Apache Spark Professional (Include 2.x
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4. No PPT's at all, advanced way of teaching
5. In depth explaination of each topic
6.Contains Hands on session
7. Any New module will be without additional charge(In this
training list only)
8. No travel and Hassel
9. Learn as per your comfort
10. Practice at Home using Virtual Machine
11. Practice using Latest Spark 2.x version (Module
33 New)
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Below is the best combo for certification and training
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Check free
demo below (Best Quality is Promise, watch it right now)
- Now Includes Apache Spark (Beginner
to Advanced Spark Training)
- Learn Complete Spark(Basic to
Advanced) Self-Paced Training
- Complete training on
Cloudera QuickStart Virtual Machine (Includes
Pre-installed all the required software ).
- Hence,
you
can practice
on
your Windows Machine Locally
- Its Better than many classroom
Hadoop Trainings (Trainer has 12+ years of real Industry
experience) Read Testimonials
- To the point session, not lengthy
session
- After this training your Apache Spark knowledge will reach
to expert Spark Professional
- Even you will use Hadoop 2.0 and HDFS, to learn Spark
- Running offer
for next 7 days actual cost 3900INR/$89 after discount
it will cost you 3500INR/$75
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Most
popular
certification
Sample
Demo
Session from Actual Training
Syllabus
and Completed Spark Training is Below
Module 1: Introduction to Apache Spark (Available
Length 48
Minutes)
- Introduction to Apache Spark
- Features of Apache Spark
- Apache Spark Stack
- Introduction to RDD's
- RDD's Transformation
- What is good and bad In MapReduce?
- Why to use Apache Spark
Module 2: Cloudera QuickStart VM
Installation (Hands-on Lab + PDF Download)
(Available Length
34 Minutes)
- Include Hadoop
- Include Apache Spark
- Include Hive
- Include Sqoop
- Include Hue
Module 3: Deep Dive in HDFS: (Available
Length 48
Minutes)
- HDFS Design
- Fundamental of HDFS (Blocks, NameNode,
DataNode, Secondary Name Node)
- Rack Awareness
- Read/Write from HDFS
- HDFS Federation and
High Availability (Hadoop 2.x.x)
- HDFS Command Line Interface
Module 4: Spark Shell Hands On Using
HDFS (Hands-on Lab + PDF Download)
(Available Length
34 Minutes)
- Spark Shell Introduction
- Create file using Hue
- Spark Shell extracting file from HDFS
- Create RDD from HDFS file
Module 5: Programming with RDD Part-1 (Hands-on Lab + PDF Download) (Available
Length 28
Minutes)
- Creating new RDD
- Transformations on RDD
- Lineage Graph
- Actions on RDD
- RDD Concepts on Persist and Cache
- Lazy evaluation of RDD
Module 6: Scala/Spark Functional
Programming (Hands-on Lab+ PDF Download)
(Available Length
28 Minutes)
- Using Function Literals
- Anonymous Functions
- Define a function which accepts another
function
Module 7: RDD Transformation
Programming in Depth (Hands-on Lab+ PDF
Download) (Available
Length 24
Minutes)
- Hands on and core concepts of map()
transformation
- Hands on and core concepts of filter()
transformation
- Hands on and core concepts of flatMap()
transformation
- Compare map and flatMap transformation
Module 8: Apache Spark in Action Depth
(Hands-on Lab+ PDF Download) (Available Length
36 Minutes)
- Hands on and core concepts of reduce()
action
- Hands on and core concepts of fold()
action
- Hands on and core concepts of
aggregate() action
- Basics of Accumulator
- Hands on and core concepts of collect()
action
- Hands on and core concepts of take()
action
- Ordered access of RDD
Module 9: Apache Spark Execution Model
(Includes PDF Download
Available Length
35 Minutes)
- How Spark execute program
- Concepts of RDD partitioning
- RDD data shuffling and performance
issue
Module 10: Apache Spark PairRDD (Include PDF Download Available Length
45 Minutes)
- Core concepts of PairRDD
- Creation of PairRDD
- Aggregation in PairRDD
- Aggregation functions understanding in
depth
a) How reduceByKey()
work conceptually?
b) How foldByKey() work
conceptually?
c) How
combineByKey()work conceptually?
Module 11: Spark PairRDD HandsOn Lab (Hands-on Lab+ PDF Download) (Available Length
12 Minutes)
- reduceByKey
- foldByKey
- combineByKey
- groupByKey
Module 12 : Spark PairRDD Joining, Zipping and Grouping (Hands-on Lab+ PDF Download) (Available
Length 30
Minutes)
- reduceByKey
versus groupByKey performance issue
- cogroup
- zip
- joining
(left, right, inner etc.)
Module 13-A:
Understanding Hadoop SequenceFile (Available
Length 7
Minutes)
Module 13-B: Creating Seqnce File and
Processing using SPark (Hands on Lab)-Part-1 (Hands-on Lab+ PDF Download) (Available
Length 23
Minutes)
- Creating SequenceFile using TSV file
- Loading Data in Apache Hive
- Processing SequnceFile as an RDD.
Module 14 : Spark Shared Variables ( PDF Download) (Available
Length 27
Minutes)
- Shared Variables: Broadcast Variables (Available Length
14 Minutes)
- Shared Variables: Accumulators (Available
Length 13
Minutes)
Module 15 : Spark Accumulator (Hands-on Lab+ PDF Download) (Available
Length 14
Minutes)
- Word count and Character Count
- Counting Bad records in a file
Module 16 : Spark BroadCast Variable (Hands-on Lab+ PDF Download) (Available
Length 12
Minutes)
- Joining two csv files one as a
Broadcasted Lookup table
Module 17 : Spark API : BroadCast Variable, Filter Functions
and Saving File to HDFS (Hands-on Lab+ PDF Download) (Available
Length 13
Minutes)
Module 18 : Spark API : Spark Join,
GroupBy and Swap function (Hands-on Lab+ PDF Download) (Available
Length 12
Minutes)
Module 19 : Spark API : Remove Header from CSV file and Map
Each column to Row Data (Hands-on Lab+ PDF Download) (Available
Length 10
Minutes)
Module 20 : Spark SQL ( PDF Download) (Available
Length 27
Minutes)
- HiveContext
- Schema RDD replaced by DataFrame API
- History of SparkSQL
- Catalyst Optimizer
Module 21 : SparkSQL HandsOn Sessions (Hands-on Lab+ PDF Download) (Available
Length 20
Minutes)
- Hive Configuration
- Create Hive table using Spark
- Load Data in HIve table using Spark
- Create another table using DataFrame
Module 22 : Implementing Business Logic using SparkSQL (Hands-on Lab+ PDF Download) (Available
Length 25
Minutes)
- Loading CSV file
- Spark Case classes (To create schema
for csv file)
- Convert RDD to DataFrame using
DataFrmae API for query data
- Using SQL query on DataFrame
Module 23 : Spark Streaming in Depth
Part-1 (PDF Download) (Available Length
26 Minutes)
- Real/Near real time data processin
- Streaming Sources and Sinks
- DStream (Discretized Stream)
- Dtream Concepts
- Stock Visualization Example (How Streaming Helpful)
Module 24 : Spark Streaming in Depth Part-2 (PDF Download ) (Available
Length : 22 Minutes)
- Execution of Spark Streming
- Spark Streaming Transformation
(Stateless and Stateful)
- Comining multiple DStream
- Understanding transform() operator
Module 25 : SPARK STREAMING PART-3
STATEFULL (WINDOW) TRANSFORMATIONS (Available
20 Minutes)
- Window Transformation
- Window Duration and Sliding Duration
- DStream Opeations
- WordCount in DStream
Module 26 : Basics of Machine Learning
and Data Science (Available
Length : 30 Minutes)
- Basics of ML and Data Science
- Example of Machine Learning
- Supervised and Unsupervised Learning
- Key terminology e.g. features, training and testing
- How to choose right algorithm
- Common steps of Machine Learning
- Collect data
- Prepare Input data
- Analyze Input data
- Train the algorithm
- Test the algorithm
- Use the Algorithm
Module 27 : SPARK STREAMING: REAL TIME STOCK MARKET DATA
PROCESSING (HANDS-ON LAB + PDF
Download Available
Length : 21 Minutes)
- Problem Statement
- Data Format
- Writing Stream script to filter Bigger Volume data
- Write results back to HDFS file System
Module 28 : SPARK STREAMING: REAL TIME
STOCK MARKET DATA MAVEN APPLICATION ( Hands-on
Lab+ PDF Download) (Available
Length 37 Minutes)
- Understanding Maven pom.xml
- Importing Scala Application in eclipse
- Creating Application JAR file using eclipse and Maven
- Run Spark Streaming Application
- Process data using Spark Stream Application
Module 29 : SPARK STREAMING & SPARK SQL: REAL TIME
MARKET DATA APPLICATION (Hands-on Lab
) (Available Length
18 Minutes)
- Create Spark Streaming Application
- Use SparkSQL in Spark Streaming
Application
- Querying data
Module 30 : SPARK STREAMING WINDOW
FUNCTION& SPARK SQL JOIN: REAL TIME MARKET DATA APPLICATION (Hands-on Lab) (Available
Length 7 Minutes)
- Create Spark
Streaming Application
- Use SparkSQL in Spark Streaming
Application
- Joining data sets , with real-time
streaming data
- Using Spark Streaming window function
to calculate , running rum of trade volume.
Module 31 : SPARK ADVANCED : DATA PARTITIONING ( PDF Download) (Available
Length 26 Minutes)
- What is Partitioning and why?
- Data Partitioning example using Join (Hash Partitioning)
- Understand Partitioning using Example for get Recommendations for
Customer
- Understand Partitioning code using Spark-Scala
- Operations which create Partitioned RDD
- Operation which get benefit of Partitioning
- Operation that affect the partitioning
Module 32 : SPARK PAIR RDD FUNCTIONS : In Depth (PDF
Download)
- reduceByKey() (Available
Length 17 Minutes)
- groupByKey() (Available
Length 14 Minutes)
- combineByKey() (Available
Length 13 Minutes)
- foldByKey() (Available
Length 15 Minutes)
- aggregateByKey() (Available
Length 11 Minutes)
- Comparision Between Function (Available
Length 11 Minutes)
Module-33 : Create Spark 2.x
Environment using Ubuntu Linux on Windows
Module-33A :
Step by Step Installation of VMWARE Workstation Player ( PDF Download) (Available Length
8 Minutes)
Module-33B : Install Ubuntu Linux in VMWare Player ( PDF Download) (Available Length 23 Minutes)
- Install
Ubuntu
Image
- Install
SSH
server
- Install
Putty
and connect to Linux OS
Module-33C : Install Apache Spark 2.x ( PDF Download) (Available
Length 17 Minutes)
- Install
Apache
Spark
- Start
spark-shell
- Start
pyspark
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Books on Spark or PDF to read :
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