our earlier example. Evaluate Confluence today.
Space shortcuts How-to articles Troubleshooting articles. Child pages. User Guides. Browse pages. A t tachments 4 Page History. Dashboard Wiki User Guides. Jira links. The getSeries API has been modified to include new query parameters. Simply call the RESTful endpoints. The server replies with a response that either contains the data you requested, or a status indicator. See examples. Therefore this is required only in instances where multiple return types are supported.
T he returned metadata conforms to the following JSON schema. No labels.Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning ML models quickly.
SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone.
SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive.
All ML development activities including notebooks, experiment management, automatic model creation, debugging, and model drift detection can be performed within the unified SageMaker Studio visual interface. For example, make updates to models inside a notebook and see how changes impact model quality using a side-by-side view of your notebook and training experiments. Managing compute instances to view, run, or share a notebook is tedious.
Now available in preview, Amazon SageMaker Notebooks provide one-click Jupyter notebooks that you can start working with in seconds. The underlying compute resources are fully elastic, so you can easily dial up or down the available resources and the changes take place automatically in the background without interrupting your work.
SageMaker also enables one-click sharing of notebooks. All code dependencies are automatically captured, so you can easily collaborate with others. You can choose from dozens of pre-built notebooks within SageMaker for different use cases. You can also get hundreds of algorithms and pre-trained models available in AWS Marketplace making it easy to get started quickly. Generate a sharable link without manually tracking dependencies, to reproduce the notebook code.
Typical approaches to automated machine learning do not give you the insights into the data used in creating the model or the logic that went into creating the model. As a result, even if the model is mediocre, there is no way to evolve it.
SageMaker Autopilot automatically inspects raw data, applies feature processors, picks the best set of algorithms, trains and tunes multiple models, tracks their performance, and then ranks the models based on performance, all with just a few clicks. The result is the best performing model that you can deploy at a fraction of the time normally required to train the model.
You can explore up to 50 different models generated by SageMaker Autopilot inside SageMaker Studio so its easy to pick the best model for your use case.API Aggregation and Integration with Cloud Elements
SageMaker Autopilot can be used by people without machine learning experience to easily produce a model or it can be used by experienced developers to quickly develop a baseline model on which teams can further iterate.
Automatically create machine learning models and pick the one that best suits your use case. For example, review the leaderboard to see how each option performs and pick the model that meets your model accuracy and latency requirements.
Successful machine learning models are built on the shoulders of large volumes of high-quality training data. But, the process to create the training data necessary to build these models is often expensive, complicated, and time-consuming. Amazon SageMaker Ground Truth helps you build and manage highly accurate training datasets quickly. Ground Truth offers easy access to labelers through Amazon Mechanical Turk and provides them with pre-built workflows and interfaces for common labeling tasks.
Additionally, Ground Truth continuously learns from labels done by humans to make high quality, automatic annotations to significantly lower labeling costs. Amazon SageMaker Experiments helps you organize and track iterations to machine learning models. Training an ML model typically entails many iterations to isolate and measure the impact of changing data sets, algorithm versions, and model parameters.
You produce hundreds of artifacts such as models, training data, platform configurations, parameter settings, and training metrics during these iterations. Often cumbersome mechanisms like spreadsheets are used to track these experiments. You can work within the visual interface of SageMaker Studio, where you can browse active experiments, search for previous experiments by their characteristics, review previous experiments with their results, and compare experiment results visually.
Track thousands of training experiments to understand the accuracy of your model. For example, view in a chart of how different time series datasets impact model accuracy.
The ML training process is largely opaque and the time it takes to train a model can be long and difficult to optimize.With version 6. We did this in support of our SData implementation which we wrote in Java.
This then provides better performance, as well as allows us to compile this part of the system for Linux with no Microsoft dependencies. All the Sage Business Logic objects have the same API, this makes it easier for us to produce these different APIs to facilitate interoperability with all sorts of external systems, allowing the programmers there to write code in a natural manner where any required interop layer is provided by us.
This is a one way communication where we only use this to call the DLLs, we never have the DLLs calling our Java code this direction is often dangerous and leads to the problems often encountered with JNI. Our JNI code handles all the data conversions between Java and C as well as provides exception handling to trap and handle exceptions that can happen in C code like bad pointers.
Generally to add custom programming to SData feeds you write Java classes that inherit from our standard SData classes to provide this. When you interact with the Views in this environment you use this Java API, but all the libraries are already included and all the details of signing on are handled for you.
The framework starts you off at a point where you can directly open and call Views. So that you can use this API directly in isolation without requiring any other framework. First to use the Java API, you need to include its jar file into your project. This changed a bit between version 6. For 6. Then you need to import the classes into any source file that uses them via:. Once you have these things included in your Java project you can start creating objects and calling methods.
However due to security you first must sign-on to a session and then create all other objects from this session. You can find all the classes, methods and properties here. A key benefit to joining this program is access to this wiki which contains all the developer documentation that we produce.
The ProgramSet and SharedDataSet are used when we deploy in a hosted configuration and run multi-tenant. In this case they must be setup correctly by the system to configure which tenant this session is for.
In most normal on-premise applications the indicated calls are fine to give the one default tenant that exists. Init call that comes first. This is true, but the information is required regardless. Now that you have a program you can start opening and using Views. Like many things I started with macro recording to get the right Views and some syntax. Anyone familiar with Sage ERP macro recording will recognize the style and variable names in the following method.
This method assumes there are class variables for the program and session that were created as indicated above. The key point of the following example is to show how to open Views, compose Views and then use the Views.
Notice that you can explicitly close things by calling the dispose method.If you are new to Postman, work through the tutorial first. When implementing new APIs, Sage Intacct provides generic or open functions that can operate on multiple types of objects.
The older, object-specific functions are labeled as legacy in the documentation, which means they are typically not enhanced. There are no plans to deprecate legacy functions, and in fact, there are cases in which they are the only functions available.
If an object or function is not included in the API documentation, it is likely not yet supported and subject to change. You can post an idea on the Sage Intacct Community or log a support case to inquire about the status of such objects and functions. This section deals with providing information about a company or working with consoles.
These objects are mainly for system administrators, but end users do interact with some of these. The General Ledger is where you create and maintain accounts, journals, and financial reports.
You use the General Ledger to post journal entries, design and run reports, create budgets, and more. Other Sage Intacct subscriptions automatically post transactions to the General Ledger in real-time.
Cash Management is used to manage your cash accounts, including banks, savings institutions, and charge cards—all in one centralized location. Accounts Payable AP enables you to keep track of your vendor accounts and bills. Basically, you enter and edit your vendor transactions, and then pay them.
Accounts Receivable AR is used to manage a full AR cycle, including customers, receivables transactions, and generating reports. Purchasing is used to automate purchasing transactions and monitor and manage merchandise acquisition.
Order Entry involves the creation and management of customer orders. Activities related to order entry include managing data related to customer orders, processing order transactions, and running reports on customer orders.
Use Inventory Control to track your merchandise and maintain and analyze your inventory. This subscription is fully integrated with the other Sage Intacct subscriptions, particularly Purchasing and Order Entry. Project and Resource Management enables services companies to automate many of the functions of financial project management. There are two consolidation methods: Global Consolidations and Advanced Consolidations. The fundamental difference is based on company data lists-such as the chart of accounts-and whether or not entities can share them.
Contracts and Revenue Management provides an automated way to address the sweeping accounting changes included in ASC Revenue from contracts with customers. Customization Services enables you to customize Sage Intacct standard objects in your company to suit your business needs.
Platform Services is a tool set that developers can use to extend Sage Intacct with custom objects and applications. Data Delivery Service DDS enables companies to extract massive amounts of data from Sage Intacct and send that data to a cloud storage location.
Toggle navigation.We can accommodate customers using just about any version of Sage and its predecessors. Because we completely understand the Sage file structure and how to integrate using the third-party API, we can perform most any type of integration necessary for Sage customers.
We specialize in Sage process automation, from intercompany transactions to tokenized credit card integration.
Sage 300 Newsletter – April 2016
IN-SYNCH is infinitely versatile, able to be expanded into complex solutions that will pay dividends in return on your investment. We can offer peace of mind to our customers, knowing IN-SYNCH has been running on Sage customer systems for over 20 years and our third-party connectors have been working perfectly now for ten years.
Plus, security will never equal down time.
IN-SYNCH seamlessly integrates and synchronizes all relevant data between Sage and the third-party system, whether the data originates in Sage or the external system. Sage ERP and your third-party system — whether e-commerce shopping cart, CRM database, or warehouse management system — are provided with automatic, real-time changes and updates as they occur.
Our solution allows for the two systems to run independently, so if one happens to be down for maintenance, the other system stays up.
Sage50 API in C#.NET
The two will sync up automatically. IN-SYNCH uses the most efficient data mirroring methods available so you can receive orders, new customers, changes, and updates automatically as they happen with maximum speed. Let our in-depth knowledge and decades of experience save you time and money. Skip to content. Top menus. Sage Integrations.
Years in Business. Real-time Synchronization. Lightning Fast. Click to Zoom. Request a Demo.That way, Sage customers can take advantage of several software platforms within a single category rather than just individually, one-to-one. For vendors, the fact that the API connections are pre-built virtually eliminates the need for custom-coded integrations between Sage products and third-party software.
According to Mark Geene, Cloud Elements CEO and co-founder, who spoke with Small Business Trends by phone, the partnership represents part of the growing trend away from information isolation toward cloud-based connections that make data sharing easy and seamless. It lets Sage customers have one unified experience between two sets of software. Cloud Elements for Sage will provide a way for independent software vendors to develop new third-party product add-ons. A payment hub is also on the development roadmap and will launch shortly.
The platform also gives them a set of tools they can use to create custom integrations that may not already be in the Sage directory. Visit the Sage website for more information on pricing, documentation and details on how to get started with Sage Integration Cloud.
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