Data Engineering & Big Data Course in Pune

Why train with CACTS vs the typical market?

Live Project Internship Integration

Unlike institutes that assign mock projects or simple copy-paste tasks, CACTS bridges the learning gap by placing you on real company software development environments. You will coordinate with active developers, write production code, submit code reviews, and deploy test cases.

• Hands-on Git access and branch merge policies.
• Real-world debugging under senior developer code review loops.
• Staging pipeline deployments.

Related Career & Technical Resource Guides

Explore our developer-vetted glossary definitions, industry comparison analyses, and career roadmap guides for Data Engineering Training.

• What is Apache Spark? : An Introduction to Distributed Big Data Processing and In-Memory Analytics
• What is Apache Kafka? : An Introduction to Distributed Event Streaming and Publish-Subscribe Messaging
• What is Apache Hadoop? : An Introduction to Distributed Storage, MapReduce Computation, and Big Data Architecture
• Spark vs Hadoop : In-Memory Computation vs. Distributed Storage: Choosing the Big Data Core
• Data Engineering Project Ideas : Architecting Data Pipelines: Building Robust ETL and Big Data Warehouses
• What Does a Data Engineer Do? : Daily Responsibilities, Core Tech Stacks, and Career Pathways in Data Infrastructure
• Beginner to Data Engineer Roadmap : A Step-by-Step Learning Guide to Mastering Data Pipelines and Warehousing
• Best Data Engineering Certifications : Curated Credentials to Boost Your Resume and Prove Data Infrastructure Skills
• How Data Engineering is Used in E-Commerce : Building Scalable Data Pipelines to Manage Inventory, Transactions, and User Clickstreams

Hands-On Implementation Preview

Here is a concrete preview of the production-level code assets and system schemas you will design, write, and deploy during our 1-to-1 live project sessions.

PySpark ETL Job with Schema Parsing & Parquet Write

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, DoubleType

schema = StructType([
    StructField("user_id", StringType(), False),
    StructField("page", StringType(), True),
    StructField("latency", DoubleType(), True)
])

spark = SparkSession.builder.appName("ETLPipeline").getOrCreate()
df = spark.read.schema(schema).json("s3a://raw-logs/*.json")
df.write.partitionBy("page").parquet("s3a://data-lake/processed/")

Modern Cloud Data Lakehouse System Architecture

[Raw JSON Feeds] (S3 Landing Zone / Web Logs)
       |
       v
[Apache Spark] (PySpark ETL Transformations Worker)
       |
       v
[Data Lakehouse] (Optimized Parquet files partitioned by page)
       |
       v
[AWS Glue / Redshift] (Analytical Star Schema Warehouse)
       |
       v
[BI Dashboards] (Power BI / Business Reports)

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