Modernize your analytics foundation with a secure, low-risk migration.
Your legacy data warehouse may be holding back your analytics potential. Don’t let slow queries and outdated architecture limit your decision-making speed and scalability. With Aegis Softtech’s data warehouse migration services, you can shift from legacy, on-premise, or siloed systems to a modern, cloud-native data warehouse.
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Trusted by Clients: Rated 4.9 Stars.Data warehousing is a business advantage, and migration can prove a more cost-effective and streamlined way of staying ahead of your competitors.
If you’re experiencing any of these challenges, it’s time to consider migration:
Our team specializes in data migration for enterprises to move from a costly, rigid legacy data warehouse to a cloud data warehouse—uninterrupted, secure, and with business continuity in mind.
Each migration type offers distinct advantages depending on your goals. Our consultants help you choose the path that balances speed, cost, and modernization.
Migrate your existing data and workloads to the cloud with minimal or no modifications. It is the fastest way to exit your legacy system and reduce infrastructure dependency.
We keep much of your core architecture intact while leveraging specific cloud capabilities. It’s suitable for enterprises seeking incremental improvements without a full-scale redesign, offering speed and optimization.
In this approach, your entire data ecosystem is redesigned from the ground up to benefit from the cloud platforms’ elasticity, scalability, and advanced features. We modernize your schemas and integrate real-time streaming and AI/ML capabilities.
We carefully assess your existing systems to determine which workloads migrate to the cloud, which should be retired, and which must be retained.
Our team brings you the expertise of migrating from various legacy enterprise data warehouses and analytics systems, including:
Each source system has its own challenges, whether it’s proprietary SQL, legacy ETL jobs, or complex schemas.
Our accelerators help automate and simplify the transition.
So, we have established that our data warehouse migration services support your transition from legacy systems to modern, cloud-native architectures.
But what strategies do we adopt to bring the desired results?
One of the major decisions lies in whether to convert your existing pipelines/code or rebuild them from scratch. The right strategy—convert or rebuild—depends on your performance goals and time-to-value expectations.
Here’s how we decide what’s best for your business:
If you have many pipelines to migrate and want to preserve as much of the current logic as possible, then it’s the better option. Some tools or accelerators can help translate SQL dialects, adapt ETL workflows, or convert code or metadata definitions.
Rebuild is better when logic is complex, legacy, inefficient, or utilises non-portable components (UDFs, obscure dependencies). It enables the usage of Databricks-native features, including Delta Lake, Auto Loader, structured streaming, and Databricks SQL for cleaner, more maintainable pipelines.
Whether you're moving from on-premise to Snowflake, Redshift, or BigQuery, our migration approach ensures minimal disruption and maximum performance. Choosing the right data migration partner solidifies the success of your transformation.
Our core intent of data migration is built around four principles that ensure reliability and measurable value.
From cloud platforms to integration frameworks, our technology choices deliver measurable outcomes for you.
Our expertise spans across leading data ecosystems and tooling environments for fast migration and long-term scalability.
Before migration, we understand your current ecosystem to suggest the best-fit approach.
The most common tools we suggest to our clients are:
| Tools | Category | Usage |
|---|---|---|
| Informatica, Talend, SSIS | Traditional enterprise ETL tools | Often used with GUI workflows, connectors, transformation logic, etc. |
| Matillion, Fivetran, Airbyte | Cloud-native/modern tools | Often used for simpler ingestion, prebuilt connectors, and more automated pipelines. |
| PySpark, Scala | Custom Spark jobs or code-based pipelines | Common for large transformations, custom logic, batch, or streaming. |
When you hire our data warehouse developers, they custom-pick the toolset so you can maintain data consistency and reduce post-migration tuning time.
Our team follows a time-tested, de-risked migration methodology designed for speed, accuracy, and zero data loss.
Our team evaluates your existing data warehouses, ETL pipelines, reporting systems, and storage to identify complexity, dependencies, and workloads. We also align your business priorities with technical requirements by choosing a high-level migration strategy.
We then prepare a detailed migration roadmap, which includes selecting the target cloud platform (Snowflake, BigQuery, Azure Synapse, etc.), DWH architecture blueprints, migration sequencing, and cutover plans. Our experts identify migration accelerators, automation tools, and cloud services to speed up the process.
Legacy database objects, such as tables, indexes, views, ETL scripts, and stored procedures, are converted to cloud-compatible equivalents. Automated tools may be used to handle SQL dialect translation and pipeline refactoring.
We begin migrating the actual data from the source to the target, which may be done in batches or via continuous replication. Our team conducts timely performance tests and reconciliation checks to confirm that migrated data matches source data and meets quality benchmarks.
After successful data migration, integrations with BI tools, analytics platforms, and downstream applications are re-established. We fine-tune the workloads to leverage cloud-native advantages, such as partitioning, elastic compute, caching, and clustering. We focus primarily on query performance, workload balancing, and cost optimization in data warehouse migration.
Extensive functional and performance testing ensures that dashboards and analytics are behaving as expected in the new environment. We also run parallel tests across legacy and cloud systems to compare results.
Post-migration monitoring is essential for establishing consistent data quality and performance. Cost monitoring and governance controls are introduced to prevent overspending.
Your organization’s data journey is unique, but the challenges faced are often the same. Hence, before migrating your data warehouse, it is helpful to know the possibilities.
Here’s how we’ve solved common migration challenges for global enterprises, and can do so for you, too.
We moved many Hive workloads to Databricks with minimal refactoring. We achieved it by changing SerDe, file formats, or partitioning rather than full rewrites.
Our team chooses a light conversion for pipelines that are largely SQL, only including setting up data access or adapting syntax.
We implemented adaptations, such as using Databricks Runtime and optimizing for Databricks cluster architecture for custom Spark jobs. Our team reused some parts while rewriting others, depending on the custom logic.
When engaging with customers, Aegis Softtech offers multiple pricing and engagement approaches tailored to the nature, complexity, scope, and risk profile of each project.
The goal is to align commercial terms with client expectations and business outcomes.
It offers a well-defined scope, timeline, and deliverables, all of which are agreed upon upfront, and the price is fixed accordingly. It’s best suited for short-term projects with clear requirements, well-documented migration tasks or repeatable implementations.
You pay based on the actual effort spent, which involves hourly/daily/monthly rates for consultants, engineers, and architects. It’s best for complex, evolving, or exploratory projects or scenarios where requirements are dynamic or not fully defined upfront.
Pricing is linked to achieving stated business outcomes or service level targets, such as a set percentage of migration success, system uptime, or performance SLAs. It’s best for long-term partnerships where measurable outcomes are critical or clients who prefer to pay for results rather than effort.
Chosen for a small-scale pilot engagement at a lower cost or fixed minimal pricing to validate feasibility before a full rollout. It’s suitable for those trying new technology adoption (e.g., Databricks migration pilot, Snowflake PoC) or seeking to validate ROI or technical compatibility before committing.
Cloud-native data warehouse migration is the process of migrating your workloads and data from a traditional, on-premises data warehouse to a modern, cloud-based DWH designed to run on cloud infrastructure. It encompasses re-architecting the system to modernize and leverage cloud advantages, such as scalability and flexibility, by decoupling storage and compute.
Deciding between rehost, replatform, and refactor migration depends on your organization’s priorities for budget, speed, long-term benefits, and modernization.
Rehost is the fastest and most budget-friendly option if the application can be moved with minimal changes. Replatform helps maintain speed with minor data warehouse modernization without a full rewrite. Refactoring involves extensive code changes and is thus best when the goal is significant innovation and agility for the long term.
Hiring professionals for data warehouse migration is imperative for ensuring data integrity, mitigating risks, minimizing downtime, and cost and time efficiency.
The four main types of data migration are:
The duration of a data warehouse migration can vary significantly depending on factors such as data volume, target platform, modernization level, and source complexity. We do offer a one-size-fits-all estimate and instead begin every engagement with a readiness assessment, scoping your workloads, and identifying dependencies.
We anticipate and mitigate the key issues commonly arising during migration. Some of which include:
Considering the sheer amount of unstructured data being generated on an everyday basis, organizations ought to be drowning in it. However, this volume still does not guarantee them insights because just collecting it is no longer enough.
In the light of a data-driven world today, organizations now depend more on complex data architectures that can store, manage, and analyze significant volumes of data. Two of the most popular approaches are Data Lakehouse and Traditional Data Warehousing.
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