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Program overview
Online
On-site
Hybrid
SQL for Data Engineering (Advanced)
Build a strong foundation in advanced SQL for real-world Data Engineering, from scalable transformations to incremental modeling patterns. Learn how to implement deduplication, upserts, SCD design, and performance optimization techniques to build reliable analytics datasets with production-quality validation.
Duration:
2 days
Level:
Advanced
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1500+ users onboarded
Who will Benefit from this Training?
- Data Engineers (beginner to intermediate moving advanced)
- Analytics Engineers
- BI Engineers working with pipelines
- Backend engineers moving into data engineering
- Data Analysts transitioning to engineering workflows
Training Objectives
- Write complex SQL queries used in real-world data engineering pipelines.
- Build reliable transformations for ELT pipelines using advanced SQL patterns.
- Use window functions for analytics engineering and incremental model building.
- Perform deduplication, SCD logic, and CDC-style merges.
- Implement incremental load logic using watermark strategies and upsert/merge patterns.
- Optimize queries for performance using explain plans, indexes, and partitioning concepts.
- Validate data quality using SQL checks including uniqueness, null validation, and referential integrity.
- Design analytics-layer transformations including star schema modeling with fact and dimension tables.
- Build reusable SQL patterns suitable for production pipelines.
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Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
- Module 1: Advanced SQL Foundations for Data Engineering Pipelines
- How SQL is used in ELT pipelines (staging → cleansed → curated)
- Query readability and maintainability (style, naming, CTE structure)
- Joins in production (types, pitfalls, row explosion awareness)
- Robust filtering patterns (date ranges, late data windows concept)
- Hands-on: Lab: Refactor a messy query into a clean, production-style CTE pipeline
- Module 2: Advanced Query Patterns (CTEs, Subqueries, Set Operations)
- CTEs for stepwise transformations and debugging
- Correlated subqueries and when to avoid them
- Set operations (UNION/UNION ALL/EXCEPT/INTERSECT) for pipeline logic
- Reusable transformation patterns for common datasets
- Hands-on: Lab: Build a multi-step ELT transformation using CTE stages and set operations
- Module 3: Window Functions for Analytics Engineering and Incremental Models
- Window function fundamentals (PARTITION BY, ORDER BY, frames concept)
- Ranking and dedup logic (row_number, dense_rank)
- Running totals and moving windows (rolling KPIs concepts)
- Latest record and snapshot-building patterns using windows
- Hands-on: Lab: Build an incremental-friendly model using windows for latest-state and KPIs
- Module 4: Deduplication and Data Cleaning Patterns in SQL
- Dedup strategies (business keys, last-write-wins concepts)
- Handling nulls and standardizing types
- Detecting duplicates and producing quarantine outputs concepts
- Data standardization patterns (dates, enums, trimming, casing)
- Hands-on: Lab: Deduplicate an events dataset and produce a clean “latest state” table
- Module 5: SCD and CDC-Style Merges (Slowly Changing Dimensions)
- SCD Type 1 vs Type 2 patterns (overwrite vs history tracking)
- Building dimension tables with effective_start/effective_end concepts
- CDC merge concepts (insert/update handling)
- Conflict handling and late-arriving updates concepts
- Hands-on: Lab: Implement SCD logic for a customer dimension using SQL patterns
- Module 6: Incremental Loads (Watermarks, Upserts, Merge Patterns)
- Full refresh vs incremental and when to choose each
- Watermark strategies (updated_at, ingestion timestamp) and storing state concepts
- Upsert patterns (merge/insert-on-conflict) across common SQL engines
- Reprocessing windows for late data and backfills
- Hands-on: Lab: Build an incremental pipeline query using a watermark and validate multiple runs
- Module 7: Performance Optimization (Explain Plans, Indexes, Partitioning)
- Reading explain plans and identifying bottlenecks
- Join optimization patterns (filter early, reduce dataset size)
- Index concepts and when they help (OLTP vs analytics awareness)
- Partitioning concepts (date partitions, pruning, clustering awareness)
- Hands-on: Lab: Optimize a slow query using explain plan insights and improved filtering/join strategy
- Module 8: Data Quality Validation Using SQL Checks
- Uniqueness checks (primary key expectations and duplicates)
- Null validation and required fields
- Referential integrity checks between fact and dimension tables
- Range and anomaly checks (negative values, impossible timestamps)
- Hands-on: Lab: Build a SQL quality gate that fails the pipeline if checks do not pass
- Module 9: Analytics Modeling (Star Schema and Curated Layer Design)
- Fact and dimension modeling fundamentals
- Designing a star schema for a real dataset (orders, customers, payments)
- Building analytics-ready transformations (grain definition, surrogate keys concepts)
- KPI tables and reporting views (daily revenue, cohorts concepts)
- Hands-on: Workshop: Design a star schema and implement fact + dimension build queries
- Module 10: Reusable SQL Patterns for Production Pipelines
- Parameterization patterns (run_date, env separation concepts)
- Standard CTE layouts for staging/cleansing/curation steps
- Idempotent query design (safe re-runs, deterministic outputs)
- Documentation and testing approach for SQL transformations
- Hands-on: Capstone Workshop: Package a reusable SQL transformation set with checks and runbook notes
Hands-on Experience with Tools
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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
1
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
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Why Advanced SQL for Data Engineering for your business?
- Faster analytics delivery: Build complex transformations and reporting logic efficiently.
- Improved performance: Optimize queries to reduce compute cost and dashboard latency.
- Better data accuracy: Advanced joins, window functions, and aggregations reduce logic errors.
- Stronger troubleshooting skills: Engineers can debug pipelines and data issues quickly using SQL.
- More self-sufficient teams: Reduce dependency on specialized roles for day-to-day data tasks.
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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.