How can you seamlessly automate data integration in Anaplan?
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Efficient data integration is the backbone of maintaining accurate and timely models in Anaplan. Businesses today have a variety of tools and approaches to automate data workflows, from built-in solutions like Anaplan Connect and Cloudworks to custom solutions using the Anaplan API. This post will explore different methods, including ETL tools, Anaplan Connect, Cloudworks, and how to build a Python-based solution using the Anaplan API.
Integration Methods for Anaplan
1. Anaplan Connect
Anaplan Connect is a command-line tool provided by Anaplan for automating data imports, exports, and process execution. It’s lightweight, easy to set up, and ideal for batch data integrations.
Use Case:
- Automating nightly data imports from a database or flat files into Anaplan.
Setup Steps:
- Download and install Anaplan Connect.
- Configure a batch script that includes your Anaplan API credentials and the desired import or export actions.
Advantages:
• Simple and cost-effective.
• Best for scheduled batch updates.
2. Anaplan Cloudworks
Anaplan Cloudworks is a native integration tool within the Anaplan platform, allowing users to connect external systems (such as cloud data stores or data lakes) to Anaplan without coding.
Use Case:
- Synchronising data from cloud platforms like AWS or Azure into Anaplan in near-real-time.
Setup Steps:
- Access Cloudworks from within Anaplan’s user interface.
- Set up a new integration flow by selecting your cloud provider and configuring the data flow.
Advantages:
- Fully native to Anaplan, no external installations required.
- Excellent for seamless scheduled integrations with cloud platforms.
3. Custom Integration Using Anaplan API and Python
For businesses that need more flexibility, custom solutions using the Anaplan API and programming languages like Python offer full control over the data integration process.
Use Case:
- Real-time or batch integration where data is pulled from custom applications or APIs.
Setup Steps:
- Install the Anaplan Python SDK or write custom Python code using the Anaplan REST API.
- Authenticate using your Anaplan API credentials.
- Send requests to import, export, or run processes in Anaplan.
Advantages:
- Fully customisable, works with any data source.
- Ability to handle complex workflows, triggers, or real-time integration.
4. ETL Tools
ETL tools such as Informatica, Dell Boomi, and MuleSoft remain popular for automating large-scale data integrations. These tools provide powerful data transformation features and connectors for a variety of systems, making them ideal for enterprise-level workflows.
Use Case:
- Automating data pipelines between ERP systems (SAP, Oracle, etc.) and Anaplan.
Advantages:
- Extensive data transformation and cleansing capabilities.
- Integration with a wide range of systems.
Anaplan Data Orchestrator
Anaplan Data Orchestrator (ADO) is Anaplan’s native tool for data integration and data management, introduced in October 2024. It connects to source systems, stores and transforms the data, and loads it into your Anaplan models, all within Anaplan. Integrations can be scheduled from ADO, or run on demand from Workflow or a UX page.
Use Case:
- • Automating data pipelines between different source systems and several Anaplan models, with transformation and data quality checks handled inside Anaplan.
Setup Steps:
- Ask your tenant administrator for the integration administrator role, which you need to access Data Orchestrator.
- Import source data through a connector, from an Anaplan model, or from a local file.
- Create a transformation view to restructure and enrich the dataset.
- Create a link or pipeline between the dataset and your Anaplan model, then push the data.
- Use the Data Orchestrator map to check how the objects relate.
What it does:
- Connect. No-code connectors bring data in from SAP, Salesforce, Snowflake, Databricks, Oracle, Microsoft SQL Server, MySQL, PostgreSQL, Google BigQuery, Amazon S3, Azure Blob Storage, Google Cloud Storage, SFTP and Anaplan Financial Consolidation.
- Store. Source data sits in one place. Several models draw on the same dataset, instead of each keeping its own copy.
- Transform. Transformation views filter, join and restructure data into the shape your models need. They are saved, so they repeat on every run.
- Validate. Pipelines can check each row for missing values, wrong data types, out-of-range figures and pattern mismatches. You decide whether bad rows are removed or the load stops.
- Distribute. Links and pipelines push lists, hierarchies and module data into your models, matching on name or code.
- Trace. A data map shows the route from source to model, including every transformation on the way. When someone asks where a number came from, you can show them.
| Released | What’s new | Why it matters |
|---|---|---|
| July 2026 | Validations in pipelines | Catch dirty data before it reaches a model, and download the failed rows for review. |
| July 2026 | Model objects and datasets as pipeline targets | Pipelines load modules, lists and hierarchies directly. Staging to a dataset supports a configured delta load of changed rows only. |
| July 2026 | Amazon S3 as a source and a target, a remove-duplicates step, column automapping | Less manual set-up, and files can flow back out to S3. |
| July 2026 | Virtual datasets | Share common data across Dataspaces without copying it. |
|
Writeback to Snowflake, and an Oracle Database connector |
Plans flow back to your data warehouse, not only in from it. |
|
|
Dataspaces |
Access-controlled partitions, so teams keep their data separate inside one Data Orchestrator. |
Anaplan has also signalled what comes next: a two-way Databricks connector, and writeback to SQL Server, MySQL and Google BigQuery. These are stated plans, not commitments, so check the latest release notes.
Advantages:
- Data integration and transformation handled within Anaplan.
- Orchestration of data transfer to Anaplan models.
- Prebuilt connectors to a wide range of systems.
- One governed source of data shared across models.
- Data quality checks before data reaches a model.
- End-to-end lineage, from source to model.
- Writeback from Anaplan to external systems such as Snowflake and Amazon S3.
Things to consider:
- Licence. Data Orchestrator is a paid add-on, not part of the core subscription. Confirm what your contract includes.
- Ownership. Name an owner for each dataset and transformation. A lineage map shows where a number came from. It doesn’t decide who is accountable for it.
- Standards. Anaplan’s Planual now has a chapter on Data Orchestrator, covering how to connect, convert, catalogue and consume data.
- Scope. Not everything has to move. A Cloudworks or Anaplan Connect flow that works, and that someone owns, may not need replacing.
| Method | Best for | Advantages |
|---|---|---|
| Anaplan Connect | Batch imports and exports | Simple, low-cost, no coding needed |
| Anaplan Cloudworks | Cloud-based real-time sync | Fully native to Anaplan, real-time |
| Custom Python & API | Full flexibility and control | Highly customisable |
| ETL Tools | Enterprise-level integrations | Powerful transformation features |
| Anaplan Data Orchestrator | Enterprise-level integrations | Powerful transformation features and data load capabilities within Anaplan |
Conclusion
Each of these integration methods has its strengths depending on the business’s needs. For lightweight batch integrations, Anaplan Connect is a cost-effective solution. Cloudworks is ideal for seamless cloud-based data integration, while custom Python scripts using the Anaplan API provide full flexibility. For large enterprises, ETL tools and ADO offer powerful data transformation and automation capabilities. ADO also keeps connection, transformation, validation and lineage inside Anaplan, so data can be traced from source to model.
Choosing the right tool for your Anaplan integration depends on the scale, complexity, and real-time requirements of your data flows. With these methods, you can automate data processes efficiently, ensuring your Anaplan models are always current and reliable.
FAQs about Data Integration in Anaplan
What to read and do next
You can also check out our other frequently asked questions for more information, including:
- How does Bedford’s Demand Application work, and what benefits can it deliver to your organisation?
- How can you leverage Anaplan for effective Scenario Planning and Analysis?
- How does Anaplan ensure forecast accuracy and optimise performance?
Read: Still stuck debating the numbers instead of the decision? Learn how connected planning turns trusted data into confident action in our blog “The planning blindspots most organisations never see.”
Get in touch: Bedford Consulting is Anaplan’s longest-standing EMEA Partner of the Year, with deep expertise in data integration, planning and reporting. To discuss any of the themes in this article, contact our team at info@bedfordconsulting.com.
The Bedford integration team
Bedford Consulting’s Data Integration team specialises in connecting planning models to the data that matters most. Working at the intersection of business, technology, and governance, the team designs and delivers robust data integration frameworks that ensure Anaplan models are accurate, trusted, and scalable across the enterprise.
With deep experience across finance, supply chain, workforce, and sales use cases, the team supports a wide range of integration approaches, including native Anaplan tools such as CloudWorks and Data Orchestrator, as well as API‑based and custom integration patterns. This flexibility allows Bedford to align integration design with each customer’s data landscape, operational needs, and long‑term planning roadmap.
