Apache Spark Training Course Content

•   1.1 SCALA (Object Oriented and Functional Programming)
•   1.2 Scala Environment Set up.
•   1.3 Functional Programming.
•   1.4 Collections (Very Important for Spark)
•   1.5 Object Oriented Programming.
•   1.6 Integrations
•   1.7 SPARK CORE.
•   1.8 Persistence.
•   1.9 CASSANDRA (N0SQL DATABASE)
•   1.10 SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2
•   1.11 SPARK STREAMING
•   1.12 SPARK SQL
•   1.13 SPARK MLIB.

SCALA (Object Oriented and Functional Programming)

•   Getting started With Scala.
•   Scala Background, Scala Vs Java and Basics.
•   Interactive Scala – REPL, data types, variables,expressions, simple functions.
•   Running the program with Scala Compiler.
•   Explore the type lattice and use type inference
•   Define Methodsand Pattern Matching.

Scala Environment Set up.

•   Scala set up on Windows.
•   Scala set up on UNIX.

Functional Programming.

•   What is Functional Programming.
•   Differences between OOPS and FPP.

Collections (Very Important for Spark)

•   Iterating, mapping, filtering and counting
•   Regular expressions and matching with them.
•   Maps, Sets, group By, Options, flatten, flat Map
•   Word count, IO operations,file access, flatMap

Object Oriented Programming.

•   Classes and Properties.
•   Objects, Packaging and Imports.
•   Traits.
•   Objects, classes, inheritance, Lists with multiple related types, apply

Integrations

•   What is SBT?
•   Integration of Scala in Eclipse IDE.
•   Integration of SBT with Eclipse.

SPARK CORE.

•   Batch versus real-time data processing
•   Introduction to Spark, Spark versus Hadoop
•   Architecture of Spark.
•   Coding Spark jobs in Scala
•   Exploring the Spark shell -> Creating Spark Context.
•   RDD Programming
•   Operations on RDD.
•   Transformations
•   Actions
•   Loading Data and Saving Data.
•   Key Value Pair RDD.
•   Broad cast variables.

Persistence.

•   Configuring and running the Spark cluster.
•   Exploring to Multi Node Spark Cluster.
•   Cluster management
•   Submitting Spark jobs and running in the cluster mode.
•   Developing Spark applications in Eclipse
•   Tuning and Debugging Spark.

CASSANDRA (N0SQL DATABASE)

•   Learning Cassandra
•   Getting started with architecture
•   Installing Cassandra.
•   Communicating with Cassandra.
•   Creating a database.
•   Create a table
•   Inserting Data
•   Modelling Data.
•   Creating an Application with Web.
•   Updating and Deleting Data.

SPARK INTEGRATION WITH NO SQL (CASSANDRA) and AMAZON EC2

•   Introduction to Spark and Cassandra Connectors.
•   Spark With Cassandra -> Set up.
•   Creating Spark Context to connect the Cassandra.
•   Creating Spark RDD on the Cassandra Data base.
•   Performing Transformation and Actions on the Cassandra RDD.
•   Running Spark Application in Eclipse to access the data in the Cassandra.
•   Introduction to Amazon Web Services.
•   Building 4 Node Spark Multi Node Cluster in Amazon Web Services.
•   Deploying in Production with Mesos and YARN.

SPARK STREAMING

•   Introduction of Spark Streaming.
•   Architecture of Spark Streaming
•   Processing Distributed Log Files in Real Time
•   Discretized streams RDD.
•   Applying Transformations and Actions on Streaming Data
•   Integration with Flume and Kafka.
•   Integration with Cassandra
•   Monitoring streaming jobs.

SPARK SQL

•   Introduction to Apache Spark SQL
•   The SQL context
•   Importing and saving data
•   Processing the Text files,JSON and Parquet Files
•   DataFrames
•   user-defined functions
•   Using Hive
•   Local Hive Metastore server

SPARK MLIB.

•   Introduction to Machine Learning
  
•   Types of Machine Learning.
•   Introduction to Apache Spark MLLib Algorithms.
•   Machine Learning Data Types and working with MLLib.
•   Regression and Classification Algorithms.
•   Decision Trees in depth.
•   Classification with SVM, Naive Bayes
•   Clustering with K-Means
•   Building the Spark server

Course advisor

Named by Onalytica as one of the three most influential people in Big Data, Also an author for a number of leading Big Data and Apache Spark websites, including Datafloq, Apache Spark Central, and The Guardian. She also regularly speaks at renowned events.

What is a Apache Spark?

Apache Spark™ is a fast and general engine for large-scale data processing.
Speed Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Ease of Use Write applications quickly in Java, Scala, Python, R.
Generality Combine SQL, streaming, and complex analytics.
Runs Everywhere Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.

Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, or on Apache Mesos.

Typical job duties for Apache Spark developer

•   Install, configure and maintain enterprise hadoop environment.
•   Loading data from different datasets and deciding on which file format is efficient for a task. Hadoop developers source large volumes of data from diverse data platforms into Hadoop platform.
•   Understanding the requirements of input to output transformations.
•   Hadoop developers spend lot of time in cleaning data as per business requirements using streaming API’s or user defined functions.
•   Defining Hadoop Job Flows.
•   Build distributed, reliable and scalable data pipelines to ingest and process data in real-time. Hadoop developer deals with fetching impression streams, transaction behaviours, clickstream data and other unstructured data.
•   Managing Hadoop jobs using scheduler.
•   Reviewing and managing hadoop log files.
•   Design and implement column family schemas of Hive and HBase within HDFS.
•   Assign schemas and create Hive tables.
•   Managing and deploying HBase clusters.
•   Develop efficient pig and hive scripts with joins on datasets using various techniques.
•   Assess the quality of datasets for a hadoop data lake.
•   Apply different HDFS formats and structure like Parquet, Avro, etc. to speed up analytics.
•   Build new hadoop clusters
•   Maintain the privacy and security of hadoop clusters.
•   Fine tune hadoop applications for high performance and throughput.
•   Troubleshoot and debug any hadoop ecosystem run time issues.

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