IICT provides 100% real-time, practical and placement focused Apache Spark training in kodambakkam. Our Apache Spark course concentrates from basic level training to advanced level training. Our Apache Spark training in completely focused to get placement in MNC in kodambakkam and certification on Apache Spark after completion of our course. Our team of Apache Spark trainers are Apache Spark certified professionals with more real-time experience in live projects. Our Apache Spark Course syllabus is enough for anyone who wants to get Apache Spark certification which meets industry expectations. In our course plan, you will learn History of Big Data & Apache Spark, Introduction to the Spark Shell and the training environment, Intro to Spark DataFrames and Spark SQL, Data Sources: reading from Parquet, S3, Cassandra, HDFS, and your local file system,Spark's Architecture, Programming with Accumulators and Broadcast variables, Debugging and tuning Spark jobs using Spark's admin UIs, Memory & Persistence, Advanced programming with RDDs, Visualization: matplotlib, gg_plot, dashboards, exploration and visualization in notebooks, Introduction to Spark Streaming, Introduction to MLlib and GraphX with lots of live practical examples.
Our Apache Spark training center is equipped with lab facilities and excellent infrastructure. We also provide Apache Spark certification training path for our students in kodambakkam. Through our associated Apache Spark training center, we have trained more than 1000+ Apache Spark students and provided 90 percent placement. Our Apache Spark Training course fee is value for money and tailor-made course fee based on the each student's training requirements. Apache Spark training in kodambakkam conducted on day time classes, weekend training classes, evening batch classes and fast track training classes.
Introduction to the Spark Shell and the training environment
Intro to Spark DataFrames and Spark SQL
Introduction to RDDs
Data Sources: reading from Parquet, S3, Cassandra, HDFS, and your local file system
Spark's Architecture
Programming with Accumulators and Broadcast variables
Debugging and tuning Spark jobs using Spark's admin UIs
Memory & Persistence
Advanced programming with RDDs (understanding the shuffle phase, partitioning, etc.)
Visualization: matplotlib, gg_plot, dashboards, exploration and visualization in notebooks
Introduction to Spark Streaming
Introduction to MLlib and GraphX