Apache Spark Certification — All Levels & Tracks
Master Apache Spark for batch processing, structured streaming, machine learning, and production deployments on YARN, Kubernetes, and cloud-managed Spark services. From core APIs to MLOps, this end-to-end program prepares you for real-world big data projects and vendor certifications.
Course Snapshot
- • Spark Core & APIs (Scala, Python, Java)
- • Spark SQL & DataFrame performance tuning
- • Structured Streaming & Kafka integration
- • MLlib, MLflow, model serving and MLOps
- • Cluster ops on YARN, Mesos, Kubernetes & Databricks
Why learn Apache Spark?
Spark is the de-facto engine for high-performance distributed data processing. It powers ETL, interactive analytics, streaming, and machine learning at scale. This course teaches both developer and operator perspectives so you can design efficient pipelines, tune performance, and deploy Spark applications in production.
Unified Engine
One engine for batch, streaming, SQL, and ML workflows.
Language Flexibility
APIs in Scala, Java, Python (PySpark), and R for wide adoption.
Ecosystem Integrations
Integrates with Kafka, Hadoop/HDFS, S3, Delta Lake, Hive, and ML tools.
Production Ready
Covers deployment, monitoring, tuning, and cost optimization for production clusters.
Tracks — Developer, Data Engineer & ML
Spark Developer Track
Master core Spark APIs, DataFrame/Dataset programming, Spark SQL, and performance-aware coding in Scala and Python.
- RDDs, DataFrames, Datasets
- Spark SQL, Catalyst optimizer
- Partitioning, skew handling, joins & shuffles
- Unit testing & CI for Spark apps
Data Engineer Track
Design robust ETL pipelines, Delta Lake transactions, batch & streaming integration, and data lakehouse patterns.
- Delta Lake & ACID transactions
- Structured Streaming & Kafka
- Data pipelines with Airflow & Spark
- Orchestration & data validation
Machine Learning & MLOps Track
Use MLlib, MLflow, feature stores, distributed training, and model serving with Spark-based workflows.
- MLlib algorithms & pipelines
- Distributed training & hyperparameter tuning
- MLflow, model registry, and serving
- Feature engineering at scale
Curriculum — Snapshot
Modular curriculum with video lessons, code notebooks (Scala/Python), and graded labs. Take single tracks or the Full Stack bundle for end-to-end expertise.
Core & Programming
- Spark architecture, jobs, stages & tasks
- RDD API, DataFrame & Dataset concepts
- Serialization, memory, and Tungsten engine
- Writing production-grade Spark applications
Performance & Tuning
- Spark SQL execution plans & Catalyst
- Shuffle management & join strategies
- Caching, broadcast joins, and serializers
- Monitoring with Spark UI, Ganglia, and metrics
Streaming, Storage & Ops
- Structured Streaming concepts & state management
- Integration: Kafka, Kinesis, S3, HDFS, Delta Lake
- Cluster deployment on YARN, Kubernetes, Databricks
- Production ops: autoscaling, cost optimization
Testing, CI & Best Practices
- Unit & integration testing with pytest, scalatest
- Packaging, deployment, and blue/green rollouts
- Data quality checks and schema evolution
Advanced ML & MLOps
- Feature stores & offline/online feature pipelines
- Model lineage, monitoring, and drift detection
- Serving strategies: batch, micro-batch, and real-time
Hands-on Labs & Capstone Projects
Real-world labs: local Docker/Kubernetes sandboxes, cloud variants (Databricks, EMR, GCP Dataproc), and graded capstones to demonstrate production readiness.
Large-Scale ETL Pipeline
Design batch pipeline to process terabytes of data, optimize joins and partitioning, and write to Delta Lake.
Structured Streaming with Kafka
Implement an end-to-end streaming pipeline with Kafka → Structured Streaming → sink (S3/Snowflake) with exactly-once guarantees.
Distributed ML Training
Train a distributed model using Spark MLlib / PySpark with hyperparameter tuning and register via MLflow.
Performance Benchmarking
Benchmark different file formats (Parquet/ORC), compression codecs, and caching strategies.
Cluster Ops Simulation
Deploy Spark on Kubernetes, simulate node failures, and validate job recovery and autoscaling.
MLOps Pipeline
Feature pipelines, model training, model registry, and serving with monitoring & alerts.
Pricing & Plans
Developer Track
Core Spark APIs, Spark SQL, DataFrame programming, and 10 hands-on labs.
Data Engineer Track
Delta Lake, Structured Streaming, Kafka integration, pipeline orchestration, and 12 labs.
ML & MLOps Track
MLlib, distributed training, MLflow, feature stores, and 15+ MLOps labs.
Instructor & Credibility
— Lead Spark Instructor
Big data architect and Spark committer (simulated role) with 18+ years building data platforms, real-time analytics, and ML systems at scale. Experience with Databricks, EMR, GCP Dataproc, and Kubernetes deployments.
Frequently Asked Questions
Do I need prior Hadoop knowledge?
No — Spark basics are taught from scratch. Familiarity with Python/Scala and distributed systems helps for advanced modules.
Which cloud environments are supported?
Labs include local Docker/Kubernetes setups and cloud examples on Databricks, AWS EMR, and GCP Dataproc. Instructions for managed services are provided.
Are industry certifications included?
We provide exam-style simulators and preparation materials. Official vendor certifications (Databricks, Cloudera) must be taken through the providers.
Career support?
Full Stack Bundle includes resume reviews, interview prep, and access to mentor office hours.
Get Started
Ready to master Apache Spark? Enroll now or contact us for group and enterprise training, or a tailored corporate workshop.