No-Code On-Prem to Cloud Migration in 2026

Quick Answer

No-code on-premise to cloud migration means moving data from legacy servers, databases, and on-premise applications to cloud destinations — Snowflake, Redshift, Azure SQL, Google Cloud — using visual, configuration-driven platforms that require no custom scripts, no API development, and no dedicated developer resources. Enterprise IT teams use no-code migration tools to connect legacy source systems, automate schema creation and transformation, maintain compliance control throughout the transfer, and begin ongoing incremental sync without writing a single line of code. Sesame Software's customer-hosted architecture handles this end-to-end while keeping all processing inside your own environment.

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Why enterprise IT teams are executing no-code migration now

Legacy on-premise infrastructure is reaching end-of-support across most enterprise environments in 2026. The SQL Server instances, Oracle databases, DB2 on AS400 systems, and on-premise ERP applications that ran core operations reliably for a decade are becoming expensive to maintain, difficult to integrate with modern analytics tools, and incompatible with the elastic compute requirements that AI and machine learning workloads demand.

The traditional alternative — building custom migration pipelines — requires developer resources that most enterprise IT teams do not have available for infrastructure projects. Custom pipelines create maintenance debt that compounds with every schema change and API update. And the documentation that explains why a custom pipeline was built the way it was tends to disappear with the developer who built it.

No-code migration platforms resolve both problems. They eliminate the developer dependency from the migration process entirely, replacing it with visual configuration that any technically competent IT administrator can operate. The migration adapts automatically to source system changes, maintains data integrity throughout the transfer, and transitions seamlessly from initial historical load to ongoing incremental sync — without any custom code to maintain.

Step 1: Assess your on-premise environment and define migration scope

Before touching any migration tool, conduct a structured assessment of your on-premise environment. The assessment prevents the two most common migration failures — discovering mid-migration that a source system has dependencies you did not map, and arriving at the cloud destination with data that does not match what business users expect.

Inventory every source system. List every on-premise database, application, and data store that contains data you may need in the cloud. Include systems that business users interact with directly — ERP, CRM, HR systems — and systems that feed other systems — ETL databases, staging environments, data marts. Migrations that scope only the visible systems and miss the underlying dependencies produce cloud environments that are incomplete in ways that surface months later.

Document data volumes and change rates. For each source system, record the total record count per table, the daily change rate — how many records are created or modified per day — and the historical data depth required in the cloud destination. Initial migration load time depends on total volume. Ongoing incremental sync infrastructure depends on daily change rate. Historical data depth determines how far back the initial load must reach.

Identify compliance obligations. Determine which data categories in each source system are subject to regulatory frameworks — GDPR personal data, HIPAA ePHI, SOX financial records, CCPA consumer data. For each regulated category, document the jurisdiction requirement, the retention period, and the data residency obligation. These requirements constrain both the cloud destination selection and the migration processing architecture.

Define the migration scope. Not every on-premise system migrates in the first phase. Prioritize systems by business value, compliance urgency, and technical dependency complexity. Systems with the highest analytical value and lowest migration complexity — flat database tables with few dependencies — make ideal first-phase migrations that build organizational confidence before tackling more complex sources.

Step 2: Select your cloud destination

The cloud destination receives the migrated data and serves as the foundation for the analytics, reporting, and AI workloads that justify the migration. The destination selection shapes every subsequent decision.

Match the destination to your analytics stack. If your BI tools — Tableau, Power BI, Looker — have native connectors to a specific cloud data warehouse, that warehouse is the natural destination. Native connectivity reduces query latency and eliminates the middleware layer that adds complexity and cost. Snowflake, AWS Redshift, Azure SQL, and Google BigQuery each have strong native connectivity to major BI tools — the right choice depends on your existing cloud vendor relationships and your BI team's existing expertise.

Confirm data residency compliance. For data categories subject to geographic processing requirements, confirm that your chosen cloud destination supports deployment in the required region. GDPR-regulated data must be processed and stored within the EU. Some national data sovereignty laws require data to remain within national borders. Select a destination region before configuring any migration pipeline — moving data to the wrong region and then moving it again doubles the migration effort and creates a compliance window during the first transfer.

Set up the destination schema. Create a dedicated database and schema in the cloud destination for migrated on-premise data. A dedicated schema separates migrated data from data loaded through other pipelines, makes access control straightforward, and produces a clean audit trail of what was migrated and when. Sesame Software creates the corresponding tables and schemas in the destination automatically based on the source structure — no manual DDL statements, no schema mapping spreadsheets.

Step 3: Configure source system connections

Connect each on-premise source system to Sesame Software's platform through the visual connection interface. No custom code required for any supported source system — the platform handles connection protocol, authentication, and schema discovery automatically.

SQL Server connections use Windows Authentication or SQL Server Authentication through the native SQL Server driver. Create a dedicated service account with read-only access to the databases and schemas in the migration scope. Do not use an account with administrative privileges — the migration service account needs only SELECT permissions on the relevant objects.

Oracle connections use Oracle's native JDBC driver. Create a dedicated Oracle service account with SELECT privileges on the tables to be migrated. For Oracle environments with Row-Level Security enabled, confirm that the service account's RLS policies allow it to see all records in the migration scope — RLS policies that silently restrict row visibility produce migrated datasets that are incomplete without producing any visible error.

DB2 on AS400 connections use the IBM i Access ODBC driver. DB2 on AS400 is one of the legacy source systems that most cloud-hosted migration platforms have deprioritized — Sesame Software's actively maintained DB2 connector covers the specific versions and configurations that production AS400 environments run, including libraries, physical files, and logical files.

On-premise ERP and application databases — SAP, Microsoft Dynamics on-premise, Oracle EBS — connect through the database layer rather than application APIs. Connect to the underlying SQL Server or Oracle database that the application uses, rather than through the application's own export or API mechanism. This approach provides complete data access, avoids application-layer rate limits, and produces cleaner data than application-layer exports.

After connecting each source, Sesame Software reads the complete schema automatically — every table, every column, every data type, every foreign key relationship — and displays it in the object selection interface. No manual schema documentation required.

Step 4: Run the initial historical load

The initial historical load moves all existing on-premise data to the cloud destination for the first time. This is the largest operation in the migration lifecycle — it may involve years of accumulated data across dozens of tables — and it needs to be executed correctly to avoid needing to repeat it.

Use bulk loading methods for the initial load. Sesame Software uses bulk extraction from source systems and bulk loading to destination systems for initial historical loads — Snowflake's COPY INTO, Redshift's COPY command, or equivalent bulk insert operations for other destinations. Bulk methods process large record volumes significantly faster than row-by-row operations and do not place excessive load on either the source or destination system during the load window.

Schedule the initial load during low-traffic periods. Initial loads consume source system resources — CPU, memory, I/O — proportional to the data volume being extracted. For production databases that users access during business hours, schedule the initial load to run outside peak usage periods. For databases that can be taken offline temporarily, a brief offline window during the initial load may allow faster extraction without competing with user queries.

Monitor load progress continuously. Sesame Software's monitoring dashboard shows extraction progress in real time — records extracted per minute, estimated completion time, current source table, and any errors encountered. Do not assume the load is running correctly without monitoring. Large initial loads can encounter errors — connection timeouts, temporary network interruptions, source system performance degradation — that need to be addressed before they cascade into larger problems.

Do not connect BI tools during the initial load. Business users and BI tools connected to the cloud destination during an incomplete initial load will query partial data. Reports built on partial data produce incorrect results that users may trust before the load completes. Connect BI tools only after the initial load completes and you have validated the migrated data.

Step 5: Validate migrated data before activating incremental sync

Validation is the step that most teams rush and most migrations regret. A validation process that takes an extra day prevents data quality problems that take weeks to diagnose and months to trust.

Run row-count comparisons for every migrated table. Compare the record count in each cloud destination table against the record count in the corresponding on-premise source table. Discrepancies indicate either extraction failures — where some records were missed during the initial load — or scope mismatches — where the on-premise table contains records that the migration configuration excluded. Resolve every discrepancy before declaring the initial load complete.

Spot-check specific records across multiple tables. For a sample of records in each migrated table, compare field values between the on-premise source and the cloud destination. Confirm that data types were preserved correctly — numeric fields that converted to strings, date fields that lost timezone information, text fields that were truncated — and that field mappings are consistent across the entire dataset.

Verify relational integrity. For every foreign key relationship in the migrated schema, confirm that child records resolve correctly to their parent records in the cloud destination. An order record that references a customer ID that does not exist in the customer table is a relational integrity failure that will produce incorrect join results in every query that uses that relationship. Sesame Software's dependency-ordered replication prevents most relational integrity failures — but validation confirms the prevention worked as expected.

Run a business logic validation. Ask the business users who will query the migrated data to run their most important reports or queries against the cloud destination and compare results to what they see in the on-premise source. Business logic validation catches data quality issues that technical validation misses — aggregation differences, filtering logic that produces different results on the cloud data, business definitions that are implemented differently in reports than in the raw data.

Step 6: Activate incremental sync for ongoing cloud migration

After the initial historical load is validated, activate incremental sync to keep the cloud destination current with ongoing changes in the on-premise source. Incremental sync is the mechanism that transforms a one-time migration into a continuous cloud migration pipeline.

Configure incremental extraction based on change indicators. For tables with timestamp columns — LastModified, UpdatedAt, or equivalent — configure Sesame Software to query only records where the timestamp is newer than the last successful extraction cycle. This incremental approach extracts only what changed since the last sync rather than re-extracting the entire table on every cycle.

Set sync frequency based on business requirements. High-priority tables that drive operational reporting or feed time-sensitive analytics sync more frequently than historical reference tables. A sales transaction table that business users query daily syncs every thirty to sixty minutes. A product catalog that changes weekly syncs daily. Sesame Software configures sync frequency per table without requiring separate pipeline instances for different frequencies.

Configure error handling and retry logic. Incremental sync encounters transient failures — temporary network interruptions, source system maintenance windows, destination system connection timeouts — that should not permanently stall the pipeline. Configure Sesame Software's retry logic to handle transient failures automatically and alert the team only when failures persist beyond a configured threshold.

Enable schema change alerts. When on-premise administrators add columns, modify data types, or create new tables, those changes need to propagate to the cloud destination without manual intervention. Sesame Software's automated schema discovery detects source schema changes and updates the cloud destination schema automatically — but alerting the team on schema changes gives IT administrators visibility into what changed and when, which is valuable context for diagnosing any downstream effects on analytics or reporting.

Step 7: Maintain compliance throughout the migration

Cloud migration is not exempt from the compliance obligations that govern the on-premise data being moved. Every regulatory requirement that applied to the data in the on-premise environment continues to apply during and after migration.

Document the migration as a data processing activity. Under GDPR Article 30, organizations must maintain records of processing activities that include data migration. Document the source systems, the data categories migrated, the destination environment, the legal basis for processing, and the retention periods applied. This documentation is the evidence that GDPR supervisory authorities request when investigating data handling practices.

Maintain encryption throughout the migration process. All data in transit from on-premise sources to cloud destinations must be encrypted using TLS 1.2 or higher. Data at rest in the cloud destination must be encrypted using AES-256 or equivalent. Sesame Software enforces encryption at both stages without requiring manual configuration — but verify that your network configuration between on-premise infrastructure and the cloud destination does not include an unencrypted leg.

Keep migration processing inside your own environment. Cloud-hosted migration platforms route data through vendor-managed infrastructure during processing — creating data processor documentation obligations under GDPR and potential jurisdiction issues for data sovereignty requirements. Sesame Software's customer-hosted architecture processes all migration pipeline operations inside your own environment. Data moves directly from your on-premise sources to your cloud destination through pipelines running on your infrastructure — Sesame Software's servers are never in the data path.

Implement role-based access controls on migrated data. Cloud destinations that receive migrated data need access controls that match or exceed the access controls applied to the on-premise source. A database that required authentication and row-level security on-premise should have equivalent controls in the cloud. Configure role-based access on the cloud destination before connecting BI tools and business users to the migrated data.

Hybrid migration: keeping some data on-premise

Not every workload belongs in the cloud. Some data categories have residency requirements that mandate on-premise storage. Some applications depend on low-latency access to data that cloud round-trip times cannot support. Some data governance policies restrict specific categories of data from leaving the organization's own data centers.

Hybrid architecture satisfies cloud migration goals for the workloads that benefit from cloud deployment while keeping on-premise workloads where they belong. Sesame Software's customer-hosted architecture supports hybrid deployment natively — pipelines can replicate selected data categories to the cloud while keeping other categories entirely on-premise, with the same platform managing both sets of pipelines from a single interface.

For regulated data categories that must remain on-premise, Sesame Software can replicate anonymized or aggregated versions to cloud analytics environments while keeping the raw regulated data in on-premise storage. This approach satisfies compliance requirements while still enabling cloud-based analytics on the data that does not require on-premise restriction.

Why Sesame Software is built for no-code on-prem to cloud migration

Sesame Software has been connecting enterprise on-premise systems to cloud destinations for 23+ years. The platform's connector library covers the legacy source systems — DB2 on AS400, Oracle EBS, older Microsoft Dynamics versions, on-premise SQL Server — that most no-code migration platforms have deprioritized in favor of modern SaaS sources.

The customer-hosted architecture processes all migration pipeline operations inside the customer's own environment. Data moves directly from on-premise sources to cloud destinations through pipelines running on your infrastructure — no Sesame Software infrastructure in the data path, no data residency exposure, no third-party access to sensitive data during transit.

Automated schema discovery adapts to source system changes continuously. The patented hyper-threaded replication engine handles migrations at hundreds of millions of records without performance degradation. Predictable annual pricing based on connectors keeps migration costs fixed as data volumes grow — no per-row charges, no consumption-based billing surprises as migration scope expands.

With 23+ years of enterprise data management expertise and a customer base that includes Procter & Gamble, Bank of America, and the U.S. Government, Sesame Software is built for the data volumes, compliance requirements, and operational realities that enterprise on-premise to cloud migration presents.

Talk to a Sesame Software data expert today at sesamesoftware.com.

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Cloud MigrationFAQs

What is no-code on-premise to cloud migration?

No-code on-premise to cloud migration is the process of moving data from legacy on-premise systems — SQL Server, Oracle, DB2 on AS400, on-premise ERP applications — to cloud storage and analytics destinations using visual, configuration-driven platforms that require no custom scripts or developer involvement. Enterprise IT teams configure source connections, destination settings, transformation rules, and sync frequency through a visual interface. The platform handles schema discovery, data extraction, transformation, transfer, and destination loading automatically.

How long does a no-code on-premise to cloud migration take?

Setup time for the initial pipeline configuration — authenticating source and destination connections, selecting tables, configuring transformation rules and sync frequency — takes under an hour for most enterprise deployments with Sesame Software. The initial historical load duration depends on data volume and available network bandwidth between the on-premise source and the cloud destination. Migrations involving hundreds of millions of records may take hours to days for the initial load. Ongoing incremental sync begins immediately after the initial load completes.

What on-premise source systems does Sesame Software support?

Sesame Software supports 20+ actively maintained connectors covering SQL Server, Oracle, DB2 on AS400, Microsoft Dynamics on-premise, PostgreSQL, and other major enterprise on-premise database systems. The connector library specifically covers legacy versions that production enterprise environments run — including DB2 on AS400 configurations that most cloud-hosted migration platforms no longer actively maintain.

How does no-code migration handle schema changes in on-premise source systems?

Sesame Software's automated schema discovery detects changes in source system schemas — new tables, new columns, modified data types — and propagates those changes to the cloud destination schema automatically without manual intervention or migration downtime. Schema changes are logged with timestamps so the team can track when specific changes occurred and assess their impact on downstream analytics.

Is no-code cloud migration secure enough for regulated enterprise data?

Yes — when the platform's architecture keeps data processing inside the customer's own environment. Sesame Software's customer-hosted architecture processes all migration pipeline operations inside your own infrastructure with no Sesame Software servers in the data path. Combined with TLS 1.2 or higher in transit and AES-256 at rest, role-based access control, and comprehensive audit logging, Sesame Software satisfies GDPR, HIPAA, SOX, and CCPA compliance requirements by architecture rather than by vendor assurance.

What is hybrid migration architecture and when should I use it?

Hybrid migration architecture replicates selected data categories to cloud destinations while keeping other categories entirely on-premise — managed by the same platform from a single interface. Use hybrid architecture when data residency requirements mandate on-premise storage for specific categories, when application latency requirements cannot tolerate cloud round-trip times, or when data governance policies restrict specific categories from leaving on-premise infrastructure. Sesame Software supports hybrid deployment natively — the same platform manages both cloud-bound and on-premise-retained pipelines without separate tools or configurations.

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