Name:Spark training for freshers & experienced from scratch
20,000/-(15,000)* condition (if you do daily tasks 5000 return)
Call: 9247159150 (please whatsapp me)
Training Time: July 1 to Aug 31- 50 days weekdays. Mon-Fri
Time 7 AM- 9.00 AM
To attend paidTraining please click on this link:
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Spark Training Course Content
- Object oriented language VS Functional language
- Difference between Scala and Java
- How JVM functioning
- Scala type hierarchy
- Scala Strings and Numbers
- List, Array, Tuples
- Create Functions and methods
- Case class, class, trait, objects.
- Important scala functions
- The power of Namenode
- HDFS & YARN fault tolarance.
- HDFS read & write data from local, HDFS, S3
- HDFS Daemons (namenode, datanode, Secondary namenode)
- Namenode High availability & Zookeeper
- Hands on Hdfs commands practice
- Install Hadoop & Spark in Ubuntu and windows.
- Configure Hadoop spark environment in Intellij
Hive & SQOOP Overview
- Hive Internals
- hive Optimization techniques.
- How Sqoop working?
- Orc, parquet, csv, json data processing.
- Importance of Bucketing & Partitioning in Hive.
- Import and export MySQL and Oracle data using Sqoop
- best practice in Hive & Sqoop
AWS with Spark
- Why AWS?
- Redshift, EMR and EC2 functionalities
- How to minimize AWS cost
- Security and IAM
- Submit a spark jar in AWS Cluster
- S3 bucket policies & IAM administration
- create databases services using RDS
- import & export data from Redshift
- Create EMR and EC2 cluster.
- The power of Spark?
- Important Spark APIs and Libraries
- Broadcast Variables and accumulators
- DAG Scheduler & Catalyst optimizer
- Spark important functions
- Create environment to implement spark applications.
- Run Programs in Command line Interface & Intellij
- Spark in Local, yarn mode
- Process Local, HDFS and S3 files
- What is RDD?
- Rdd operations (Transformations, Actions)
- Key-Value Pairs RDDs
- Narrow & wide transformations
- Ways to create rdds.
- apply transformations and actions.
- Optimize Rdds using DAG
- Debugging RDD Code
- Broad cast variables examples
- Debugging using Spark web UI
- Spark Job Execution in cluster
SparkSQL and DataFrames
- Different ways to create DataFrames
- Catalyst optimizer importance in Spark.
- Spark SQl Internal concepts.
- Process CSV, JSON, ORC, PARQUET, XML data using Spark.
- Process data using DSL commands & SQL Queries
- Cache, persist importance
- Hive data processing using Spark
- Get data from Cassandra, Hbase, and other resources
Spark DataSet API
- The power of Dataset API in Spark 2.x
- Serialization concept in DataSet
- Power of encoder
- Flink introduction
- process unstructured, structure data using dataset api
- Process CSV, JSON data.
- DataSet vs Dataframe Vs Rdd
- Get oracle, mysql data using dataset api
- 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
Advanced concepts in Spark
- Memory management in Spark
- How to optimize Spark Applications
- How Spark integrate with other Applications
- Spark with Cassandra Integration
- Process GBs of data using alluxio.
- Hbase, Phoenix and spark integration
- How Spark streaming working
- Micro batch processing Vs Streaming processing
- Flink Introduction
- Challenges in Streaming applications
- Kafka architecture
- Creating DStreams from Different Sources
- Viewing Streaming Jobs in the Web UI
- Sample Flink Streaming program.
- Kafka sample program
- Spark structure Structure streaming.
- Write Kafka Producer & Consumer apis programmatically
- Get Oracle data using kafka
Sample Spark Project
- End to end a project workflow.
- Developer, architect, and manager responsibilities.
- Resume preparation and bigdata interview tips.
- Performance optimization & best practice in real time projects.
- As a spark developer, daily activities.
- Data migration Spark sql project
- Kafka, Cassandra, Spark Streaming project
- Spark performance testing (test cases, debugging, unit tests)
- Spark, Hbase, Phoinex integration with kafka.
- Daily after training ill assign different tasks
- If you do all these tasks, you will get 5000/- money back.
- Daily Ill give support through whatsapp. It means if you struct anywhere to solve that problem, just whatsapp me screenshot, ill help you.
- Minimum 3 months online support & Job Assistance
- My Spark training always using latest spark versions such as spark 2.1.
- Consolidated spark and Scala materials.
- Help to get Cloudera or databricks spark certifications
- Our main aim after training
Recommendations:To learn Apache Spark, minimum knowledge on Scala and sql mandatory. Hadoop knowledge also recommended, but it’s covered in my Spark training.
Please note even i am giving online spark training, ill give better than offline training. The main reason always available in whatsapp, when you face any problem, ill resolve asap.
If you are interested please fill this form: