Navigating the SQL Server to Databricks Migration: Suggestions for a Seamless Transition

The crucial for modernization

Conventional database options like SQL Server have struggled to maintain up with the calls for of recent knowledge workloads because of an absence of help for AI/ML, streaming capabilities, and excessive prices. Enterprises more and more undertake cloud-native options like Databricks to realize flexibility, scalability, and price effectivity whereas enabling superior analytics use circumstances.

Key advantages of Databricks over SQL Server

• Unified platform: Combines structured and unstructured knowledge processing with AI/ML capabilities. Additional, Unity Catalog gives complete knowledge governance for all knowledge belongings.
• Scalability: Databricks, by way of its cloud-native infrastructure, can scale assets elastically in line with workload calls for. This structure permits it to deal with giant, advanced workloads with improved question efficiency and decreased latency.
• Price effectivity: Pay-as-you-go cloud pricing fashions scale back infrastructure {hardware} prices. Decrease administrative prices and improved useful resource utilization additionally considerably scale back the general TCO.
• Superior analytics: Databricks gives built-in options for superior analytics use circumstances comparable to AI/ML, GenAI, and real-time streaming. Additional, with Databricks SQL, customers can combine their BI instruments of alternative, thus empowering them to carry out advanced analyses extra effectively.

Architectural deep dive

Migrating from SQL Server to Databricks entails rethinking your knowledge structure to leverage the Lakehouse mannequin’s strengths. Understanding the important thing variations between the 2 platforms is crucial for designing an efficient migration technique. Key variations between SQL Server and Databricks: 

Enterprise knowledge migration

Migrating knowledge from on-premises SQL Server to cloud-based Databricks requires choosing the suitable instruments and methods based mostly on workload measurement and complexity.

Really helpful approaches for knowledge migration: 

1. Databricks Lakeflow Join: Lakeflow Join affords a completely managed SQL Server connector for seamless knowledge ingestion from SQL Server into Databricks lakehouse. For extra info, check with Ingest knowledge from SQL Server.
2. Leveraging Databricks Lakehouse Federation: Databricks Lakehouse Federation permits for federated queries throughout completely different knowledge sources, together with SQL Server.
3. ISV Companions: Databricks ISV Companions, comparable to Qlik and Fivetran can replicate knowledge from SQL Server to the Databricks Delta desk.

Code migration

Migrating from T-SQL to Databricks SQL requires refactoring SQL scripts, saved procedures, and ETL workflows into Databricks-compatible codecs whereas optimizing efficiency. Databricks has mature code converters and migration tooling to make this course of smoother and extremely automated.

Databricks Code Converter (acquired from BladeBridge) can mechanically convert the logic into both Databricks SQL or PySpark notebooks. The BladeBridge conversion device helps schema conversion (tables and views) and SQL queries (choose statements, expressions, features, user-defined features, and many others.). Additional, saved procedures will be transformed to modular Databricks workflows, SQL Scripting, or DLT pipelines. 

ETL Workflow modernization

Databricks affords a number of choices for modernizing ETL pipelines, simplifying advanced workflows historically managed by SSIS or SQL Agent.

Choices for ETL orchestration on Databricks:

1. Databricks Workflows: Native orchestration device supporting Python scripts, Notebooks, dbt transformations, and many others.
2. DLT (DLT): Declarative pipelines with built-in knowledge high quality checks.

BI and analytics instruments integration

Databricks SQL permits organizations to fulfill knowledge warehousing wants and help downstream purposes and BI Dashboards. Repointing BI instruments like Energy BI or Tableau is crucial after migrating knowledge pipelines to make sure enterprise continuity.

Microsoft Energy BI, a generally seen downstream software in numerous buyer environments, sometimes operates on high of SQL Server’s serving layer.

Energy BI integration finest practices

1. Use DirectQuery mode for real-time analytics on Delta tables. DirectQuery is 2- 5x quicker with Databricks vs. SQL Server.
2. Leverage materialized views in Databricks SQL Warehouse for quicker dashboards through aggregations.
3. Use SQL Serverless Warehouse for the most effective efficiency for high-concurrency, low-latency workloads.

Under is a future-state structure that works nicely for optimizing BI fashions and semantic layers to align with enterprise wants. It features a bronze, silver, and gold layer to feed dashboards, purposes, AI, and ML use circumstances. 

Validation framework

Validation ensures that migrated datasets preserve accuracy and consistency throughout platforms. Really helpful validation steps:

1. Carry out schema checks between the supply (Netezza) and the goal (Databricks).
2. Evaluate row counts and combination values utilizing automated instruments like Remorph Reconcile or DataCompy.
3. Run parallel pipelines throughout a transitional part to confirm question outcomes

Information switch and organizational readiness

Upskilling groups on Databricks ideas, Delta Lake structure, Databricks SQL, and efficiency optimization is crucial for long-term success. Coaching suggestions:

• Practice analysts on Databricks SQL Warehouse options.
• Present hands-on labs for engineers transitioning from SSIS to DLT pipelines.
• Doc migration patterns and troubleshooting playbooks.

Predictable, low-risk migrations

Migrating from SQL Server to Databricks represents a major shift not simply in expertise however in method to knowledge administration and analytics. By planning totally, addressing the important thing variations between platforms, and leveraging Databricks’ distinctive capabilities, organizations can obtain a profitable migration that delivers improved efficiency, scalability, and cost-effectiveness. 

The migration journey is a chance to modernize the place your knowledge lives and the way you’re employed with it. By following the following tips and avoiding frequent pitfalls, your group can easily transition to the Databricks Platform and unlock new potentialities for data-driven decision-making. 

Keep in mind that whereas the technical features of migration are essential, equal consideration must be paid to organizational readiness, information switch, and adoption methods to make sure long-term success.

What to do subsequent

Migration will be difficult. There’ll all the time be tradeoffs to stability and sudden points and delays to handle. You want confirmed companions and options for the migration’s folks, course of, and expertise features. We advocate trusting the specialists at Databricks Skilled Companies and our licensed migration companions, who’ve in depth expertise delivering high-quality migration options promptly. Attain out to get your migration evaluation began.

You must also try the Modernizing Your Knowledge Property by Migrating to Azure Databricks eBook. 

We even have a whole SQL Server to Databricks Migration Information–get your free copy right here.