Apache Spark Intermediate Training
Tune Spark jobs, process streams with Structured Streaming and manage lakehouse tables with Delta Lake or Iceberg. Learn Apache Spark through a clear Foundation to Advanced path, with hands-on labs, live cohort options, certification support and full LMS access.
Last updated
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Apache Spark levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
About Apache Spark Intermediate Training.
Understand where Apache Spark fits in modern software delivery, what this level covers, and how the program builds practical confidence step by step.
Program overview
Apache Spark is taught as a practical technical skill for modern engineering, operations and platform teams. The course explains where the tool fits, how it is used in real delivery environments, and how learners can apply it through guided practice.
This DevOpsSchool Apache Spark program is built for working professionals who want usable skills instead of only conceptual exposure. The training emphasizes live examples, workflow clarity, operational controls and progressive skill depth from Foundation to Intermediate.
Private Apache Spark training for delivery teams, platform groups and engineering organizations.
DevOpsSchool provides corporate Apache Spark training for teams that need faster adoption, workflow standardization, administrative control, integration guidance and governance-ready rollout. Private batches can be aligned to development practice, release workflow, access model, maturity level and internal tooling.
- Private online or classroom delivery for engineering teams
- Aligned to Apache Spark usage maturity, admin depth and collaboration patterns
- Useful for rollout, migration, governance and workflow consistency goals
- Can be tailored for developers, administrators, DevOps teams and leadership stakeholders
Why teams use DevOpsSchool
DevOpsSchool combines live demonstration, practical workflow discussion, implementation-oriented examples, and strong post-session learning support. That makes the training more usable for real teams than generic slide-led courses.
Choose any level. The page still shows the full progression.
Foundation, Essential, Intermediate and Advanced are presented as one linked training flow. Self-paced buyers get all levels by default, even if they start from only one course page.
Instructor-led, self-paced and corporate delivery options.
Compare delivery format, duration, level access and batch style. Fees are shown only in the top decision panel and once again after the agenda.
| Duration | Mode | Level access | Batch |
|---|---|---|---|
| 8 to 10 Hrs. max | Live & Interactive | Intermediate | Public batch |
| 8 to 10 Hrs. max | 1:1 Live training | Intermediate | Starts immediately |
| 1 year LMS access | Self-paced video | All 4 levels | Anytime access |
| 3 Days | Corporate | Apache Spark Intermediate Training | Private team batch |
What learners should be able to do after Apache Spark training.
The outcomes are written for practical delivery teams: developers, DevOps engineers, release engineers, administrators and technical leads.
Process data at scale
Write PySpark and Spark SQL jobs that transform large datasets reliably.
Tune and stream confidently
Diagnose slow jobs, tune shuffles and joins and build Structured Streaming pipelines.
Run Spark in production
Operate Spark on Kubernetes or managed platforms with orchestration, security and monitoring.
Why this skill, what you will achieve, and who should attend.
Use this section to decide whether the course matches your role, current experience and expected Apache Spark responsibilities before reviewing the detailed agenda.
Why Apache Spark matters
- Apache Spark helps teams standardize a critical part of their software delivery, infrastructure, automation or operations workflow.
- It gives learners a repeatable way to move from basic usage to confident execution in team and enterprise environments.
- For DevOps and platform teams, Apache Spark becomes more valuable when connected with automation, governance, monitoring and delivery practices.
- Teams using Apache Spark effectively reduce ad-hoc execution and move toward consistent engineering standards.
Objectives
- Understand Apache Spark concepts, architecture, terminology and operational usage patterns.
- Learn practical configuration, workflow design, access controls, team usage and quality practices.
- Apply Apache Spark in real delivery workflows using automation, integrations, traceability and governance patterns.
- Build practical confidence to use Apache Spark for individual, team and organizational use cases.
Pre-requisites
- Basic computer literacy and familiarity with software development workflows.
- Helpful but not mandatory: introductory DevOps, cloud, automation or software delivery awareness.
- No deep programming background is required for the Foundation and Essential levels.
Who this course is for
- Developers, administrators and engineers who need practical Apache Spark usage skills.
- DevOps engineers, release engineers, SREs and platform teams managing delivery standards.
- Engineering leads and technical managers responsible for workflow quality, access controls and governance.
- Corporate teams migrating to or standardizing on Apache Spark.
Small-batch live training starts every month.
Weekday batches start on the 1st and weekend batches on the first Saturday of every month. One-to-one (1:1) training can start immediately; corporate sessions are scheduled on demand.
Weekend live batch
Starts on the first Saturday of every month. Weekend sessions with demos, labs and Q&A; max 10 learners.
Weekday live batch
Starts on the 1st of every month. Weekday sessions for working professionals and distributed teams; max 10 learners.
Ask advisorAgenda depth up to the selected level.
This page shows the learning flow from Foundation through Intermediate. Each level adds to the previous one, and the agenda stops at the current page level. Day counts are for corporate (day-wise) delivery; live online batches cover the same agenda in max 8 to 10 hours.
Foundation Level: Spark architecture, setup and first jobsDay 1
- Big data processing basics: batch vs streaming, distributed storage and why Spark replaced classic MapReduce
- Spark architecture: driver, executors, cluster managers, jobs, stages and tasks
- Spark components: Spark Core, Spark SQL, Structured Streaming, MLlib and GraphX
- Installation: Java, Spark binaries, PySpark via pip, environment variables and spark-shell or pyspark
- SparkSession: creating a session, configuration options and the local[*] master
- RDD basics: parallelize, map, filter, reduceByKey and collect
- Lazy evaluation: transformations vs actions and how the DAG is built
- DataFrame basics: creating DataFrames, schemas, select, filter, withColumn and show
- Reading and writing data: CSV, JSON and Parquet with read and write options
- Spark SQL basics: temporary views and running SQL queries against DataFrames
- Spark UI: jobs, stages, storage and executors tabs for a first job
Essential Level = Foundation + DataFrames, Spark SQL and data engineeringDay 2 · 2 days cumulative
- Schemas and data types: StructType, explicit schemas vs inference and handling nulls
- Column expressions: built-in functions, when and otherwise, string, date and timestamp functions
- Aggregations: groupBy, agg, pivot and rollup and cube
- Joins: inner, outer, semi and anti joins and avoiding duplicate column problems
- Window functions: ranking, running totals and lag and lead over partitions
- User-defined functions: Python UDFs vs pandas UDFs vs built-in functions
- File formats: Parquet, ORC, Avro and compression codecs
- Partitioned writes: partitionBy, save modes, bucketing and small-file problems
- Catalog and tables: managed vs external tables and the Hive metastore
- Data quality checks: deduplication, validation rules and quarantine of bad records
- Notebooks and scripts: Jupyter, packaging jobs and spark-submit options
Intermediate Level = Essential + Performance tuning, streaming and lakehouse tablesDay 3 · 3 days cumulative
- Partitions and shuffles: repartition vs coalesce, shuffle partitions and data skew
- Adaptive Query Execution: coalescing partitions, skew join handling and dynamic join selection
- Join strategies: broadcast hash join, sort-merge join and join hints
- Caching and persistence: cache, persist storage levels and when caching hurts
- Reading query plans: explain modes, Catalyst optimiser and predicate pushdown
- Memory and executor sizing: executor cores, memory overhead and dynamic allocation
- Structured Streaming: sources, sinks, triggers, output modes and checkpoints
- Streaming with Kafka: reading topics, watermarks, windowed aggregations and exactly-once sinks
- Lakehouse tables: Delta Lake or Apache Iceberg, ACID writes, MERGE, time travel and schema evolution
- Spark on YARN and Kubernetes: deploy modes, spark-submit to a cluster and resource requests
- Troubleshooting: out-of-memory errors, stragglers, failed stages and reading executor logs
- Testing Spark code: unit tests with pytest and small local sessions
Choose live training or a full year of self-paced access.
If someone buys Foundation, Essential, Intermediate or Advanced as self-paced video, the price stays the same and access is granted to all four levels by default.
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Apache Spark levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
- Start immediately: no waiting for the monthly batch.
- One learner, one trainer: full attention on your questions.
- Sessions planned around your working hours.
- Your pace, with examples from your own projects and tools.
Method of training and assessment.
The course emphasizes practical demonstration, hands-on labs and assignment-based validation instead of passive lecture-only learning.
| SL | Method of training and assessment | Weightage |
|---|---|---|
| 1 | Understanding the problem space | 5% |
| 2 | Concept discussion | 10% |
| 3 | Live demo and walkthrough | 25% |
| 4 | Lab and hands-on exercises | 50% |
| 5 | Assignments and evaluation | 10% |
Labs, exercises and real workflow practice.
Every level moves from explanation to hands-on practice, review and troubleshooting.
First PySpark data job
Install PySpark locally, load a CSV dataset into a DataFrame, clean and aggregate it with DataFrame and SQL queries and write the result as Parquet.
Batch ETL pipeline with Spark SQL
Build a spark-submit job that joins orders and customers, applies window functions and data quality checks and writes partitioned Parquet tables.
Tune a job and stream from Kafka
Fix a skewed join using AQE and broadcast hints, then build a Structured Streaming job that reads Kafka events and upserts them into a Delta Lake table.
Apache Spark is taught as part of a working delivery ecosystem.
You learn where the tool sits in the wider engineering workflow, not in isolation.
Core foundations
Terminology, concepts, architecture and the operating model behind the tool.
Workflow traceability
Map practical work to tasks, approvals, documentation and delivery visibility.
Automation and integration
Use Apache Spark with validation checks, automation hooks and delivery workflows.
Governance controls
Operate with permissions, approvals, policy checks and production-safe workflows.
How your practical work is assessed.
Your certificate is backed by labs, assignments, capstone thinking and completion evidence.
Assignments
Level-specific assignments verify setup, configuration, workflow quality and operating discipline.
Hands-on validation
Learners are expected to complete practical usage, automation and troubleshooting exercises.
Completion record
Certificate issuance is tied to demonstrated participation, practical completion and course-level coverage.
Led by Rajesh Kumar and the DevOpsSchool practitioner network.
Training is grounded in practical DevOps, SRE, security, release and platform engineering experience with real enterprise delivery context.
Earn a DevOpsSchool Apache Spark certificate.
Completion includes a digital certificate with credential details, practical assignments and a learning record aligned to the selected Apache Spark level.
Certificate verificationCertificate of Completion
Apache Spark Intermediate Training
Unique credential ID, verifiable online
What learners say about training with Rajesh Kumar.
“Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.”
“Very well organized training, helped a lot to understand the AWS concept and detailed related to various tools. Very helpful”
“The training was very useful and interactive. Rajesh helped develop the confidence of all.”
Rated 4.6 on Google · All testimonials · Video reviews on YouTube
Quoted as published, from learners in Rajesh Kumar's live trainings across DevOpsSchool courses.
Used by developers, DevOps engineers, release leads and engineering managers.
Private batches are available for teams that want the same agenda aligned to their own tools and workflows.
Why this Apache Spark path is different.
The value is not only one course. The subscription unlocks the full learning ecosystem for one year.
| Feature | DevOpsSchool Apache Spark Training | Typical short course |
|---|---|---|
| Levels | Foundation, Essential, Intermediate, Advanced | Single generic syllabus |
| Self-paced access | All four levels plus 20+ courses and 50+ tools | Usually one course only |
| Live training | Demos, labs, Q&A and assignment review | Often lecture-heavy |
| Certificate | DevOpsSchool completion certificate | Varies by provider |
Questions learners ask before joining.
Does Foundation cost less than Advanced?
No. Self-paced pricing is intentionally the same because access includes all four levels by default.
What is included in the yearly subscription?
One year of complete LMS access, all four levels for the selected course, plus 20+ additional courses and 50+ tools access.
Can corporate teams request customization?
Yes. The live format can be aligned to team tools, governance requirements, workflows and internal delivery standards.
What is the recommended sequence?
Foundation → Essential → Intermediate → Advanced. The agenda is designed so each level builds on the previous one.
Talk to DevOpsSchool before choosing the batch.
Use this support section for batch questions, corporate planning, LMS access clarifications, or guidance on which Apache Spark level fits your team.
India / Worldwide: +91 99057 40781
USA: +1 (469) 756-6329
Email: contact@DevOpsSchool.com
Chat on WhatsAppExtend the course with articles, explainers and workflow guidance.
Continue learning with supporting articles that connect Apache Spark practices to DevOps, automation, governance and delivery workflows.
Training snapshots and certificate preview.
A look at the completion certificate and the course.
Certificate preview
See what course completion evidence looks like.
Tool-focused training
Course visuals reinforce the Apache Spark workflow and subject area.
Live batch experience
Training supports live online, private team and workshop-style delivery.
Start Apache Spark Intermediate training.
Next weekday batch starts on the 1st; weekend batch on the first Saturday. Batches are capped at 10 learners. Send your details and DevOpsSchool will share the syllabus and batch options.