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From Legacy to Lakehouse: The New Data Strategy with Databricks | Adroitent Home/ Blog/ From legacy to Lakehouse Blog · Data & AI From legacy to Lakehouse: the new data strategy for modern enterprises Failed AI initiatives, spiralling infrastructure costs, and data engineers firefighting pipelines instead of building business value all trace back to the same root cause. Here is why the Lakehouse — and Databricks — has become the answer. Adroitent InsightsData & AIDatabricks6 min read Key takeaways Legacy data warehouses were built for reporting on structured data — not for the velocity, volume, and variety that define enterprise data today. Data teams lose up to 70% of their time to brittle pipelines, data reconciliation, and ageing infrastructure. The Databricks Lakehouse unifies storage, governance, processing, and AI through Delta Lake, Unity Catalog, Apache Spark, MLflow, and AutoML. Enterprises that migrate report 30–40% infrastructure cost savings and AI moving from proof-of-concept to production in weeks rather than months. A phased migration — audit, govern, prioritize, run parallel, decommission — is what separates programmes that land from programmes that stall. There is a quiet crisis unfolding inside enterprise data teams worldwide, and its consequences show up everywhere: failed AI initiatives, spiralling infrastructure costs, and data engineers firefighting pipelines instead of building business value. The cause is a legacy data architecture that can no longer support the way modern enterprises need to compete. The solution is the Lakehouse — and Databricks is the platform leading that transformation. As enterprises accelerate their digital transformation journeys, they need a modern data strategy that can unify analytics, data engineering, governance, and AI on a single platform. This is where the Lakehouse architecture, powered by Databricks, is changing the way organizations manage and derive value from their data. The problemThe challenges of legacy data architectures Traditional data warehouses were built primarily for reporting and business intelligence workloads. They create real friction when organizations attempt to scale analytics and AI initiatives. Common challenges include: Data silos spread across multiple systems High infrastructure and licensing costs Complex ETL pipelines that increase latency Limited support for unstructured and semi-structured data Slow access to business insights Difficulty scaling AI and ML workloads As enterprises generate data from cloud applications, IoT devices, customer interactions, and digital platforms, maintaining separate systems for storage, analytics, and AI becomes increasingly inefficient and costly. The diagnosisWhy legacy data infrastructure fails modern enterprises Traditional data warehouses delivered reliable reporting on structured, predictable data, but they were never designed for the velocity and volume that defines enterprise data in 2026. Data teams spend up to 70% of their time managing brittle pipelines, reconciling inconsistent data, and maintaining ageing infrastructure. AI and ML initiatives stall because the governed, accessible data they require is perpetually out of reach. Business leaders wait days for insights that should arrive in minutes. The problem is the architecture — and that is precisely what Databricks solves. By the numbers 70% of data-team time spent maintaining pipelines and infrastructure instead of creating value 30–40% average infrastructure cost reduction after consolidating onto a Lakehouse Hours not days or weeks — the new cycle time for analytics that previously ran in batch Weeks not months — proof-of-concept to production AI on governed, unified data The platformWhat makes Databricks the right platform for Lakehouse migration Databricks’ Lakehouse architecture addresses those challenges by storing data in open object stores like S3, ADLS, or GCS while adding ACID transactions, metadata management, and indexing for reliable analytics. Built on open-source projects including Apache Spark, Delta Lake, and MLflow, the Lakehouse keeps data free from proprietary formats and closed ecosystems. Databricks is not just another cloud data platform. It is the most trusted and most adopted enterprise data and AI platform available today. Databricks introduced the Lakehouse to combine the best capabilities of data lakes and data warehouses into a unified platform. The Databricks Data Intelligence Platform lets organizations store, process, govern, analyze, and apply AI to a single source of truth. Four components do the work: Delta Lake An open-source storage layer that brings ACID transactions, schema enforcement, and versioned data management to cloud storage. Enterprises get the reliability and query performance of a warehouse combined with the flexibility and cost efficiency of a lake, without compromising either. On one platform, teams run SQL analytics, build and deploy machine learning models, process real-time streaming data, and develop generative AI applications — no duplication, no silos. Unity Catalog Enterprise-grade governance built directly into the platform, centralizing data discovery, access control, lineage tracking, and compliance enforcement across every workload and cloud. For enterprises operating across multiple geographies and regulatory environments, compliance becomes an automatic, platform-enforced standard. Apache Spark Processes data at a scale and speed legacy systems cannot approach — accelerating ETL pipelines, reducing processing times from hours to minutes, and enabling real-time analytics. MLflow and AutoML Close the loop between data and AI, giving teams a unified environment to experiment, train, track, and deploy models against the same governed, high-quality data that powers analytics. The result is AI that is faster to build, easier to trust, and simpler to scale. Unlike traditional architectures that require multiple technologies and constant data movement between systems, Databricks provides one integrated environment supporting: Data engineering Data warehousing Real-time analytics Machine learning Generative AI Data governance The roadmapHow to begin your legacy-to-Lakehouse migration A successful Databricks migration is not a single event. It is a structured journey that balances speed with stability. The most effective enterprise migrations follow a phased approach: Audit first. Run a comprehensive data audit to catalog existing sources, pipelines, and quality gaps. Govern before you migrate. Establish a governance framework using Unity Catalog ahead of moving data. Prioritize high-value workloads. Migrate these early to demonstrate ROI quickly. Run in parallel. Keep legacy and Lakehouse environments live together to validate outputs. Decommission progressively. Retire legacy systems as confidence in the new platform grows. The difference between migrations that succeed and migrations that stall is expertise. Certified Databricks engineers with hands-on mastery of

One Embedded Team, Four Live Applications | Adroitent Home/ Customer Stories/ Software Engineering Customer Story · IT Services · Software Engineering One Embedded Team, Four Live Applications, Zero Quality Drift The customer is a leading IT services provider delivering ongoing development and enhancement support to its own portfolio of end customers, spanning travel, logistics and e-commerce. Every project carried its own technology stack, its own release cadence and its own business priorities — and all of them ran at the same time. Adroitent Case FileIT ServicesAgile Delivery · Multi-Application Support4 min read The inflection pointFour codebases, four cadences, one delivery problem The customer needed reliable technical support that would let them seamlessly support their own end customers. That customer base was highly diverse, with multiple concurrent projects running in parallel. Each came with its own technology stack, release cadence and distinct business priorities, requiring a flexible, responsive and deeply collaborative support approach. 01Travel Travel-tracking platform Undergoing a complete rebuild from a legacy codebase carrying performance bottlenecks, response issues and long-term maintainability problems. 02Travel Configurable flight-notification system Designed to deliver highly accurate, rules-driven messaging based on dynamic conditions and individual customer requirements. 03Logistics Dock appointment & scheduling platform Enabling seamless coordination of pick-ups, deliveries and end-to-end supply chain visibility across multiple partners. 04E-commerce High-traffic e-commerce and CMS platform Powering online liquor retail operations in Australia, requiring scalability, reliability and a consistent user experience under load. The customer needed a delivery partner who could act as a true extension of their team — embedded, accountable and capable of driving agile execution across a diverse, evolving application landscape. They required consistent quality across multiple codebases, the flexibility to adapt to changing requirements, and the ability to meet externally committed go-live timelines for ongoing modernization work. The hard part was never building any one application. It was holding four of them to the same standard, at the same time. The interventionOne embedded team, one repeatable framework Adroitent placed a single embedded team across the portfolio and gave it a delivery framework that did not change from application to application. The cadence flexed; the standards did not. An agile model built for shifting priorities Adopted a hybrid Scrum model with daily backlog grooming, so priorities could move between applications without stalling the team Applied three-point estimation at task level, alongside enforced coding standards Ran UAT across every application in the portfolio, not only the ones nearing release Time-boxed modernization, delivered in parallel Each project was scoped end to end, from planning through release, on an agile overlapping-phase timeline Those timelines were built to hold against the customer’s own externally committed go-live dates Governance the customer could see Daily stand-ups kept work synchronized before issues ever reached the customer Weekly management reviews tracked risk, with status visible in JIRA in real time rather than in a periodic report The result was a consistent, auditable view of progress across every work stream The game changer QA discipline that scaled across four codebases Every check-in required a QA test request — no exceptions by application Every change set went through code review before it moved forward Continuous regression testing stayed in place throughout, not just before releases This consistency let one team support four live, unrelated applications without quality drifting on any of them QA per check-inMandatory code reviewContinuous regressionPortfolio-wide UAT By the numbers 4 Live applications supported 1 Embedded delivery team 3 Industries served in parallel 0 Quality drift across codebases The payoffFrom portfolio strain to a reusable model Optimized portfolio delivery Four live applications across different domains were supported by a single embedded team, reducing the need for separate pods, separate governance and separate QA. Improved quality and velocity Concurrent delivery was sustained across all four applications without compromising quality or delivery speed. Reduced maintenance burden Reliability issues and accumulated technical debt were addressed, creating a scalable and sustainable foundation across the portfolio. Enabled platform modernization Modernization was delivered on time, reducing migration risk and protecting business continuity. Created a reusable delivery model A repeatable framework covering grooming, code review, regression testing and JIRA visibility now extends to future engagements. The stack behind itTools & technology Build .NETMVC patternSQL Delivery & governance TFS — code repositoryJIRA — requirements & acceptance criteriaConfluence — user stories & project docsVisio — design This engagement was delivered as part of Adroitent’s Software Engineering Services practice. Good to knowFrequently asked questions What is an embedded delivery team, and how does it differ from staff augmentation? An embedded delivery team operates as an extension of the customer’s own organization, owning outcomes across planning, build, QA and release rather than filling individual seats. Staff augmentation supplies people who work under the customer’s process and management. An embedded team brings its own delivery framework, governance and accountability, which is what makes it viable to support several unrelated applications at once. How do you maintain consistent quality across multiple concurrent applications? Consistency comes from making quality gates mandatory rather than release-dependent. Every check-in raises a QA test request, every change set passes code review before it moves forward, and regression testing runs continuously instead of only ahead of a release. Because the same gates apply to every codebase, quality does not depend on which application a developer happened to be working in that week. What is a hybrid Scrum model with daily backlog grooming? A hybrid Scrum model keeps Scrum’s sprint cadence, ceremonies and estimation while borrowing continuous-flow practices for work that cannot wait for the next sprint boundary. Grooming the backlog daily rather than once per sprint lets priorities shift between applications without stalling a team, which matters when several customers set their own release cadences. What is three-point estimation and why apply it at task level? Three-point estimation captures an optimistic, most-likely and pessimistic figure for each item and derives a weighted estimate from the three. Applying it at task level rather than story level exposes uncertainty early and produces forecasts reliable enough to commit to externally, which is essential when go-live