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Program overview
Online
On-site
Hybrid
Databricks SQL and Migration from T-SQL
Migrate T-SQL workloads to Databricks SQL — map syntax differences, rewrite scripts for the lakehouse, and tune queries with warehouses, Photon, and Delta performance patterns.
Duration:
2 Days
Level:
Advanced
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1500+ users onboarded
Who will Benefit from this Training?
Training Objectives
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
Download Curriculum
- Databricks SQL Workspace and Warehouses
- SQL editor, SQL warehouses, query history, and dashboards in the Databricks SQL workspace
- Warehouse types: Classic versus Pro versus Serverless — differences and when to use each
- How warehouse sizing and autoscaling affect interactive and scheduled SQL workloads
- Navigating query history to review past runs and share SQL with teammates
- Organising SQL assets for migration projects and recurring analytics
- Connecting Clients to Databricks SQL
- Connection essentials: server hostname, HTTP path, and authentication options
- Personal access tokens versus other auth patterns for SQL clients
- JDBC and ODBC connectivity for desktop tools and applications
- Partner Connect and common BI tool patterns for Databricks SQL
- Checklist for secure, least-privilege access from migration source systems
- Mapping T-SQL Syntax to Databricks SQL
- Side-by-side mental model: what stays familiar and what must change
- Row limiting: TOP versus LIMIT and ORDER BY pairing rules
- Case sensitivity for identifiers versus string comparisons
- Common migration gotchas that break scripts on first run
- Building a personal T-SQL to Databricks SQL cheat sheet during the course
- Temporary Objects, Identity Columns, and Data Types
- Temporary tables: CREATE #temp versus CREATE OR REPLACE TEMP VIEW and session scoping
- IDENTITY columns versus GENERATED ALWAYS AS IDENTITY
- Data type mapping: NVARCHAR versus STRING, DATETIME2 versus TIMESTAMP, MONEY versus DECIMAL
- Choosing safe casts when rewriting typed T-SQL scripts
- Validating migrated DDL before rewriting dependent queries
- Strings, Dates, Collation, and NULL Semantics
- Date and time functions: GETDATE() versus CURRENT_TIMESTAMP() and DATEADD equivalents
- String functions: LEN() versus LENGTH(), CHARINDEX() versus LOCATE(), ISNULL() versus IFNULL()/COALESCE()
- UTF8_LCASE collation for case-insensitive matching — syntax, use cases, and migration implications
- NULL comparison behaviour differences that change filter results
- Practical rewrite patterns for reporting queries that rely on T-SQL null and string defaults
- Migrating CTEs, Subqueries, and Window Functions
- CTE and subquery compatibility notes when moving from T-SQL
- Window function syntax that ports cleanly versus patterns that need adjustment
- Preserving business logic while simplifying nested SQL for Databricks
- Testing migrated analytical queries for row-level parity
- Hands-on: Migrate a set of T-SQL scripts to Databricks SQL with guided corrections and testing
- Query Execution, Photon, and the Query Profile
- How Databricks SQL executes queries: warehouse compute, Photon, and query compilation
- Reading the query profile: stages, tasks, spill, and bottleneck identification
- Linking profile symptoms to SQL and table-layout fixes
- Using query history metrics to prioritise optimisation work
- Baseline a slow query before and after each tuning change
- Writing Efficient SQL on Delta Lake
- Avoid SELECT *: project only required columns to reduce scan width
- Predicate pushdown and filters that enable Delta file skipping
- Partition pruning: writing predicates that reduce data scanned
- Prefer built-in functions over UDFs for Photon compatibility and speed
- Use MERGE INTO carefully; know when a full overwrite is more efficient
- Avoid unnecessary DISTINCT and ORDER BY on large sets without LIMIT
- Table Layout: Partitioning, Z-Order, OPTIMIZE, and VACUUM
- Partitioning strategy: column choice, cardinality, and anti-patterns
- Z-Ordering: choosing columns and running ZORDER BY to co-locate related data
- OPTIMIZE for the small-file problem — when to run and how to schedule it
- VACUUM: safe retention, time travel trade-offs, and scheduling
- Liquid clustering versus partitioning and Z-Ordering, plus a practical migration path
- Result cache and disk cache behaviour in Databricks SQL
- Capstone: Migrate and Optimise a T-SQL Workload
- Rewrite a representative T-SQL script set to run correctly in Databricks SQL
- Validate functional parity for filters, joins, and aggregations
- Profile slow queries and apply layout or SQL fixes (Z-Order, OPTIMIZE, predicates)
- Document remaining risks: collation, types, and warehouse sizing
- Hands-on: Deliver migrated, profiled SQL ready for a Databricks lakehouse environment
Hands-on Experience with Tools
Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
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100% Live
on-site/online training
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Hands-on
Labs and capstone projects
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Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
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Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
3
Why this course
- SQL on the lakehouse: Work natively in Databricks SQL warehouses, the SQL editor, and query history.
- Confident T-SQL migration: Translate temporary tables, functions, types, and collation behaviour without guesswork.
- Performance with Photon: Read query profiles and write SQL that leverages Delta file skipping and warehouse compute.
- Production-ready tuning: Apply partitioning, Z-Ordering, OPTIMIZE, liquid clustering, and caching patterns.
Training objectives
- Write and execute SQL queries natively in the Databricks SQL environment
- Map key syntax differences between T-SQL and Databricks SQL with practical examples
- Handle migration-specific challenges: temporary tables, case sensitivity, string collation, and date functions
- Rewrite existing T-SQL scripts to run correctly and efficiently in Databricks SQL
- Connect clients and BI tools to Databricks SQL using HTTP path, tokens, and JDBC/ODBC
- Read and interpret query plans and the Databricks SQL query profile
- Apply SQL optimisation techniques: partitioning, Z-Ordering, OPTIMIZE, liquid clustering, and caching
- Apply best practices for writing performant SQL in a lakehouse environment
Who will benefit
- Data Engineers
- Data Analysts
- Analytics Engineers
- BI Professionals
- Cloud Engineers
- Data Platform Teams
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Frequently Asked Questions
2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.