performance Tuning course
Intensive 7-Week Program with Hands-On Spark Optimization Learning
Entire Course Designed and Taught by Sumit Mittal
Why Databricks Performance Tuning Matters More Than Ever
As data volumes grow, these performance problems become increasingly difficult to ignore.
- A pipeline that works perfectly on 5 GB of data can behave very differently when it starts processing 500 GB or even multiple terabytes.
- Jobs taking longer time to finish.
- Clusters becoming more expensive.
- Unexpected spill, skew, and shuffle bottlenecks start appearing.
- Adding more compute only increases the cost without addressing the root cause.
Common Databricks Performance Challenges Engineers Face
Some of the biggest challenges Engineers face in Performance Tuning are:
- Many engineers know what to do but don't always understand why it works.
- The underlying problem around; Increasing the cluster size, Changing Spark configuration and Repartitioning remains a mystery.
Real Engineers explore answers to these below real problems during this Databricks Performance Tuning Course at Trendytech.
- What is actually happening inside Spark and Databricks when a workload runs.
- Why does a shuffle become expensive?
- Why does spill happen?
- How does skew impact performance?
- What really happens during a join?
- When does AQE help, and when does it not?
- How do Delta Lake, caching, partitioning, and file layouts influence performance?
Practical Skills You’ll Build During These 7 Intensive Weeks:
- Learners work on large-scale datasets ranging from 50 GB to over 1 TB and analyze real performance bottlenecks that Data Engineers encounter in production environments.
- Beyond just knowing tuning techniques, you develop deep understanding required to diagnose performance problems, make better engineering decisions, and confidently optimize large-scale Databricks workloads.
- In-depth knowledge around fundamentals, makes understanding the optimizations much easier.
Modules Covered
- Spark Architecture
- Spark UI in Databricks
- Understanding, why some queries perform faster than others
- Understanding & tuning Data skew
- Avoid or reduce data shuffle
- Minimize or avoid data spill
- Join optimization
- Dynamic file pruning
- Caching - Disk Caching vs Spark Cache
- Partitioning
- Performance problems with Serialization
- Mitigating Serialization issues
- Best practices for User defined functions
- Small file problem
- Liquid clustering
- Photon acceleration
- Adaptive query execution
- Z ordering
- Table statistics
- Predictive optimization
- Estimating cluster size & right Instance type
- Course Duration – 2 months
- Course Validity – 18 months from batch starting date
A Curriculum that prepares you to thrive in the Industry as an Expert Data Engineer.
Build Practical Expertise with Databricks Performance Tuning Training
- Throughout the course, learners analyze large-scale workloads using datasets ranging from 50 GB to over 1 TB and explore real performance challenges involving reads, joins, shuffles, spill, skew, memory management, partitioning, Delta Lake optimization, caching, Adaptive Query Execution (AQE) and cost optimization.
- By looking at a workload, identify bottlenecks, understand Spark execution behavior, and make the right optimization decisions.
- Instead of just learning how to make a job run faster, understand why it was slow in the first place.
- Develop a mindset, to build scalable, efficient, and cost-effective data platforms.
Understand Compute, Resource Utilization, and Cost Optimization
- A common mistake engineers make is assuming more compute always improves performance.
- Performance bottlenecks often stem from inefficient reads, shuffling, spills, data skew, poor partitioning, and suboptimal query plans.
- Learn how compute resources impact performance and when scaling a cluster is the right solution.
Key areas covered include:
- Understanding CPU, memory, disk, and network bottlenecks
- Choosing the right compute for different workload patterns
- Estimating resource requirements for production workloads
- Identifying when a workload is compute-bound versus I/O-bound
- Understanding the relationship between performance and infrastructure costs
- Optimizing resource utilization without over provisioning
- Making informed decisions about scaling and workload optimization
Learn How to Diagnose and Resolve Performance Bottlenecks
- Understand root cause around bottleneck caused by expensive reads.
- Why workload is spending most of its time shuffling data across the network.
- Is spill impacting execution or, Is skew causing a few tasks to take significantly longer than others.
Throughout the program, learners investigate real-world performance bottlenecks and develop a practical understanding of:
- Query execution plans
- Join strategies and their trade-offs
- Shuffle behavior and optimization
- Spill and memory-related bottlenecks
- Data skew detection and mitigation
- Partitioning strategies
- Caching and storage optimization
- Adaptive Query Execution (AQE)
Learn Why Things Break at Scale
- Understand why a pipeline that runs perfectly on 5 GB of data started behaving very differently when it processes hundreds of gigabytes or multiple terabytes data.
- Know how joins become suddenly expensive.
- Why Shuffle started dominating execution time.
- How Spill begins appearing unexpectedly.
Learn to Think Like a Performance Engineer
- Many engineers immediately start changing configurations when they encounter a slow job.
- Sometimes they increase the cluster size and sometimes they increase partitions or add more compute.
- This program is designed to help learners develop the way of thinking around, where is the time being spent, is the bottleneck caused by reads, join, shuffle, memory pressure or skew?
Advanced Databricks Optimization Techniques for Real-World Workloads
Throughout the program, learners explore advanced optimization techniques that are commonly used in large-scale production environments, including:
- Adaptive Query Execution (AQE)
- Z-Ordering and Data Layout Optimization
- Predicate Pushdown, Partition Pruning
- Data Skipping Techniques
- Shuffle Optimization
- Disk Caching Strategies
- Query Execution Diagnostics
- Delta Lake Optimization
- Liquid Clustering
- Broadcast Join Optimization
- Data Skew Mitigation
- Spark UI Analysis
- Workload Benchmarking
- Cost-Aware Optimization Strategies
Who Should Join this Databricks Performance Tuning Course?
This Databricks Performance Tuning Course is designed for professionals who already work with Spark, Databricks, or large-scale data platforms and want to develop a deeper understanding of how performance optimization actually works.
The program is particularly valuable for:
- Data Engineers
- Databricks Engineers
- ETL Developers
- Cloud Data Architects
- Engineers preparing for advanced Data Engineering roles
- Engineers who want to master Spark performance beyond building pipelines.
- Spark Developers
- Analytics Engineers
- Big Data Professionals
- Platform Engineers
- Professionals responsible for optimizing Spark workloads
- Professional who wants to Learn optimization of Spark, Delta Lake, and infrastructure for maximum performance.
Course Fee
For Students accessing the course from India
Course Fee: ₹59000
Limited Period Discount: ₹10000
Offer Price: ₹49000*
*Valid for limited period
6 Months No Cost EMI available on all major Credit Cards.
For Students accessing the course from outside India
Course Fee: $730
Limited Period Discount: $160
Offer Price: $570*
*Valid for limited period
Why Choose TrendyTech as a Top Choice for Databricks Performance Tuning Training
Choosing the right training provider plays a major role in building practical engineering expertise. At TrendyTech, we focus on real-world learning, hands-on projects, and industry-relevant skills that help professionals master databricks performance tuning with confidence.
Expert-Led Learning
- Taught completely by Sumit Mittal himself, he brings practical engineering experience and teach optimization concepts based on real production scenarios.
Hands-On Practical Training
- Learners work on practical exercises and project-based use cases to understand real-world databricks performance optimization challenges.
Industry-Focused Curriculum
- The course is designed around enterprise data engineering requirements and modern databricks optimization techniques.
Flexible Learning Support
- We provide structured sessions, practical assignments, and learning support to help professionals succeed.
Career-Focused Skill Development
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Our course helps learners build expertise in:
1. Databricks Spark Performance Tuning
2. Query Optimization
3. Cluster Tuning
4. Delta Lake Optimization
5.Cost-efficient workload management
6. Advanced databricks performance optimization
Upskill Your Databricks Performance Tuning Skills with TrendyTech
As data workloads continue to grow in complexity, organizations need engineers who can build efficient, scalable, and cost-optimised Spark systems. Mastering Databricks Performance Tuning enables professionals to improve workload execution, reduce infrastructure costs, optimize cluster performance, and enhance business-critical data pipelines.
At TrendyTech, our Databricks Performance Tuning Course combines in-depth concepts with hands-on learning and real-world engineering scenarios. This course equips you with the practical knowledge and confidence needed to solve complex performance challenges.
FAQs
Databricks Performance Tuning is the process of identifying and resolving performance bottlenecks in Spark workloads.
For example, a slow-running job could be caused by expensive joins, excessive shuffling, data skew, spill, inefficient file layouts, or suboptimal query execution plans.
Understanding how to identify these bottlenecks and optimize them is what Performance Tuning is all about.
The objective is not simply to make a job faster, but to understand why it was slow in the first place.
Performance issues often don’t become visible until data volumes start growing.
A pipeline that performs well on a small dataset can behave very differently when it begins processing hundreds of gigabytes or multiple terabytes of data.
Jobs take longer to finish, infrastructure costs increase, and unexpected bottlenecks start appearing.
Databricks Performance Tuning helps engineers understand where time and resources are being spent, identify the root cause of performance issues, and build workloads that remain efficient as data volumes scale.
This program is ideal for professionals who have experience building data pipelines in Spark or Databricks and want to move beyond implementation into optimization.
Whether you’re a Data Engineer, Spark Developer, Analytics Engineer, ETL Professional, or Cloud Architect, this course helps you develop a deeper understanding of how Spark and Databricks work under the hood and how experienced engineers approach performance challenges in production environments.
This Databricks Performance Tuning Course goes far beyond basic Spark optimization techniques.
The program covers the internals of Spark execution, performance troubleshooting, workload optimization, and Databricks-specific optimization strategies that are commonly used in large-scale production environments.
Throughout the program, learners work with datasets ranging from 50 GB to over 1 TB to understand how Spark and Databricks behave under realistic workload conditions.
You can check the curriculum for more information.
Absolutely.
Performance Tuning cannot be learned through theory alone.
Throughout the program, learners work through a wide range of real performance tuning scenarios and observe how Spark and Databricks behave under different workload conditions.
Rather than building projects, the focus is on investigating workloads, identifying bottlenecks, understanding execution behavior, and applying the right optimization strategies.
This practical approach helps learners develop the confidence to diagnose and optimize Databricks workloads in production environments.
Many professionals join TrendyTech because they are looking for more than optimization tips and Spark configurations.
They want to understand how Spark and Databricks actually work.
This Databricks Performance Tuning Course is built around that philosophy.
Most Spark and Databricks courses focus on building pipelines, writing transformations, and learning platform features.
This program focuses exclusively on performance tuning.
Over 7 intensive weeks, learners develop a deeper understanding of Spark execution, workload behavior, bottleneck analysis, Databricks optimization techniques, and performance troubleshooting using datasets ranging from 50 GB to over 1 TB.
The goal is to help professionals move beyond implementation and develop expertise in optimization.
Many Data Engineers spend years building pipelines in Spark and Databricks but rarely get the opportunity to deeply understand why certain workloads perform well while others struggle at scale.
This course focuses on the optimization side of Data Engineering, helping professionals understand Spark internals, workload behavior, bottleneck analysis, Delta Lake optimization, AQE, skew handling, and cost optimization.
For experienced Data Engineers, this often becomes the missing piece that helps them move beyond implementation and develop expertise in performance engineering.
Many experienced Data Engineers already know how to build pipelines in Spark and Databricks.
What they are often looking for is a deeper understanding of how Spark actually works under the hood.
This program focuses on the topics that are rarely covered in depth, including memory management, shuffles, spill, skew, AQE, joins, Delta Lake optimization, caching, and workload troubleshooting.
The goal is to help Data Engineers understand not just how to build pipelines, but how to optimize them at scale.
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