TL;DR
In the data lakehouse vs data warehouse decision, a warehouse is usually the simpler choice when governed SQL reporting drives most of your BI work. A lakehouse earns its place when dashboards, machine learning and streaming must share one governed copy of data in open formats. Neither option is automatically cheaper, because compute, idle capacity, data movement and people cost more than storage. Test both against your real dashboards under real user load, and trace every access rule from table to report. The products overlap, so the catalog, the query engine and your team’s skills matter more than the label. If you switch, migrate in slices with parallel runs, and treat keeping the warehouse as a valid result.
Key Takeaways
Start from your BI workloads and service levels rather than an architecture label. A data warehouse fits governed SQL reporting because it has the fewest moving parts to run. A data lakehouse fits when BI and ML must share one governed copy of data in open formats. Cheap object storage does not make a lakehouse cheap, so compare compute, idle time, egress and people. Major warehouses read Iceberg and lakehouses serve SQL, so catalog, engine and skills decide more than storage. Migrate in slices with parallel runs, and remember that keeping the warehouse is a legitimate outcome. Watch on YouTube
Databricks vs Snowflake 2026: Pricing, AI, Security Compared
Kanerika puts the leading lakehouse platform and the leading cloud warehouse side by side on pricing, AI support and security. It is a quick way to see how the two operating models differ before you read the decision guide below.
The Architecture Meeting Where Both Proposals Sound Right
A mid-sized consumer goods company holds its quarterly architecture review, and this one opens with two requests. The finance lead wants certified revenue dashboards that refresh before the morning close call. Across the table, the data science lead wants the same sales history, plus raw clickstream JSON, for a demand forecasting model.
The platform lead has two proposals on screen. One extends the cloud warehouse that already runs reporting. The other moves to a lakehouse, so that both teams work from one copy of the data. Each comes with a vendor deck promising lower cost and faster queries.
So the room lands on the real question behind data lakehouse vs data warehouse. Which one serves this BI team’s workloads, budget and governance rules better, and how hard would it be to change course later? The answer depends on a few things you can measure before anyone signs.
Data Lakehouse vs Data Warehouse: The Short Answer
A data warehouse is the simpler default when most of your analytics is governed SQL reporting on modelled data. A data lakehouse is the better fit when BI has to share one governed copy of data with data science, streaming or semi-structured workloads. Neither is automatically cheaper or faster, and each borrows features from the other.
What a Data Warehouse Is Built For
A data warehouse is a managed SQL store tuned for reporting on modelled data. Data is cleaned and shaped before it lands, so the tables analysts query stay stable and documented. Snowflake, Google BigQuery, Amazon Redshift and the Fabric Warehouse all work this way, as this overview of the cloud data warehouse explains.
Most warehouses also query JSON and other semi-structured data. Their strengths are predictable SQL serving, mature security and a short list of things to run. For the layers inside one, see this guide to data warehouse architecture .
What a Data Lakehouse Is Built For
A data lakehouse puts warehouse-style tables on top of open file formats in cloud object storage. Table formats such as Delta Lake, Apache Iceberg and Apache Hudi add transactions and schema enforcement to plain Parquet files. Because the files are open, several engines can read them, from Spark notebooks to SQL endpoints.
Databricks built its platform around this pattern, and as of November 2026, Fabric, BigQuery and Snowflake support it too. For the full definition, see this explainer on what a data lakehouse is .
Where This Comparison Stops
Because this guide is a two-way decision between a lakehouse and a warehouse, it leaves raw data lakes aside. If you are still weighing one, the comparisons of data lake vs lakehouse and data lake vs data warehouse cover that ground. To see what real lakes hold and feed, our roundup of data lake examples walks through eight designs.
How a Lakehouse and a Warehouse Differ Where a BI Buyer Feels It
Both designs share four layers, which are storage, a table format, a compute engine and a catalog for permissions and metadata. The real difference, however, is who owns each one. In a warehouse, the vendor runs all four and hands you SQL.
In a lakehouse, by contrast, you own the storage and the files, while the engine and catalog can come from one vendor or several. That ownership brings flexibility, and it also brings chores a warehouse team never sees, such as compacting thousands of small files.
Because of that one design choice, most rows in the comparison below follow the same pattern.
Dimension Data warehouse Data lakehouse Storage and format Vendor-managed storage and format Your object storage, in Delta Lake or Iceberg Data types Structured and semi-structured Structured, semi-structured and unstructured files Transactions Multi-table ACID transactions in SQL ACID per table; multi-table support varies by engine User concurrency Scales with warehouse size or clusters Depends on the SQL engine and file layout ML and unstructured data Usually exported to another tool Spark, Python and SQL on the same tables Governance Inside one engine’s permission model In a shared catalog every engine must honour Lock-in Data leaves through exports or shares Open files, though catalogs can still tie you in Operating effort Low, since the vendor tunes storage Higher, since compaction and layout are your job Pricing unit Credits, slots or capacity units DBUs or capacity units, plus cloud storage
Read the last two rows together. A warehouse charges for a managed service and does most of the tuning. A lakehouse, by contrast, gives you open files and more choice of engine, but hands more work to your team.
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Data Analytics Services
Kanerika builds governed reporting and self-service BI on Microsoft Fabric, Databricks or Snowflake, matched to the workloads your teams actually run.
Explore Data Analytics Which Workloads Each One Wins
Start with the work, not the architecture. List your top twenty dashboards, models and data feeds, with their users and refresh times. That list settles more debates than any vendor slide.
Governed Reporting and Executive Dashboards
Finance close packs, regulatory returns and board dashboards need certified numbers, stable schemas and strict access rules. A warehouse is the simpler home for this work, because SQL modelling, row-level security and predictable query times come built in. A lakehouse can do the same job, but your team builds more of the guardrails by hand.
High-Concurrency Self-Service BI
Hundreds of people opening dashboards at 9 a.m. is a different load than one analyst running a heavy query. Both platforms can serve it, yet neither does it well without a tuned serving layer. In Power BI, for example, Import and Direct Lake modes answer from the in-memory VertiPaq engine. DirectQuery, on the other hand, sends queries back to the source, as Microsoft’s Direct Lake overview explains.
That storage-mode choice often matters more than the platform underneath, as this guide to Direct Lake semantic models in Power BI shows. So test concurrency on whichever option you shortlist.
Checklist
Power BI Best Practices Checklist
Model design, storage modes, refresh and performance checks to run before you blame the platform for slow dashboards.
Get the Checklist → BI That Shares Data With Data Science and ML
This is where the lakehouse finally pulls ahead. When analysts and data scientists need the same customer history, a lakehouse lets dashboards and Python notebooks read one governed copy. Even Microsoft’s own Fabric decision guide points Spark and Python developers to the Lakehouse and T-SQL developers to the Warehouse.
With a warehouse, the usual pattern is to export data to a separate ML platform. That works, but every extra copy needs its own permissions, refresh job and reconciliation. A layered design such as the medallion architecture instead keeps raw, cleaned and business-ready tables in one place for both groups.
Streaming, Semi-Structured and AI Data Preparation
Event streams, logs and documents for retrieval-augmented generation sit naturally in a lakehouse, since files and tables live side by side. Warehouses have closed much of the gap on JSON, though, so check your actual formats first. Truly unstructured content such as PDFs and images still favours the lakehouse.
The same logic runs through the wider modern data stack , where storage follows the workloads that touch it. Teams that plan to sell data products should also note that outside buyers often expect open formats. This guide to building a data monetization strategy covers that point.
So if your list is mostly governed reports, the warehouse fits. But if it mixes reports with models, streams and files, the lakehouse starts to pay for its extra work.
Data Lakehouse vs Data Warehouse Costs: What You Actually Pay For
Storage is the line item vendors love to compare, although it is usually the smallest one. The bill that surprises teams comes from compute, idle capacity, data movement and the people who keep the platform running.
Pricing Units Differ, So Compare Workloads, Not List Prices
Because every platform meters compute in its own unit, list-price comparisons are close to useless. For example, Snowflake bills credits per second while a virtual warehouse runs, with a 60-second minimum at each start. A suspended warehouse uses no credits, according to Snowflake’s compute cost guide . BigQuery, in contrast, charges either per TiB processed or per slot-hour of reserved capacity, as its pricing page sets out.
Databricks measures usage in DBUs billed per second, per Databricks pricing . Classic compute also runs in your own cloud account, as the Databricks architecture docs explain, so those machines show up on your cloud bill too. Finally, Microsoft Fabric sells capacity as F SKUs measured in capacity units, and a pay-as-you-go capacity can be paused to stop billing.
Platform How compute is metered What makes the bill climb Snowflake Credits per second while a warehouse runs Warehouses left running or oversized Google BigQuery Per TiB processed, or per slot-hour Unpartitioned scans and chatty dashboards Databricks DBUs per second, plus cloud VMs on classic compute Always-on clusters and small-file overhead Microsoft Fabric Capacity units on an F SKU Capacity sized for peaks and never paused
The Six Cost Lines Most Comparisons Skip
A fair comparison prices each of these lines for both options rather than just storage and headline compute.
BI serving compute. Dashboard queries under peak concurrency, often the largest recurring line.Transformation compute. Scheduled jobs that build modelled tables whether anyone looks or not.Idle capacity. Clusters, warehouses or capacities left running between workloads.Data movement and egress. Copies between tools and cross-region reads. Snowflake’s Iceberg tables documentation notes that querying a table stored in another region incurs egress costs.File maintenance. Compaction and small-file cleanup on the lakehouse side.People. Platform engineers, catalog administrators and on-call time.None of these lines appears in a storage price comparison. That is why two teams with the same data volume can see very different monthly bills.
A Workload-Normalised Cost Worksheet
To compare fairly, cost the same workloads on both options. For each one, monthly cost equals platform compute, plus storage and replicas, plus data movement, plus operating labour, plus governance tooling. Keep the one-time migration cost separate, so it does not distort the run rate.
Cost line Where the number comes from BI serving compute Peak-hour pilot on your top dashboards Transformation compute Last quarter’s job history, rerun on each candidate Idle capacity Auto-suspend or pause settings plus usage logs Storage, replicas and egress Data volume, retention, copies and region map Operating labour and tooling Named roles, hours per month, catalog and monitoring tools
Then fill in one column per candidate. The worksheet is illustrative, not a pricing benchmark, but it puts people and idle time on the same page as compute. The people line is the one most spreadsheets leave blank, and it is often the line that settles the call. A lakehouse is not inherently cheaper, and a warehouse is not inherently expensive.
Governance and Performance Under Real BI Load
Two questions decide whether a pilot result can be trusted. Do access rules follow the data all the way to the report, and do dashboards stay fast when everyone opens them at once?
Follow Access Rules From Table to Report
Warehouses keep security inside one engine, so it is easy to reason about. For instance, the Fabric Data Warehouse supports object, column and row-level security plus dynamic data masking, per Microsoft’s security docs . This walkthrough of data warehouse security in Microsoft Fabric shows the setup. Similarly, Snowflake’s Horizon governance covers masking policies, row access policies and lineage, as this guide to Snowflake data governance shows.
Lakehouses, on the other hand, move security into a catalog that several engines share. Unity Catalog applies privileges, attribute-based policies and row and column filters, and it tracks lineage through to dashboards, per the Databricks docs . The weak point is the hand-off. A rule set in one engine may not follow the data into a BI model that imports a copy.
So trace one sensitive column, such as salary, from the table to the final report on each option. Regulated firms feel this most, as this look at a data lakehouse for financial services explains.
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Data Governance Services
Kanerika designs access policies, lineage and data quality rules that follow your data from the table to the report, on a warehouse or a lakehouse.
Explore Data Governance Benchmark Dashboards, Not Single Queries
Vendor benchmarks are run by vendors, on datasets and settings they choose, so they reflect the vendor’s tuning. They therefore say little about your dashboards at 9 a.m. on the first working day of the month. Instead, a useful pilot measures what users feel, with real reports and real concurrency.
Then check these six results before you trust any pilot.
Median and 95th-percentile dashboard load times. Cold-cache and warm-cache runs, since the first open of the day is the one users remember. A concurrency burst that matches your busiest hour. Refresh windows that finish before business hours. Workload isolation, so heavy transformation jobs do not slow dashboards. Cost per pilot day, recorded next to the speed numbers. A platform that passes these checks on your own reports is a safer bet than one that wins a published benchmark. Keep the pilot data too, because it becomes the baseline for any later migration.
The Convergence: Warehouses Read Open Tables, Lakehouses Serve SQL
A few years ago, this was a clean fork in the road. Since then, the two camps have borrowed so much from each other that product names alone tell you little.
On the warehouse side, Snowflake supports Apache Iceberg tables in your own storage or Snowflake-managed storage, as this guide to Snowflake Iceberg tables covers. BigQuery also offers Iceberg managed tables that keep data in buckets you own.
On the lakehouse side, Databricks SQL warehouses serve BI tools such as Power BI and Tableau, as this guide to the Databricks SQL warehouse explains. Microsoft Fabric goes furthest, since its Warehouse and Lakehouse both store Delta tables in OneLake and share one SQL engine.
The format war has also cooled. Delta Lake’s UniForm lets Iceberg clients read Delta tables, and Databricks agreed to acquire Tabular , founded by Iceberg’s original creators, in June 2024. Meanwhile, catalogs are opening up through the open-source Unity Catalog server and Apache Polaris .
So what still differs? Mostly things that never appear on a spec sheet. The catalog that enforces your policies, the engine’s behaviour under concurrency, your team’s skills and the cost of switching later decide the outcome.
Fabric, Databricks and Snowflake: Where the Choice Shows Up
Many teams make this decision inside a platform they already use. The table below maps each platform’s options to what your pilot should prove. For a wider view, see this Databricks vs Snowflake vs Fabric comparison .
Platform Warehouse-side option Lakehouse-side option What your pilot must prove Microsoft Fabric Fabric Warehouse (T-SQL) Fabric Lakehouse (Spark and Delta) Direct Lake speed on your models Databricks SQL warehouses over Delta tables Core lakehouse with Unity Catalog BI concurrency and semantic-model fit Snowflake Standard Snowflake tables Iceberg tables Catalog choice and egress exposure
Datasheet
Snowflake vs Databricks vs Microsoft Fabric
A side-by-side view of the three platforms where most warehouse and lakehouse decisions are made, from architecture to pricing model.
View the Datasheet → Microsoft Fabric: One Product, Two Engines
Fabric puts the choice inside one product, which makes it a clean test case for Microsoft Fabric buyers. Microsoft’s guide sends T-SQL developers, and workloads needing multi-table transactions, to the Fabric Data Warehouse . Spark developers and mixed data go to the Fabric Lakehouse , whose SQL analytics endpoint is read-only.
Databricks: Lakehouse First, SQL Warehouses Added
Databricks starts from the lakehouse and adds warehouse behaviour through SQL warehouses and Unity Catalog. SQL works well there, so a BI pilot should focus on dashboard concurrency and cost under always-on use. This guide to Databricks lakehouse architecture covers the design.
Snowflake: Warehouse First, Iceberg Tables Added
Snowflake, by contrast, starts from the warehouse and adds open storage through Iceberg tables and Horizon governance. So a BI-first team can keep the managed warehouse experience and still open selected tables to other engines. Still, test which catalog owns those tables, and where cross-region reads might add egress.
Which Should You Choose? A Data Lakehouse vs Data Warehouse Decision Table
Run the decision in three passes. First remove any option that fails a hard requirement, then match your main workloads to a starting point, and only then score what is left.
Clear the Hard Gates First
Before any scoring starts, some requirements rule out an option. So check these six gates with the people who own each one.
Data residency rules for every region you serve. BI tools and integrations that must connect on day one. The access-control model your auditors expect, down to rows and columns. Recovery targets, and who is responsible for backups. The data freshness each business process needs. Who will run the platform, and how many engineers that team has. Once a gate fails, the discussion ends for that option. With Iceberg tables on external storage, for instance, Snowflake’s documentation puts data protection and recovery on the customer. That matters if nobody owns the job today.
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Data Architecture Consulting
Assessment, architecture design and implementation advisory to choose a warehouse or a lakehouse and plan the move with governance built in.
Explore Data Architecture The Scenario Table
Once the gates are clear, match your dominant workloads to a starting point. Each row also names what would reverse the call, so you know which evidence to collect in the pilot.
Scenario Start with Why What would change the answer BI-only finance and regulatory reporting Warehouse Certified SQL models, mature security, low operating load A new ML or streaming need on the same data High-concurrency self-service BI on modelled data Warehouse Predictable serving for many users at once A lakehouse SQL engine that passes your concurrency pilot BI plus ML or feature engineering on the same data Lakehouse One governed copy for dashboards and notebooks ML stays small and can read a warehouse share Streaming or near-real-time operational analytics Lakehouse Files, streams and tables in one place A warehouse with streaming ingest that meets your freshness test Semi-structured events, documents or GenAI data preparation Lakehouse Handles files of any type next to tables Data is mostly JSON that your warehouse already queries well Small team with no platform engineers Warehouse Fewest moving parts to run A managed lakehouse service plus a partner to run it Multi-engine or open-format requirement Lakehouse on Iceberg or Delta Several engines read the same files A warehouse with Iceberg tables that meets the same need Existing warehouse that meets its service levels Warehouse, kept and tuned Migration risk with no clear payoff Cost or capability gaps you can measure Existing lakehouse with unstable dashboards Lakehouse, plus a warehouse serving layer for BI Fixes serving without moving engineering work A tuned lakehouse SQL engine that passes the pilot
Every row starts from one side. Where a row names both, one stays the system of record and the other only serves dashboards, and the table says which.
Score What Survives
If two options still pass the gates, score them on weighted criteria. The weights below are an editorial starting point, not an industry standard, so adjust them to your own priorities.
Criterion Weight What to measure BI service levels 25 Dashboard load times at peak, refresh windows Governance fit 20 Policies traced from table to report Total cost 20 The cost worksheet above, for the same workloads Team operability 15 Skills on hand, on-call load, hiring needs Workload breadth 10 ML, streaming and file workloads covered Migration risk 10 Conversion effort and parallel-run length
Then rate each option from 1 to 5 per criterion, multiply by the weight and divide by 5 for a total out of 100. A gap of a few points is a tie, so let the pilot results break it.
Migration Paths in Both Directions
Teams move between the two in both directions, although neither move has to be a big-bang cut-over. The safest migrations shift one workload at a time and keep the old path running until the new one proves itself.
Warehouse to Lakehouse
First, move the workloads the warehouse handles worst, such as ML feature preparation, raw history and semi-structured feeds. Keep certified BI marts on the warehouse until the lakehouse matches them on numbers, speed and access rules. Then convert SQL logic, stored procedures and security policies, which is usually where the hidden effort sits.
This guide to data warehouse migration covers the planning in more detail.
Lakehouse to a Warehouse Serving Layer
Sometimes the lakehouse is right for engineering, but dashboards keep missing their targets. In that case, publish gold tables to a warehouse or a warehouse-style SQL engine for BI, and leave data engineering and ML in the lakehouse. Then name one system of record for each table, so nobody argues about which number is true.
The same idea, separating where data is managed from where it is served, comes up when teams compare a data fabric vs a data warehouse .
Gates Before Cut-Over
Whichever direction you take, the same seven gates keep a migration honest.
First, baseline today’s cost and performance on the current platform. Pick one representative workload, not the easiest one. Build the target path with named owners for data, security and cost. Then run old and new in parallel and reconcile every published metric. Validate access rules, recovery, freshness and peak load on the new path. Cut over in increments, with a rollback plan for each step. Finally, retire old assets only after business owners sign off. Some situations call for waiting instead. Hold off on a migration if any of these is true.
The current platform meets its service levels, and the business case rests on storage savings alone. Nobody can say who will run compaction, the catalog and cost monitoring after go-live. Metric definitions differ between teams, since moving them only moves the argument. The plan depends on a vendor benchmark rather than your own pilot. A migration that clears these gates rarely surprises anyone at cut-over. By contrast, one that skips them tends to rediscover each gate the hard way, usually during month-end reporting.
How Kanerika Helps Teams Choose and Migrate
Kanerika works across all three major platforms as a Microsoft Fabric Featured Partner, a Databricks Consulting Partner and a Snowflake Select Tier Partner. It also holds Microsoft’s advanced specialization in Data Warehouse Migration to Microsoft Azure. That spread matters here, because the recommendation is not tied to one vendor’s answer.
Its data architecture consulting comes in three engagement types. A Data Architecture Assessment reviews the current estate, identifies gaps and defines modernization priorities. An Architecture Design Project defines the target architecture, maps systems and workflows and produces an implementation blueprint.
Implementation Advisory then guides platform setup, reviews design decisions and supports rollout planning. The page also commits to governance-first design, so access controls, data quality rules and ownership models go in from the start. Its stated principle is foundation before features.
When code has to move, Kanerika’s migration services use FLIP, an AI-enabled, low-code platform that automates discovery, asset extraction, logic mapping, format conversion and validation.
One engagement shows the answer landing on the warehouse side. A leading soft-drink manufacturer runs eight filling plants that serve franchises across the US. It moved its reporting from legacy SSAS to Snowflake, as described in this Snowflake migration case study . The move delivered 28% annual cost savings, 45% faster refresh cycles and 50% fewer outages, while table-level refreshes gave near real-time work-order tracking.
That client was a BI-first buyer whose shareholders’ ERP systems already ran on Snowflake, so a cloud warehouse was the right call. The same assessment can just as easily point to a lakehouse when shared ML and streaming data dominate. Teams short on in-house engineers sometimes pair either build with data analytics outsourcing for day-to-day running.
Case Study
28% Cost Savings with Snowflake Migration for Analytics
A leading soft-drink manufacturer serving franchises across the US moved its reporting from legacy SSAS to Snowflake with Kanerika, gaining 28% annual cost savings, 45% faster refresh cycles and 50% fewer outages.
Read the Case Study → Choosing Between a Data Lakehouse and a Data Warehouse
A data warehouse remains the better choice for governed SQL reporting on modelled data, with the least operational load. A data lakehouse is the better choice when BI must share one governed copy of data with ML, streaming and open-format workloads. In both cases, compute, idle time, data movement and people drive the cost far more than storage.
So clear the hard gates, cost both options on the same workloads and pilot your real dashboards under real load. If you want a second opinion on the shortlist, talk to Kanerika’s data architects . Choose the warehouse when BI is the main job, and the lakehouse when BI is one of several jobs on the same data.
Frequently Asked Questions
What is the main difference between a data lakehouse and a data warehouse?
A data warehouse is a managed SQL store where the vendor runs storage, format, engine and security, tuned for governed reporting. A data lakehouse keeps tables in open formats such as Delta Lake or Iceberg on your own object storage, so several engines, including Spark and SQL, can work on the same governed copy of data.
Is a data lakehouse better than a data warehouse for BI?
Not by default. For governed SQL reporting and high-concurrency dashboards on modelled data, a warehouse is usually simpler to run and tune. A lakehouse becomes the better fit when BI shares its data with machine learning, streaming or document workloads. Pilot your real dashboards under peak load before you decide either way.
Is a data lakehouse cheaper than a data warehouse?
Not automatically. Object storage is cheap, but the larger costs are BI serving compute, transformation jobs, idle capacity, egress, file maintenance and the engineers who run the platform. A lakehouse often moves cost from the vendor’s bill to your team’s time. Cost the same workloads on both options before comparing totals.
Can a data lakehouse replace a data warehouse?
It can for many teams, since lakehouse platforms now include SQL engines that serve BI tools. Replacement works best when the lakehouse matches the warehouse on metric values, dashboard speed and access rules during a parallel run. If dashboards keep missing targets, keep the lakehouse and add a warehouse serving layer for BI.
Should I use a Fabric Lakehouse or a Fabric Warehouse?
Microsoft’s decision guide points T-SQL developers and workloads that need multi-table transactions to the Fabric Warehouse. Spark developers, and teams with mixed structured and unstructured data, are pointed to the Fabric Lakehouse. Both store Delta tables in OneLake and share one SQL engine, and the Lakehouse SQL analytics endpoint is read-only.
Can Power BI connect to both a lakehouse and a warehouse?
Yes. Power BI connects to cloud warehouses such as Snowflake and BigQuery and to lakehouse SQL engines such as Databricks SQL warehouses. In Microsoft Fabric, Direct Lake mode loads Delta tables from either a lakehouse or a warehouse into memory, which avoids the slower DirectQuery path for most dashboard queries.
Do I need both a lakehouse and a data warehouse?
Usually you need one system of record. Some teams run a lakehouse for engineering and ML and publish selected tables to a warehouse serving layer for dashboards. Others keep the warehouse and let a separate ML platform read from it. Either way, name which side owns each table so reported numbers stay consistent.
When should you choose a data warehouse over a lakehouse?
Choose a warehouse when most of your work is governed SQL reporting on structured or semi-structured data, when you have few platform engineers, and when strict, well-understood access controls matter most. A warehouse that already meets its service levels is also worth keeping, since migration adds risk without a clear payoff.
How do you migrate from a data warehouse to a lakehouse?
Move the workloads the warehouse handles worst first, such as ML features, raw history and semi-structured feeds. Keep certified BI marts in place until the lakehouse matches them. Then convert SQL logic and security policies, run both paths in parallel, reconcile every published metric and cut over in small, reversible steps.
