We are planning to start online spark training in Bangalore. If you are interested please fill the form.
Spark training evening batch
Fee: 20,000/- with 5000 money back (if u practice well)
Call: 9247159150 (please whatsapp me)
Weekend Spark Training for Non-Hadoop background Students.
Daily Training: Jan 8 to March 2 – 55 days . Mon-fri
Time 6.30 AM to 8.30 AM IST
Demo : Jan 6th 7 to 9 pm ist
To attend paid Training please click on this link: (whats app me for password)
Next weekend batch:
Jan 20 Evening 5PM – 9PM ist Sat & Sunday
Feb 2nd Morning 7.00 am to 11.00 am IST Saturday and Sundays
March 5 .. 6.30 am – 8.30 am ist, weekdays (Mon-Fri)
If you are looking for spark training, please fill this form:
Spark Training for Non-Hadoop background Students.
Note: In my training, every session recorded. I ll share that video after training for revision purpose.
Each and every Spark training session recorded,
Give daily tasks with realtime actions.
Recorded Spark Demo
Within this time, if you want to learn, just contact me ill send some materials, just learn those.
- How HDFS read/write the data
- YARN internal architecture
- HDFS Internal Architecture .
- HDFS Shell Commands
- Install Hadoop & Spark in Ubuntu
- Configure hadoop/spark environment in Eclipse
- How Hive functioning properly
- Optimize Hive queries
- Using Sqoop
- Process csv, json data
- Bucketing, Partitioning tables.
- Import MySQL/Oracle data using Sqoop
- Functional language
- Scala Vs Java
- Strings, Numbers
- List, Array, Map, Set
- Control Statements, collections
- Functions, methods
- Patren matching
- The power of Spark?
- Spark Ecosystem
- Spark Components vs Hadoop
- Installation & Eclipse configuration
- Programs in Command line Interface & Eclipse
- Process Local, HDFS files
- Purpose and Structure of RDDs
- Transformations, Actions, and DAG
- Key-Value Pair RDDs
- Creating RDDs from Data Files
- Reshaping Data to Add Structure
- Interactive Queries Using RDDs
SparkSQL and DataFrames
- Spark SQL and DataFrame Uses
- DataFrame / SQL APIs
- Catalyst Query Optimization
- Creating (CSV, JSON) DataFrames
- Querying with DataFrame API and SQL
- Caching and Re-using DataFrames
- Process Hive data in Spark
Spark DataSet API
- Power of Dataset API in Spark 2.0
- Serialization concept in DataSet
- Creating DataSet API
- Process CSV, JSON, XML, Text data
- DataSet Operation
Spark Job Execution
- Jobs, Stages, and Tasks
- Partitions and Shuffles
- Broadcast Variables and accumulators
- Job Performance
- Visualizing DAG Execution
- Observing Task Scheduling
- Understanding Performance
- Measuring Memory Usage
- shared variables usage
- Cluster Managers for Spark: Spark Standalone, YARN, and Mesos
- Understanding Spark on YARN
- What happened in cluster when you submit a job
- Tracking Jobs through the Cluster UI
- Understanding Deploy Modes
- Submit a sample job and monitor job
- Streaming Sources and Tasks
- DStream APIs and Stateful Streams
- Flink Introduction
- Kafka architecture
- Creating DStreams from Sources
- Operating on DStream Data
- Viewing Streaming Jobs in the Web UI
- Sample Flink Streaming program.
- Kafka sample program
AWS with Spark
- AWS architecture
- Redshift, EMR and EC2 functionalities
- How to minimize AWS cost
- Submit a sample jar in AWS Cluster
- Create a cluster using EMR
- Read/Write data from Redshift
Advanced concepts in Spark
- Memory management in Spark
- How to optimize Spark Applications
- Spark how to integrate with other Applications
- Spark with Cassandra Integration
- Alluxio/Tachyon hands on experience
Sample Spark Project
- End to end a project overview
- Complicated problems in a project
- Common steps in any project
- Implement Spark SQL Mini project
- Kafka, Cassandra, Spark Streaming project
- Pull Twitter data and analyse the data
- Daily after training assign a task
- Who completed all these tasks they will get 5000/- money back.
- After training provide solution to that problem.
- Minimum 3 months online support & Job Assistance
- Training in Spark 2.x and spark 1.6.2 in Scala language
- Excellent Materials all major spark and Scala books
- Guide to get Cloudera/MapR/Databricks spark certification
Recommendations: To learn Apache Spark, no need to learn Hadoop, but If you have hadoop knowledge, it’s huge plus to implement production level project.
To learn Spark Minimum core java (to learn Scala) and SQL queries knowledge mandatory.
This training intentionally done for non hadoop background students.