Azure Synapse has served as a dependable basis for SQL analytics at scale, and groups that constructed on it made a good choice on the time. Nevertheless, a platform primarily designed round an information warehouse is not constructed for the complete vary of what knowledge groups are actually anticipated to ship. Filling these gaps normally entails including extra companies, integrations and operational overhead, which builds up over time.
Migrating to Databricks is one option to handle this situation. On this weblog we handle the best way to method Synapse migration and what to bear in mind whereas executing it.
What a Synapse Migration Unlocks
Throughout Synapse migration engagements we see with our clients, three enterprise drivers come up persistently:
- Unified knowledge property. As knowledge platforms develop, the variety of companies concerned will increase too. For instance, Synapse Analytics swimming pools deal with one set of workloads, Spark swimming pools deal with one other, and serverless SQL offers advert hoc entry. Azure Knowledge Manufacturing unit typically sits alongside these to orchestrate all the things. Many organisations even have legacy SSIS workloads that also must be supported. None of those parts are problematic in isolation. Nevertheless, the problem arises when extra companies are launched, as this provides one other layer of governance, monitoring, permissions administration and operational overhead.
Databricks addresses this situation by unifying knowledge engineering, analytics, machine studying and governance on a single platform. Moderately than shifting between companies with completely different working fashions, groups can work towards the identical underlying structure and governance framework. The result’s diminished complexity, fewer integration factors, and a platform that is simpler to function at scale. - Future readiness. Focus of recent knowledge groups now’s shifted in direction of supporting machine studying fashions, real-time knowledge pipelines, and AI-powered purposes. All of those workloads rely upon the identical underlying knowledge. The problem is that conventional, warehouse-centric architectures weren’t designed for this degree of convergence and have been primarily focusing on BI wants. As necessities broaden, organisations typically discover themselves including extra companies and specialised instruments to fill functionality gaps.
Databricks is constructed for this convergence, unifying knowledge, analytics, and AI on a single platform. With Unity Catalog offering constant governance throughout knowledge, notebooks, and AI/ML property, and Unity AI Gateway extending these controls to fashions, brokers, and AI purposes, organizations can undertake new AI workloads with out including new governance silos. - Operational effectivity. Whereas most migration enterprise instances start with licensing prices, that is not often the place the largest financial savings come from. The bigger influence typically comes from lowering the variety of techniques that groups must function and assist. Fewer companies means fewer integrations, fewer handoffs between instruments, and fewer potential points.
Organisations which have already made the transfer are seeing tangible outcomes. For instance, Casey’s, the third-largest comfort retailer chain in the USA, migrated its analytics setting from Synapse to Databricks Lakehouse (previously Databricks SQL), lowering operational knowledge supply occasions from eight hours to 4. As one other instance, Italgas simplified its structure by eradicating each Synapse and Azure Evaluation Companies. The corporate reported a 73% discount in workload prices whereas serving each Energy BI and AI-driven analytics straight from Databricks.
Whereas the specifics differ from organisation to organisation, the sample stays constant: less complicated architectures, sooner knowledge supply and a platform higher aligned to the calls for positioned on trendy knowledge groups.
Understanding What You Are Truly Migrating
One factor that always catches groups off guard early in a Synapse migration is the scope of what they’re shifting. Though Azure Synapse is usually thought-about a single platform, in follow it contains a wide range of distinct companies working underneath one model, every of these could require completely different migration methods and has a special degree of complexity.
Most migrations spend nearly all of their effort on Devoted SQL Swimming pools, the place enterprise logic, saved procedures, distribution methods, indexing choices, and efficiency optimisations have amassed over years. However the complexity not often stops on the SQL. The identical migration normally has to account for orchestration (Azure Knowledge Manufacturing unit and Synapse Pipelines), permissions and governance (SQL permissions plus Microsoft Purview, with lineage typically stitched collectively manually), and BI and third-party connectivity (semantic fashions, experiences, and downstream instruments wired straight into Synapse endpoints). That is the a part of the property that calls for probably the most redesign, testing, and validation – and the half most probably to be underestimated.
Serverless SQL Swimming pools are typically less complicated, as a result of they primarily present a question layer over recordsdata in an information lake. Migration right here is generally about re-establishing views, exterior tables, and entry patterns relatively than redesigning complicated workloads. Spark Swimming pools are the best element to maneuver, since Synapse Spark and Databricks are each constructed on Apache Spark and notebooks can typically migrate with comparatively few adjustments.
The vital level is that these parts transfer at completely different speeds, contain completely different stakeholders, and current completely different dangers. Organisations that method the migration as a single workstream with a single timeline typically underestimate each effort and complexity. That is the place schedules begin to slip and migration programmes start to broaden past their unique scope. Emigrate efficiently, groups ought to construction the migration journey.
The right way to Construction the Migration
A Synapse-to-Databricks migration is just not a single workstream. You are shifting three completely different compute fashions, consolidating governance, modernising orchestration, and remodeling years of amassed T-SQL logic. The groups that deal with this nicely deal with it as a structured programme relatively than a technical mission with the phased method.
Discovery. Each migration begins with understanding what is definitely working. Lakebridge Profiler scans the Synapse property and collects metadata on configuration, useful resource utilisation, question patterns, and efficiency baselines. Output is used to construct a TCO case.
Evaluation. As soon as the stock is in place, the subsequent step is knowing complexity. Lakebridge Analyzer evaluates the T-SQL codebase, classifying each object by complexity, flagging unsupported constructs, and mapping dependencies. Output can be utilized to evaluate the migration timeline and related efforts in addition to defining the precedence of property migrations. Begin with the lower- and medium-complexity workloads as low hanging fruits, and plan effort afterwards for probably the most crucial use instances.
Design. With visibility into the property, consideration shifts to the structure and design. The primary is method: lift-and-shift, modernise, or hybrid. For many Synapse migrations, hybrid is the best reply. Automated tooling handles the majority of code conversion to get off Synapse on schedule, whereas modernisation occurs incrementally as soon as workloads are working on Databricks.
The second determination is sequencing. A BI-first method tends to construct momentum sooner than beginning with ETL. Utilizing Lakehouse Federation, you possibly can expose Synapse knowledge by means of Unity Catalog earlier than the underlying pipelines have moved – and a sensible option to begin is to land the business-facing, augmented knowledge (your knowledge marts) on Databricks first, then put it straight in entrance of enterprise customers with Genie for natural-language analytics. Enterprise stakeholders see progress and worth early, whereas engineering groups modernise the extra complicated ETL beneath. Learn our weblog submit to outline the best migration method for you.
Pilot. Earlier than scaling, the migration technique must be validated end-to-end towards an actual workload. Decide one lighthouse use case, migrate it from ingestion by means of to consumption, and lower it over to manufacturing. A pilot validates the structure, governance mannequin, testing procedures, and tooling towards real-world circumstances, and produces reusable property for the waves that observe.
Migration in waves. For scale section, migration in waves is beneficial. Every wave is designed to ship a visual enterprise win and establishes the suggestions loop with finish customers.
Execution usually runs as 4 parallel workstreams: ingestion (shifting ADF and Synapse Pipeline workloads to Lakeflow Join), transformation (migrating T-SQL procedures and enterprise logic to Databricks), orchestration (shifting schedules and dependencies to Databricks Workflows), and consumption (repointing BI instruments and semantic fashions to Databricks SQL Warehouses). Working them in parallel lets groups ship worth early and retire Synapse on a predictable timeline.
Databricks helps Synapse migrations from a number of angles: advisory and supply from our Ahead Deployed Engineering staff, licensed Brickbuilder companions, and accelerators like Lakebridge that automate the heavy lifting. The purpose isn’t just to finish the migration however to construct the abilities and working mannequin the staff must maintain the platform lengthy after the mission ends.
Knowledge Ingestion
Earlier than changing SQL code, knowledge should first be ingested into the lakehouse. Databricks offers a number of choices relying on the supply techniques and operational necessities.
For a lot of frequent enterprise sources, Lakeflow Join presents a managed ingestion expertise with built-in connectors and automatic pipeline administration. On the identical time, Databricks is constructed on open storage codecs, permitting organizations to make use of a variety of third-party ingestion instruments. Options akin to Fivetran, Airbyte, and different ETL/ELT platforms can ingest knowledge straight into Delta Lake, enabling clients to combine with current knowledge integration ecosystems relatively than being tied to a single ingestion framework.
Code Conversion in Follow
With the info accessible within the lakehouse, the migration effort shifts to code conversion, which is usually probably the most complicated section of the migration. Whereas automated tooling handles nearly all of the interpretation, usually round 80-90%, the remaining effort is spent refining procedural logic and resolving patterns that can not be translated mechanically.
Under are some variations to be careful for in Synapse and Databricks syntax.
Eradicating Bodily Directives
The commonest conversion sample is the removing of bodily optimization directives. Devoted SQL Swimming pools rely closely on constructs akin to HASH distribution, ROUND_ROBIN distribution, REPLICATE distribution, and clustered columnstore indexes. These are basic to Synapse efficiency tuning however don’t have any direct equal in Databricks, so they’re usually omitted throughout migration.
As a substitute, Databricks depends on storage optimization and Liquid Clustering to enhance question efficiency. The previous is dealt with mechanically by means of Predictive Optimization, which constantly performs upkeep operations akin to file compaction, statistics assortment, and VACUUM for Delta tables. The latter is offered by Liquid Clustering, which organizes knowledge inside Delta tables utilizing a number of clustering columns to enhance question efficiency. Choosing the optimum clustering columns, nonetheless, is dependent upon understanding how knowledge is queried, a activity that’s typically troublesome in follow and regularly adjustments as workloads evolve. To cut back this operational burden, Databricks launched CLUSTER BY AUTO, which mechanically identifies and constantly refines clustering columns based mostly on noticed question entry patterns. Collectively, these capabilities considerably cut back the quantity of handbook bodily tuning required in comparison with Devoted SQL Swimming pools.
Bodily design choices that consumed vital engineering effort in Synapse are merely dropped. The platform handles what was beforehand handbook.
Operate Remapping
Mostly used T-SQL features have direct Databricks equivalents, and Lakebridge handles the overwhelming majority of mappings mechanically.
| T-SQL | Databricks SQL |
|---|---|
| GETDATE() | CURRENT_TIMESTAMP() |
| ISNULL(a, b) | COALESCE(a, b) or IFNULL(a, b) |
| LEN(s) | LENGTH(s) |
| CHARINDEX(sub, str) | LOCATE(sub, str) |
| SELECT TOP 10 | SELECT … LIMIT 10 |
| CONVERT(INT, col) | CAST(col AS INT) |
The extra frequent supply of points is just not the perform mappings themselves however behavioral variations that have an effect on ends in refined methods. String comparability is an effective instance. Synapse Devoted SQL Swimming pools usually function with case-insensitive collations, whereas Databricks SQL is case-sensitive by default. Logic that implicitly depends on case-insensitive matching could return completely different outcomes after migration. The place wanted, comparisons needs to be made express utilizing LOWER() or UPPER() on either side. Syntax conversion is normally simple; semantic variations require extra care.
Saved Procedures: Migrate First, Optimize Second
With native saved process assist in Databricks, most Synapse procedures can migrate with their total construction intact. Parameters, variables, conditional logic, and DML operations are all supported.
The process itself isn’t the issue. The complexity lives inside it: cursors, row-by-row processing, dynamic SQL, and Synapse-specific efficiency optimizations. These patterns require judgment, not simply translation.
SCD Sort 2: Preserving Historical past with Delta Lake
Slowly Altering Dimensions are one of many areas the place Synapse implementations differ probably the most. Many organizations have amassed customized saved procedures and merge logic over years. The migration purpose is to not reproduce that implementation precisely however to protect the enterprise requirement: sustaining historic variations of dimension information whereas protecting the present state queryable.
A standard Databricks method makes use of two steps. First, expire the information which have modified. Then insert the brand new variations.
Delta Lake’s ACID transactions make this sample secure even when a number of operations are concerned.
Error Dealing with
Many Synapse saved procedures depend on TRY…CATCH blocks to seize failures or write audit information. Databricks SQL offers native equivalents by means of situation handlers, so most current patterns can keep SQL-based.
Easy eventualities like audit logging and managed failures usually translate straight. Extra complicated workflows may have extra design, notably the place downstream coordination by means of Databricks Workflows is concerned.
What Area Expertise Teaches
A couple of classes come up persistently throughout Synapse migrations, no matter group dimension or property complexity.
Begin with evaluation, not conversion. Run Lakebridge Profiler and Analyzer earlier than writing a single line of transformed code. Get clear on precise utilization, scope, complexity, and dependencies – and use that knowledge to chop scope the place you possibly can.
Automate aggressively. Lakebridge handles 80–90% of code conversion. Focus engineering time on the ten–20% that wants human judgment – cursors, dynamic SQL, complicated error dealing with.
By no means underestimate the validation. In follow, validation typically consumes extra effort than the migration itself. The best method is to run reconciliation after each migration wave, evaluating row counts, aggregations, hash-based report comparisons, and tolerance-based checks for values the place precise equality is just not acceptable. Lakebridge Reconcile helps this throughout all these dimensions. For business-critical workloads, working each environments in parallel earlier than closing cutover lets groups evaluate outputs side-by-side whereas customers proceed working with acquainted experiences.
Steer from Synapse-shaped pondering. A great instance is desk design. Groups regularly try to map Synapse HASH distribution keys on to Delta Lake partition columns. Usually, this introduces pointless complexity and poor efficiency traits. Excessive-cardinality values akin to buyer IDs or order IDs are not often appropriate partition keys and are sometimes higher dealt with by means of liquid clustering and Databricks’ automated optimisation capabilities like predictive upkeep.
Do not recreate what the platform now handles. Migrations create a possibility to simplify architectures relatively than reproduce them precisely. Delta Lake, automated optimisation, and trendy lakehouse patterns get rid of lots of the handbook tuning methods that have been mandatory in conventional warehouse environments. Carrying each historic optimisation determination into Databricks typically preserves outdated constraints with out preserving the explanations these constraints existed.
Put together operational readiness. Delta tables naturally accumulate small recordsdata as incremental workloads run over time. With out compaction and upkeep processes, efficiency can regularly degrade. Groups coming from conventional knowledge warehouse platforms are sometimes stunned that storage optimisation turns into a part of the continued working mannequin. It is not troublesome to handle, however it does must be deliberate from the start.
Plan for change administration. Most Synapse groups are new to Databricks, and underinvesting in enablement is likely one of the commonest causes initiatives miss adoption targets. Work the enablement plan as severely because the technical plan.
Keep away from early decommissioning of Synapse. Most profitable migrations hold the legacy setting accessible for a interval after manufacturing workloads have moved. Compute will be paused to minimise prices whereas preserving a rollback possibility if sudden points emerge. Extra importantly, sustaining that security internet provides enterprise stakeholders confidence whereas the brand new platform proves itself underneath real-world utilization.
Migrating from Synapse to Databricks isn’t only a know-how mission. At its core, it entails simplifying a platform that has change into more and more complicated over time, whereas establishing a basis that may assist the subsequent era of analytics, AI and knowledge merchandise. Whereas the technical work is vital, the organisations that profit most from these migrations are those who use the chance to simplify their structure, get rid of pointless complexity and modernise their working practices concurrently. The better profit is ending up with an information platform that is less complicated to function, simpler to increase and higher aligned with the organisation’s future path.
What to do subsequent
For those who’re in the beginning of a Synapse migration:
