A migration project covers the full move: auditing and profiling the source data, designing the target structure, writing field-level mapping and transformation rules, cleaning and deduplicating, running trial migrations, validating the results against the source, executing the cutover, and supporting the first production weeks. We handle database, application, cloud, and full infrastructure migrations, including moves from on-premises systems to Azure, AWS, or Google Cloud.

Migration is a one-time move with an end date: data leaves one system and lands in another, and the old system gets retired.

Data integration is permanent plumbing: pipelines that keep running so several systems stay in sync. Projects often need both, since data usually arrives in a new platform that then has to exchange information with everything else. We scope them separately so you can see what each one costs.

For most projects, minutes rather than hours. The bulk of the data moves while the old system stays live, then a final delta sync runs inside the cutover window. Zero-downtime migrations are possible where the source system supports change data capture, though they cost more and add complexity, so we recommend them only when the business genuinely cannot pause.

Each stage has a defined rollback point, and the trial migration is where most problems surface, which is exactly why it exists. If validation fails after the cutover, the old system is still intact and reachable until we agree it can be decommissioned. Decommissioning is a separate decision made after the new system runs clean, never on cutover day.

Yes, and this is common with systems that outlived the people who built them. We profile the data directly to reconstruct the actual structure, spot the fields that are used in ways the schema does not suggest, and confirm the rules with the people who work in the system daily. It adds time to the audit stage, and we scope that stage separately so the estimate does not rest on guesswork.

Data engineering covers everything between your source systems and the reports your team reads: pipeline development, data warehouse and data lake design, migration from legacy storage, integration between systems, data quality and validation rules, and the reporting layer on top. Specific engagements often focus on one of these, and Forbytes runs each as a separate service with its own scope: data migration, data integration, data warehouse services, and business intelligence and analytics.

Reporting from production works until it stops working, usually when a heavy query slows down the application your customers use. A warehouse becomes worth building when you need history that production purges, when several systems have to combine in one report, or when analysts need access you cannot safely give them in production. The assessment stage answers this for your case, and a warehouse is not always the answer.

We build on Microsoft Azure, AWS, and Google Cloud, including BigQuery, and we work with on-premises and hybrid setups where a full cloud move is not on the table. Pipelines run with managed cloud services or custom ETL and ELT code, depending on your volume, budget, and who maintains the system afterwards.

Yes, we can. A common split is that Forbytes builds the platform and the pipelines while your analysts keep owning the metrics and the reports. We document the architecture, the transformation logic, and the operational runbook so your team can run and extend it. Where you plan to hire in-house later, we build with that handover in mind from the start.

Usually, yes. Most stalled AI projects fail on data rather than on models: inputs are incomplete, inconsistent, or scattered across systems that never agreed on a customer ID. A short data readiness assessment answers whether your AI use case is buildable now, what would need fixing first, and what that costs. That answer is worth having before anyone budgets for the model.

The cost of our business intelligence & analytics services depends on your specific request. Since requests can vary widely, from building a business intelligence solution from scratch to integrating or customizing an existing one, the price differs. 

To determine the cost of BI analytics services, we conduct a discovery call that doesn’t obligate you to anything. During the call, you can share your requirements, and we’ll provide a detailed breakdown of costs and timelines. We take into account your goals, budget, business specifics, and other relevant factors. 

Your data is legally protected. Before launching business analytics services, we agree upon, prepare, and sign contracts. Your data is fully protected under GDPR, and, if you wish, we also sign an NDA. 

On a practical level, we have strong data protection mechanisms in place. Before making any changes to your systems, we create data backups. We also never use your data in any other projects. Your business information remains 100% confidential and secure. 

You can contact us to receive sample contracts and review them with your legal team to ensure our professionalism and reliability in this area. 

We work with both custom and off-the-shelf systems, and we safely integrate them with our Business Intelligence analytics solutions. Additionally, we can use off-the-shelf solutions or build fully custom tools from scratch. 

We offer different integration methods, such as via API, middleware, or third-party tools, but every method is completely secure. Before proposing any approach, we fully align each decision with you. 

To determine the best type of integration and confirm compatibility with your systems, you can schedule a call with us. During the call, we’ll discuss the technical details and provide a clear answer. 

Most BI projects take 2 to 4 months from discovery to launch. A dashboard layer on top of clean, structured data can go live in 4–6 weeks. A full build — data warehouse, pipelines from multiple sources, transformation logic, and reporting — usually takes 3 to 4 months. 

The main factors that affect the timeline are the number of data sources, the state of your data, and how much custom transformation logic your reports require. After the discovery call, we give you a phased delivery plan with dates, so you see working results early instead of waiting for a single final release. 

That’s the starting point for most of our clients, and it doesn’t block the project. We begin by auditing your sources — ERP, CRM, ecommerce platform, spreadsheets, legacy databases — and mapping how data from each one aligns with the rest. Then we build pipelines that extract, clean, and standardize the data into one model, with validation rules that catch duplicates, gaps, and format conflicts before they reach your reports. 

You don’t need to clean or restructure anything on your side first. The consolidation work is part of the service, and your teams keep using their current systems while we connect them in the background. 

Our data warehouse development services bring senior expertise, proven processes, and flexibility without the overhead of hiring, training, and managing in-house staff. You get a self-sufficient data warehousing team focused on results, faster delivery, and reduced risk. Your investment pays off in speed, quality, and operational efficiency.

The costs of data warehousing development services are transparent and tied to the team size, project scope, and duration. Unlike chaotic in-house hiring or hourly consulting, our model gives you predictable expenses. You only pay for what you need, and we help optimize your setup to bring value and minimize wasted budget.

We staff your team based on expertise, seniority, and project relevance. Most data warehouse engineers are at the senior level, with deep experience in similar platforms and industries. Before kick-off, we match skills to your requirements. This ensures that your project benefits from practical know-how and industry-tested solutions from day one.

Data security is baked into everything we do. In data warehousing services, we follow strict access controls, encryption standards, and best practices to safeguard your business and customer information. Confidentiality agreements, secure communication channels, and compliance with international regulations ensure your data stays protected at all times.

You control the level of involvement. Whether you want daily updates, sprint reviews, or high-level progress reports, our teams adapt. We provide full transparency with tools, dashboards, and direct communication channels. You can stay informed without managing day-to-day operations. 

Absolutely. We offer a trial period where you can see how the data warehouse services team works in practice. You’ll be able to evaluate output quality, responsiveness, and integration with your processes. It’s a low-risk way to ensure the team matches your needs before engaging for the full project.

Big data consulting covers the decisions that come before a platform gets built: assessing current volumes, growth, and query patterns, defining what the business needs from the data, designing the target architecture, selecting storage and processing technologies, setting up governance and security rules, and sequencing implementation with costs per stage. The output is a documented plan any competent team can execute, including your own.

Consulting answers what to build and why. Data engineering builds it: the pipelines, the warehouse, the reporting layer. Clients with a clear technical direction usually skip straight to engineering. Clients choosing between platforms, or facing a costly commitment they cannot easily reverse, start with consulting. Both work as standalone engagements.

There is no threshold in terabytes, and volume alone is a poor trigger. The signals that matter are cost and capability: infrastructure bills growing faster than usage, queries too slow to run on the full dataset, or use cases your current architecture cannot support at any price. Plenty of companies asking about big data need a properly designed warehouse rather than a big data platform, and we would rather tell you that in week two than after implementation.

We work across Google Cloud including BigQuery, Microsoft Azure, and AWS, plus on-premises and hybrid setups where a full cloud move is not realistic. Selection weighs three things: how the platform handles your specific workload, what it costs at your projected volume rather than today’s, and whether your team can operate it. A platform your people cannot run becomes your problem after the engagement ends.

Yes, and it forms part of every engagement rather than an add-on. That means data ownership, role-based access, retention periods, quality standards, and the documentation GDPR requires. For regulated sectors we map the requirements against the proposed architecture early, since compliance constraints often decide where data can physically live, and that decision is expensive to revisit later.