Database Migration Services

What is HIPAA-compliant Database?  A database is an organized collection of structured information controlled by a database management system. To be HIPAA-compliant, the database must follow administrative, physical, and technical safeguards of the HIPAA Security Rule. Often it means limiting access to PHI, as well as safely processing, transmitting, receiving, and encrypting data, plus having a proactively breach mitigation strategy. Administrative, physical, and technical safeguards of the HIPAA Security Rule HIPAA Rules for Database Security If your database contains even a part of PHI, it is covered by the HIPAA Act of 1996 and can attract the attention of auditors. PHI is the information containing any identifiers that link an individual to their health status, the healthcare services they have received, or their payment for healthcare services. The HIPAA Security Rule (the part of HIPAA Act) specifically focuses on protecting electronic PHI. Technical safeguards (the part of HIPAA Security Rule) contain requirements for creating a HIPAA-compliant database. Centers for Medicare & Medicaid Services (CMS) covers HIPAA Technical Safeguards for database security in their guidance. The first question that can arise is whether you should use any specific database management system to address the requirements? The answer is absolutely no. The Security Rule is based on the concept of technology neutrality. Therefore, no specific requirements for types of technology are identified. Businesses can determine themselves which technologies are reasonable and appropriate to use. There are many technical security tools, products, and solutions that a company may select. However, the guidance warns that despite the fact that some solutions may be costly, it can’t be the cause of not implementing security measures. "Required" (R) specifications are mandatory measures. "Addressable" (A) specifications may not be implemented if neither the standard measure nor any reasonable alternatives are deemed appropriate (this decision must be well-documented and justified based on the risk assessment). Here are the mandatory and addressable requirements for a HIPAA-compliant database. Mandatory HIPAA Database Security Requirements HIPAA Compliant Database Access Control Database authentication. Verify that a person looking for access to ePHI is the one claimed. Database authorization. Restrict access to PHI according to different roles ensuring that no data or information is made available or disclosed to unauthorized persons. Encrypted PHI PHI must be encrypted both when it is being stored and during transit to ensure that a malicious party cannot access information directly. Unique User IDs You need to distinguish one individual user from another followed by the ability to trace activities performed by each individual within the ePHI database.  Database security logging and monitoring All usage queries and access to PHI must be logged and saved in a separate infrastructure to archive for at least six years.  Database backups Must be created, tested, and securely stored in a separate infrastructure, as well as properly encrypted.  Patching and updating database management software Regular software upgrades, as soon as they are available, to ensure that it’s running the latest tech. ePHI disposal capability Methods of deleting ePHI by trained specialists without the ability to recover it should be implemented. By following the above requirements you create a HIPAA-compliant database. However, it’s not enough. All HIPAA-compliant databases must be settled in a high-security infrastructure (for example, cloud hosting) that itself should be fully HIPAA-compliant. HIPAA-Compliant Database Hosting You need HIPAA-compliant hosting if you want either to store ePHI databases using services of hosting providers, or/and to provide access to such databases from the outside of your organization. Organizations can use cloud services to store or process ePHI, according to U.S. Department of Health & Human Services. HIPAA compliant or HIPAA compliance supported? Most of the time, cloud hosting providers are not HIPAA compliant by default but support HIPAA compliance, which means incorporating all the necessary safeguards to ensure HIPAA requirements can be satisfied. If healthcare business wants to start collaborating with a cloud hosting provider, they have to enter into a contract called a Business Associate Agreement (BAA) to enable a shared security responsibility model, which means that the hosting provider takes some HIPAA responsibility, but not all.  deloitte.com/content/dam/Deloitte/us/Documents/risk/us-hipaa-compliance-in-the-aws-cloud.pdf In other words, it is possible to utilize HIPAA compliance supported services and not be HIPAA compliant. Vendors provide tools to implement HIPAA requirements, but organizations must ensure that they have properly set up technical controls - it's their responsibility only. Cloud misconfigurations can cause an organization to be non-compliant with HIPAA. So, healthcare organizations must: be ensured that the ePHI is encrypted during transit, in use, and at rest; enable data backup and disaster recovery plan to create and maintain retrievable exact copies of ePHI, including secure authorization and authentication  even during times where emergency access to ePHI is needed; implement authentication and authorization mechanisms to protect ePHI from being altered or destroyed in an unauthorized manner as well as include procedures for creating, changing, and safeguarding passwords; implement procedures to monitor log-in attempts and report discrepancies; conduct assessments of potential risks and vulnerabilities to the confidentiality, integrity, and availability of ePHI; include auditing capabilities for their database applications so that security specialists can analyze activity logs to discover what data was accessed, who had access, from what IP address, etc. In other words, one needs to track, log, and store data in special locations for extended periods of time. PaaS/DBaaS vs IaaS Database Hosting Solutions Healthcare organizations may use their own on-premise HIPAA-compliant database management solutions or utilize cloud hosting services (sometimes with managed database services) offered by external hosting providers.  Selecting between different hosting options is often selecting between PaaS/DBaaS and IaaS.  For example, Amazon Web Services (AWS) provides Amazon Relational Database Services (Amazon RDS) that not only gives you access to already cloud-deployed MySQL, MariaDB, PostgreSQL, Oracle, Microsoft SQL Server or Amazon Aurora relational database management software, but also removes almost all administration tasks (so-called PaaS/DBaaS solution). In turn, Amazon's Elastic Compute Cloud (Amazon EC2) services are for those who want to control as much as possible with their database management in the cloud (so-called IaaS solution).  on-Premise vs PaaS/DBaaS vs IaaS Database Hosting Solution PaaS/DBaaS vs IaaS Database Hosting Solution Azure also provides relational database services that are the equivalent of Amazon RDS: Azure SQL Database, Azure Database for MySQL, Azure Database for PostgreSQL, and Azure Database for MariaDB. Other database engines such as SQL Server, Oracle, and MySQL can be deployed using Azure VM Instances (Amazon EC2 equivalent in Azure). Our company is specializing in database development and creates databases for large and smaller amounts of data storage. Belitsoft’s experts will help you prepare a high-level cloud development and cloud migration plan and then perform smooth and professional migration of legacy infrastructure to Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. We also employ experts in delivering easy to manage HIPAA-compliant solutions and technology services for medical businesses of all sizes. Contact us if you would like to get a HIPAA risk assessment and analysis.

Belitsoft is a team of Azure migration and modernization experts with a proven track record and portfolio of projects to show for it. We offer comprehensive application modernization services, which include workload analysis, compatibility checks, and the creation of a sound migration strategy. Further, we will take all the necessary steps to ensure your successful transition to Azure cloud. Planning your migration to Azure is an important process as it involves choosing whether to rehost, refactor, rearchitect, or rebuild your applications. A laid-out Azure migration strategy helps put these decisions in perspective. Read on to find our step-by-step guide for the cloud migration process, plus a breakdown of key migration models. An investment in on-premises hosting and data centers can be a waste of money nowadays, because cloud technologies provide significant advantages, such as usage-based pricing and the capacity to easily scale up and down. In addition, your downtime risks will be near-zero in comparison with on-premises infrastructure. Migration to the cloud from the on-premises model requires time, so the earlier you start, the better. Dmitry Baraishuk Chief Innovation Officer at Belitsoft on Forbes.com Cloud Migration Process to Microsoft Azure We would like to share our recommended approach for migrating applications and workloads to Azure. It is based on Microsoft's guidelines and outlines the key steps of the Azure Migration process. 1. Strategize and plan your migration process The first thing you need to do to lay out a sound migration strategy is to identify and organize discussions among the key business stakeholders. They will need to document precise business outcomes expected from the migration process. The team is also required to understand and discover the underlying technical aspects of cloud adoption and factor them into the documented strategy. Next, you will need to come up with a strategic plan that will prioritize your goals and objectives and serve as a practical guide for cloud adoption. It begins with translating strategy into more tangible aspects like choosing which applications and workloads have higher priority for migration. You move on deeper into business and technical elements and document them into a plan used to forecast, budget, and implement your Azure migration strategy. In the end, you'll be able to calculate your total cost of ownership with Azure’s TCO calculator which is a handy tool for planning your savings and expenses for your migration project. 2. Evaluate workloads and prepare for migration After creating the migration plan you will need to assess your environment and categorize all of your servers, virtual machines, and application dependencies. You will need to look at such key components of your infrastructure as: Virtual Networks: Analyze your existing workloads for performance, security, and stability and make sure you match these metrics with equivalent resources in Azure cloud. This way you can have the same experience as with the on-premise data center. Evaluate whether you will need to run your own DNS via Active Directory and which parts of your application will require subnets. Storage Capacity: Select the right Azure storage services to support the required number of operations per second for virtual machines with intensive I/O workloads. You can prioritize usage based on the nature of the data and how often users access it. Rarely accessed (cold data) could be placed in slow storage solutions. Computing resources: Analyze how you can win by migrating to flexible Azure Virtual Machines. With Azure, you are no longer limited by your physical server’s capabilities and can dynamically scale your applications along with shifting performance requirements. Azure Autoscale service allows you to automatically distribute resources based on metrics and keeps you from wasting money on redundant computing power. To make life easier, Azure has created tools to streamline the assessment process: Azure Migrate is Microsoft’s current recommended solution and is an end-to-end tool that you can use to assess and migrate servers, virtual machines, infrastructure, applications, and data to Azure. It can be a bit overwhelming and requires you to transfer your data to Azure’s servers. Microsoft Assessment and Planning (MAP) toolkit can be a lighter solution for people who are just at the start of their cloud migration journey. It needs to be installed and stores data on-premise but is much simpler and gives a great picture of server compatibility with Azure and the required Azure VM sizes. Virtual Machine Readiness Assessment tool Is another great tool that guides the user all the way through the assessment with a series of questions. Besides the questions, it also provides additional information with regard to the question. In the end, it gives you a checklist for moving to the cloud. Create your migration landing zone. As a final step, before you move on to the migration process you need to prepare your Azure environment by creating a landing zone. A landing zone is a collection of cloud services used for hosting, operating, and governing workloads migrated to the cloud. Think of it as a blueprint for your future cloud setup which you can further scale to your requirements. 3. Migrate your applications to Azure Cloud  First of all, you can simply replace some of your applications with SaaS products hosted by Azure. For instance, you can move your email and communication-related workloads to Office 365 (Microsoft 365). Document management solutions can be replaced with Sharepoint. Finally, messaging, voice, and video-shared communications can step over to Microsoft Teams. For other workloads that are irreplaceable and need to be moved to the cloud, we recommend an iterative approach. Luckily, we can take advantage of Azure hybrid cloud solutions so there’s no need for a rapid transition to the cloud. Here are some tips for migrating to Azure: Start with a proof of concept: Choose a few applications that would be easiest to migrate, then conduct data migration testing on your migration plan and document your progress. Identifying any potential issues at an early stage is critical, as it allows you to fine-tune your strategy before proceeding. Collect insights and apply them when you move on to more complex workloads. Top choices for the first move include basic web apps and portals. Advance with more challenging workloads: Use the insights from the previous step to migrate workloads with a high business impact. These are often apps that record business transactions with high processing rates. They also include strongly regulated workloads. Approach most difficult applications last: These are high-value asset applications that support all business operations. They are usually not easily replaced or modernized, so they require a special approach, or in most cases - complete redesign and development. 4. Optimize performance in Azure cloud After you have successfully migrated your solutions to Azure, the next step is to look for ways to optimize their performance in the cloud. This includes revisions of the app’s design, tweaking chosen Azure services, configuring infrastructure, and managing subscription costs. This step also includes possible modifications when after you’ve rehosted your application, you decide to refactor and make it more compatible with the cloud. You may even want to completely rearchitect the solution with Azure cloud services. Besides this, some vital optimizations include: Monitoring resource usage and performance with tools like Azure Monitor and Azure Traffic Manager and providing an appropriate response to critical issues. Data protection using measures such as disaster recovery, encryption, and data back-ups. Maintaining high security standards by applying centralized security policies, eliminating exposure to threats with antivirus and malware protection, and responding to attacks using event management. Azure migration strategies The strategies for migrating to the Azure cloud depend on how much you are willing to modernize your applications. You can choose to rehost, refactor, rearchitect, or rebuild apps based on your business needs and goals. 1. Rehost or Lift and Shift strategy Rehosting means moving applications from on-premise to the cloud without any code or architecture design changes. This type of migration fits apps that need to be quickly moved to the cloud, as well as legacy software that supports key business operations. Choose this method if you don’t have much time to modernize your workload and plan on making the big changes after moving to the cloud. Advantages: Speedy migration with no risk of bugs and breakdown issues. Disadvantages: Azure cloud service usage may be limited by compatibility issues. 2. Refactor or repackaging strategy During refactoring, slight changes are made to the application so that it becomes more compatible with cloud infrastructure. This can be done if you want to avoid maintenance challenges and would like to take advantage of services like Azure SQL Managed Instance, Azure App Service, or Azure Kubernetes Service. Advantages: It’s a lot faster and easier than a complete redesign of architecture, allows to improve the application’s performance in the cloud, and to take advantage of advanced DevOps automation tools. Disadvantages: Less efficient than moving to improved design patterns like the transition to microservices from monolith architecture. 3. Rearchitect strategy Some legacy software may not be compatible with the Azure cloud environment. In this case, the application needs a complete redesign to a cloud-native architecture. It often involves migrating to microservices from the monolith and moving relational and nonrelational databases to a managed cloud storage solution. Advantages: Applications leverage the full power of Azure cloud with high performance, scalability, and flexibility. Disadvantages: Migrating may be tricky and pose challenges, including issues in the early stages like breakdowns and service disruptions. 4. Rebuild strategy The rebuild strategy takes things even further and involves taking apart the old application and developing a new one from scratch using Azure Platform as a service (PaaS) services. It allows taking advantage of cloud-native technologies like Azure Containers, Functions and Logic Apps to create the application layer and Azure SQL Database for the data tier. A cloud-native approach gives you complete freedom to use Azure’s extensive catalog of products to optimize your application’s performance. Advantages: Allows for business innovation by leveraging AI, blockchain, and IoT technologies. Disadvantages: A fully cloud-native approach may pose some limitations in features and functionality as compared to custom-built applications. Each modernization approach has pros and cons as well as different costs, risks and time frames. That is the essence of the risk-return principle, and you have to balance between less effort and risks but more value and outputs. The challenge is that as a business owner, especially without tech expertise, you don't know how to modernize legacy applications. Who's creating a modernization plan? Who's executing this plan? How do you find staff with the necessary experience or choose the right external partner? How much does legacy software modernization cost? Conducting business and technical audits helps you find your modernization path. Dmitry Baraishuk Chief Innovation Officer at Belitsoft on Forbes.com Professional support for your Azure migration Every migration process is unique and requires a personal approach. It is never a one-way street and there are a lot of nuances and challenges on the path to cloud adoption. Often, having an experienced migration partner can seriously simplify and accelerate your Azure cloud migration journey.

Why Financial Institutions Migrate Data Legacy systems are dragging them down Most migrations start because something old is now a blocker. Aging infrastructure no one wants to maintain, systems only one person understands (who just resigned), workarounds piled on top of workarounds. Eventually, the cost of not migrating becomes high. Compliance doesn’t wait New regulations show up, and old systems cannot cope. GDPR, SOX, PCI, local data residency rules. New audit requirements needing better lineage, access logs, encryption. If your platform cannot prove control, migration becomes the only way to stay in business. M&A forces the issue When banks merge or acquire, they inherit conflicting data structures, duplicate records, fragmented customer views. The only path forward is consolidation. You cannot serve a unified business on mismatched backends. Customer expectations got ahead of tech Customers want mobile-first services, real-time transactions and personalized insights. Legacy systems can’t provide that. They weren’t designed to talk to mobile apps, stream real-time data, or support ML-powered anything.  Analytics and AI hit a wall You can’t do real analytics if your data is trapped in ten different systems, full of gaps and duplicates, updated nightly via broken ETL jobs. Modern data platforms solve this. Migrations aim to centralize, clean, and connect data. Cost pressure from the board Everyone says “cloud saves money.” That’s only half true. If you’re running old on-premises systems with physical data centers, licenses, no elasticity or automation …then yes, the CFO sees migration as a way to cut spending. However, smart teams don’t migrate for savings alone. They migrate to stop paying for dysfunction. Business wants agility. IT can’t deliver When the business says “launch a new product next quarter,” and IT says “that will take 8 months because of system X,” migration becomes a strategy conversation. Cloud-native platforms, modern APIs, and scalable infrastructure are enablers. But you can’t bolt them onto a fossil. Core system upgrades that can’t wait anymore This is the “we’ve waited long enough” scenario. A core banking system that can’t scale. A data warehouse from 2007. A finance platform with no support. It’s not a transformation project. It’s triage. You migrate because staying put means stagnation, or worse, failure, during a critical event. We combine automated tools and manual checks to find hidden risks early before they become problems through a discovery process, whether you’re consolidating systems or moving to the cloud. Database Migration Strategy Start by figuring out what you really have Inventory is what prevents a disaster later. Every system, every scheduled job, every API hook: it all needs to be accounted for. Yes, tools like Alation, Collibra, and Apache Atlas can speed it up, but they only show what is visible. The real blockers are always the things nobody flagged: Excel files with live connections, undocumented views, or internal tools with hard-coded credentials. Discovery is slow, but skipping it just means fixing production issues after cutover. Clean the data before you move it Bad data will survive the migration if you let it. Deduplication, classification, and data profiling must be done before the first trial run. Use whatever makes sense: Data Ladder, Spirion, Varonis. The tooling is not the hard part. The problem is always legacy data that does not fit the new model. Data that was fine when written is now inconsistent, partial, or unstructured. You cannot automate around that. You clean it, or you carry it forward. Make a real call on the strategy — not just the label Do not pick a migration method because a vendor recommends it. Big Bang works, but only if rollback is clean and the system is small enough that a short outage is acceptable. It fails hard if surprises show up mid-cutover. Phased is safer in complex environments where dependencies are well-mapped and rollout can be controlled. It adds overhead, but gives room to validate after each stage. Parallel (or pilot) makes sense when confidence is low and validation is a high-priority. You run both systems in sync and check results before switching over. It is resource-heavy, you are doubling effort temporarily, but it removes guesswork. Hybrid is a middle ground. Not always a cop-out, it can be deliberate, like migrating reference data first, then transactions. But it requires real planning, not just optimism. Incremental (trickle) migration is useful when zero downtime is required. You move data continuously in small pieces, with live sync. This works, but adds complexity around consistency, cutover logic, and dual writes. It only makes sense if the timeline is long. Strategy should reflect risk, not ambition. Moving a data warehouse is not the same as migrating a trading system. Choose based on what happens when something fails. Pilot migrations only matter if they are uncomfortable Run a subset through the full stack. Use masked data if needed, but match production volume. Break the process early. Most failures do not come from the bulk load. They come from data mismatches, dropped fields, schema conflicts, or edge cases the dev team did not flag. Pilot migrations are there to surface those, not to "prove readiness." The runbook is a plan, not a document If people are confused during execution, the runbook fails. It should say who does what, when, and what happens if it fails. All experts emphasize execution structure: defined rollback triggers, reconciliation scripts, hour-by-hour steps with timing buffers, a plan B that someone has actually tested. Do not rely on project managers to fill in gaps mid-flight. That is how migrations end up in the postmortem deck. Validation is part of the job, not the cleanup If you are validating data after the system goes live, you are already late. The validation logic must be scripted, repeatable, and integrated, not just “spot checked” by QA. This includes row counts, hashing, field-by-field matching, downstream application testing, and business-side confirmation that outputs are still trusted. Regression testing is the only way to tell if you broke something. Tools are fine, but they are not a strategy Yes, use DMS, Azure Data Factory, Informatica, Google DMS, SchemaSpy, etc. Just do not mistake that for planning. All of these tools fail quietly when misconfigured. They help only if the underlying migration plan is already clear, especially around transformation rules, sequence logic, and rollback strategy. The more you automate, the more you need to trust that your input logic is correct. Keep security and governance running in parallel Security is not post-migration cleanup. It is active throughout. Access must be scoped to migration-only roles PII must be masked in all non-prod runs Logging must be persistent and immutable Compliance checkpoints must be scheduled, not reactive Data lineage must be maintained, especially during partial cutovers This is not a regulatory overhead. These controls prevent downstream chaos when audit, finance, or support teams find data inconsistencies. Post-cutover is when you find what you missed No matter how well you planned, something will break under load: indexes will need tuning, latency will spike, some data will have landed wrong, even with validation in place, reconciliations will fail in edge cases and users will see mismatches between systems. You need active monitoring and fast intervention windows. That includes support coverage, open escalation channels, and pre-approved rollback windows for post-live fixes. Compliance, Risk, and Security During Migration Data migrations in finance are high-risk by default. Regulations do not pause during system changes. If a dataset is mishandled, access is left open, records go missing, the legal and financial exposure is immediate. Morgan Stanley was fined after failing to wipe disks post-migration. TSB’s failed core migration led to outages, regulatory fines, and a permanent hit to customer trust. Security and compliance are not post-migration concerns. They must be integrated from the first planning session. Regulatory pressure is increasing The EU’s DORA regulation, SEC cyber disclosure rules, and ongoing updates to GDPR, SOX, and PCI DSS raise the bar for how data is secured and governed.  Financial institutions are expected to show not just intent, but proof: encryption in transit and at rest, access logs, audit trails, and evidence that sensitive data was never exposed, even in testing. Tools like Data Ladder, Spirion, and Varonis track PII, verify addresses, and ensure that only necessary data is moved. Dynamic masking is expected when production data is copied into lower environments. Logging must be immutable. Governance must be embedded. Strategy choice directly affects your exposure The reason phased, parallel, or incremental migrations are used in finance has nothing to do with personal preference — it is about control. These strategies buy you space to validate, recover, and prove compliance while the system is still under supervision. Parallel systems let you check both outputs in real time. You see immediately if transactional records or balances do not match, and you have time to fix it before going live. Incremental migrations, with near-real-time sync, give you the option to monitor how well data moves, how consistently it lands, and how safely it can be cut over — without needing full downtime or heavy rollback. The point is not convenience. It is audit coverage. It is SLA protection. It is a legal defense. How you migrate determines how exposed you are to regulators, to customers, and to your own legal team when something goes wrong, and the logs get pulled. Security applies before, during, and after the move Data is not less sensitive just because it is moving. Testing environments are not immune to audit. Encryption is not optional — and access controls do not get a break. This means: Everything in transit is encrypted (TLS minimum) Storage must use strong encryption (AES-256 or equivalent) Access must be restricted by role, time-limited, logged, and reviewed Temporary credentials are created for migration phases only Any non-production environment gets masked data, not copies Belitsoft builds these controls into the migration path from the beginning — not as hardening after the fact. Access is scoped. Data is verified. Transfers are validated using hashes. There is no blind copy-and-paste between systems. Every step is logged and reversible. The principle is simple: do not treat migration data any differently than production data. It will not matter to regulators that it was “temporary” if it was also exposed. Rely on Belitsoft’s database migration engineers and data governance specialists to embed security, compliance, and auditability into every phase of your migration. We ensure your data remains protected, your operations stay uninterrupted, and your migration meets the highest regulatory standards. Reconciliation is the compliance checkpoint Regulators do not care that the migration was technically successful. They care whether the balances match, the records are complete, and nothing was lost or altered without explanation. Multiple sources emphasize the importance of field-level reconciliation, automated validation scripts, and audit-ready reports. During a multi-billion-record migration, your system should generate hundreds of real-time reconciliation reports. The mismatch rate should be in the double digits, not thousands, to prove that validation is baked into the process. Downtime and fallback are also compliance concerns Compliance includes operational continuity. If the system goes down during migration, customer access, trading, or payment flows can be interrupted. That triggers not just customer complaints, but SLA penalties, reputational risk, and regulator involvement. Several strategies are used to mitigate this: Maintaining parallel systems as fallback Scheduling cutovers during off-hours with tested recovery plans Keeping old systems in read-only mode post-cutover Practicing rollback in staging Governance must be present, not implied Regulators expect to see governance in action, not in policy, but in tooling and workflow: Data lineage tracking Governance workflows for approvals and overrides Real-time alerting for access anomalies Escalation paths for risk events Governance is not a separate track, it is built into the migration execution. Data migration teams do this as standard. Internal teams must match that discipline if they want to avoid regulatory scrutiny. No margin for “close enough” In financial migrations, there is no tolerance for partial compliance. You either maintained data integrity, access control, and legal retention, or you failed. Many case studies highlight the same elements: Drill for failure before go-live Reconcile at every step, not just at the end Encrypt everything, including backups and intermediate outputs Mask what you copy Log everything, then check the logs Anything less than that leaves a gap that regulators, or customers, will eventually notice. Database Migration Tools There is no single toolset for financial data migration. The stack shifts based on the systems involved, the state of the data, and how well the organization understands its own environment. Everyone wants a "platform" — what you get is a mix of open-source utilities, cloud-native services, vendor add-ons, and custom scripts taped together by the people who have to make it work. Discovery starts with catalogs” Cataloging platforms like Alation, Collibra, and Apache Atlas help at the front. They give you visibility into data lineage, orphaned flows, and systems nobody thought were still running. But they’re only as good as what is registered. In every real migration, someone finds an undocumented Excel macro feeding critical reports. The tools help, but discovery still requires manual effort, especially when legacy platforms are undocumented. API surfaces get mapped separately. Teams usually rely on Postman or internal tools to enumerate endpoints, check integrations, and verify that contract mismatches won’t blow up downstream. If APIs are involved in the migration path, especially during partial cutovers or phased releases, this mapping happens early and gets reviewed constantly. Cleansing and preparation are where tools start to diverge” You do not run a full migration without profiling. Tools like Data Ladder, Spirion, and Varonis get used to identify PII, address inconsistencies, run deduplication, and flag records that need review. These aren’t perfect: large datasets often require custom scripts or sampling to avoid performance issues. But the tooling gives structure to the cleansing phase, especially in regulated environments. If address verification or compliance flags are required, vendors like Data Ladder plug in early, especially in client record migrations where retention rules, formatting, or legal territories come into play. Most of the transformation logic ends up in NiFi, scripts, or something internal For format conversion and flow orchestration, Apache NiFi shows up often. It is used to move data across formats, route loads, and transform intermediate values. It is flexible enough to support hybrid environments, and visible enough to track where jobs break. SchemaSpy is commonly used during analysis because most legacy databases do not have clean schema documentation. You need visibility into field names, relationships, and data types before you can map anything. SchemaSpy gives you just enough to start tracing, but most of the logic still comes from someone familiar with the actual application. ETL tools show up once the mapping is complete. At this point, the tools depend on environment: AWS DMS, Google Cloud DMS, and Azure Data Factory get used in cloud-first migrations.AWS Schema Conversion Tool (SCT) helps when moving from Oracle or SQL Server to something modern and open. On-prem, SSIS still hangs around, especially when the dev team is already invested in it. In custom environments, SQL scripts do most of the heavy lifting — especially for field-level reconciliation and row-by-row validation. The tooling is functional, but it’s always tuned by hand. Governance tooling Platforms like Atlan promote unified control planes: metadata, access control, policy enforcement, all in one place. In theory, they give you a single view of governance. In practice, most companies have to bolt it on during migration, not before. That’s where the idea of a metadata lake house shows up: a consolidated view of lineage, transformations, and access rules. It is useful, especially in complex environments, but only works if maintained. Gartner’s guidance around embedded automation (for tagging, quality rules, and access controls) shows up in some projects, but not most. You can automate governance, but someone still has to define what that means. Migration engines Migration engines control ETL flows, validate datasets, and give a dashboard view for real-time status and reconciliation. That kind of tooling matters when you are moving billions of rows under audit conditions. AWS DMS and SCT show up more frequently in vendor-neutral projects, not because they are better, but because they support continuous replication, schema conversion, and zero-downtime scenarios. Google Cloud DMS and Azure Data Factory offer the same thing, just tied to their respective platforms. If real-time sync is required, in trickle or parallel strategies, then Change Data Capture tooling is added. Some use database-native CDC. Others build their own with Kafka, Debezium, or internal pipelines. Most validation is scripted. Most reconciliation is manual Even in well-funded migrations, reconciliation rarely comes from off-the-shelf tools. Companies use hash checks, row counts, and custom SQL joins to verify that data landed correctly. In some cases, database migration companies build hundreds of reconciliation reports to validate a billion-record migration. No generic tool gives you that level of coverage out of the box. Database migration vendors use internal frameworks. Their platforms support full validation and reconciliation tracking and their case studies cite reduced manual effort. Their approach is clearly script-heavy, format-flexible (CSV, XML, direct DB), and aimed at minimizing downtime.  The rest of the stack is coordination, not execution. During cutover, you are using Teams, Slack, Jira, Google Docs, and RAID logs in a shared folder. The runbook sits in Confluence or SharePoint. Monitoring dashboards are built on Prometheus, Datadog, or whatever the organization already uses.  What a Serious Database Migration Vendor Brings (If They’re Worth Paying) They ask the ugly questions upfront Before anyone moves a byte, they ask, What breaks if this fails? Who owns the schema? Which downstream systems are undocumented? Do you actually know where all your PII is? A real vendor runs a substance check first. If someone starts the engagement with “don’t worry, we’ve done this before,” you’re already in danger. They design the process around risk, not speed You’re not migrating a blog. You’re moving financial records, customer identities, and possibly compliance exposure. A real firm will: Propose phased migration options, not a heroic “big bang” timeline Recommend dual-run validation where it matters Build rollback plans that actually work Push for pre-migration rehearsal, not just “test in staging and pray” They don’t promise zero downtime. They promise known risks with planned controls. They own the ETL, schema mapping, and data validation logic Real migration firms write: Custom ETL scripts for edge cases (because tools alone never cover 100%) Schema adapters when the target system doesn’t match the source Data validation logic — checksums, record counts, field-level audits They will not assume your data is clean. They will find and tell you when it’s not — and they’ll tell you what that means downstream. They build the runbooks, playbooks, and sanity checks This includes: What to do if latency spikes mid-transfer What to monitor during cutover How to trace a single transaction if someone can’t find it post-migration A go/no-go checklist the night before switch The good ones build a real migration ops guide, not a pretty deck with arrows and logos, but a document people use at 2AM. They deal with vendors, tools, and infrastructure, so you don’t have to They don’t just say “we’ll use AWS DMS.” They provision it, configure it, test it, monitor it, and throw it away clean. If your organization is multi-cloud or has compliance constraints (data residency, encryption keys, etc.), they don’t guess; they pull the policies and build around them. They talk to your compliance team like adults Real vendors know: What GDPR, SOX, PCI actually require How to write access logs that hold up in an audit How to handle staging data without breaking laws How to prepare regulator notification packets if needed They bring technical project managers who can speak of “risk”, not just “schema.” So, What You’re Really Hiring You’re not hiring engineers to move data. You’re hiring process maturity, disaster recovery modeling, DevOps with guardrails and legal fluency. With 20+ years of database development and modernization expertise, Belitsoft owns the full technical execution of your migration—from building custom ETL pipelines to validating every transformation across formats and platforms. Contact our experts to get a secure transition, uninterrupted operations, and a future-proof data foundation aligned with the highest regulatory standards.