Apache Spark Training in Porur Chennai
Greens Technologys located in Porur provides Apache Spark training in Chennai to provide knowledge and skills to become a successful Spark Developer and prepare you for the Cloudera Certified Associate Spark Hadoop Developer Certification Exam CCA175. You will get in-depth knowledge of concepts such as HDFS, Flume, Sqoop, RDDs, Spark Streaming, MLlib, SparkSQL, Kafka cluster & API by taking this Apache Spark Course in Chennai.
The Apache Spark Training course in Chennai enables you to master the essential skills in Apache Spark & Scala such as Real-time processing, Spark SQL, Spark streaming, Machine learning programming, GraphX programming, and Shell scripting spark.
About Our Trainer
- Karthik is an experienced statistician and data miner with more than 10+ years of experience using R, Python and SAS and a passion for building analytical solutions. He is a M.S. in Quantitative Economics and Applied Mathematics graduate who has analytics experience working with companies like Capital One, Walmart, ICICI Lombard etc.
Karthik is a lead Data Scientist at Citi Bank. As a Certified Predictive Modeler, Statistical Business Analyst, and Certified Advanced Programmer, Karthik is passionate about sharing his knowledge on how data science can support data-driven business decisions.
Qualification: M.S. in Statistics
Membership American Statistical Association
Want a free career Advice or any career related queries? Reach him by
+91- 8939915572
Curriculum
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
Apache Spark Training Course description
With Greens Technology’s Apache Spark and Scala certification training in Chennai you would advance your expertise in Big Data Hadoop Ecosystem. With this Apache Spark certification you will master the essential skills such as Spark Streaming, Spark SQL, Machine Learning Programming, GraphX Programming, Shell Scripting Spark. And with real life industry project coupled with 30 demos you would be ready to take up Hadoop developer job requiring Apache Spark expertise.
Apache Spark Training Objectives
- Understand what is Apache Spark and Scala programming
- Understand the difference between Apache Spark and Hadoop
- Learn Scala and its programming implementation
- Implement Spark on a cluster
- Write Spark Applications using Python, Java and Scala
- Understand RDD and its operation along with implementation of Spark Algorithms
- Define and explain Spark Streaming
- Learn about the Scala classes concept and execute pattern matching
- Learn Scala Java Interoperability and other Scala operations
- Work on Projects using Scala to run on Spark applications
Who should take this Spark and Scala Certification course?
- Software Engineers looking to upgrade Big Data skills
- Data Engineers and ETL Developers
- Data Scientists and Analytics Professionals
- Graduates looking to make a career in Big Data
What are the Prerequisites for this course?
There are no prerequisites for taking up this course. Basic knowledge of database, SQL and query language can help.
Why take Apache Spark and Scala training course?
- Apache Spark is an open source computing framework up to 100 times faster than Mapreduce
- Spark is alternative form of data processing unique in batch processing and streaming
- This is a comprehensive course for advanced implementation of Scala
- Prepare yourself for cloudera Hadoop Developer and Spark Professional Certification
- Get professional credibility to your resume so you get hired faster with high salary
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.
