Apache Spark Training Course
This Apache Spark training is so much in demand because is gives you the chance to earn an eye catching perks in the industry. You will get an idea about Spark essentials like: a in-depth knowledge on SQL along with Streaming chek out the sequel. You will learn how to use the Spark technology to analyze the big data with lightening speed.
Self Paced Course
- Learn to dominate Big Data platform with Apache Spark training
- Learn how to make an efficient use of Spark technology to analyze Big Data at swift pace
- Develop understanding of SQL dataframes: RDDs & Tables
- Gain live industry experience with projct based training
- Get a chance to earn way high than your current salary
- Clear your doubts with expert professionals
Why to Opt for Apache Spark?
Learning on Apache Spark ecosystem would help you in grabing the best paying job opportunities around the globe in Big Data domain. According to one of the most reputed job portal indeed.com the average salary of an Spark professional is $108,366 per annum, which is quite alluring.
Using the Spark technology will help you to effectively analyze data in an interactive environment definitely at very swift pace. With 40 lectures consisting of 6.5 hours of content that are accessible 24X7. Through this training, you will learn thow to use the different libraries & features of Spark for various analytics. In short, this training gives you all that matches the requirements of any world-class organization.
Who should join
Apache Spark Training Course?
The course suites the IT professionals the best, who wants an increment in perks. The individuals wish to switch to Big Data domain with Spark skill are:
- Mainframe Professionals
- BI /ETL/DW Professionals
- Software Architects, Engineers and Developers
- Data Scientists and Analytics Professionals
- Senior IT Professionals
- Testing Professionals
- Big Data Enthusiasts
- Developers and Architects
- Novice graduates
There isn’t any mandatory prerequisite for this course, but intelligence on Java and SQL would surely add an advantage.
- You, This Course and Us
- Introduction to Spark (83.93)
- What does Donald Rumsfeld have to do with data analysis?
- Why is Spark so cool?
- An introduction to RDDs – Resilient Distributed Datasets
- Built-in libraries for Spark
- Installing Spark
- The Spark Shell
- See it in Action : Munging Airlines Data with Spark
- Transformations and Actions
- Resilient Distributed Datasets (71.05)
- RDD Characteristics: Partitions and Immutability
- RDD Characteristics: Lineage, RDDs know where they came from (6:06)
- What can you do with RDDs?
- Create your first RDD from a file
- Average distance travelled by a flight using map() and reduce() operations
- Get delayed flights using filter(), cache data using persist()
- Average flight delay in one-step using aggregate()
- Frequency histogram of delays using countByValue()
- Advanced RDDs: Pair Resilient Distributed Datasets (49.11)
- Special Transformations and Actions
- Average delay per airport, use reduceByKey(), mapValues() and join()
- Average delay per airport in one step using combineByKey()
- Get the top airports by delay using sortBy()
- Lookup airport descriptions using lookup(), collectAsMap(), broadcast()
- Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes (47.29)
- Get information from individual processing nodes using accumulators
- Long running programs using spark-submit
- Spark-Submit with Scala – A demo
- Behind the scenes: What happens when a Spark script runs?
- Running MapReduce operations
- PageRank: Ranking Search Results (38.32)
- What is PageRank?
- The PageRank algorithm
- Implement PageRank in Spark
- Join optimization in PageRank using Custom Partitioning
- Spark SQL (15.48)
- Dataframes: RDDs + Tables
- MLlib in Spark: Build a recommendations engine (43.41)
- Collaborative filtering algorithms
- Latent Factor Analysis with the Alternating Least Squares method
- Music recommendations using the Audioscrobbler dataset
- Implement code in Spark using MLlib
- Spark Streaming (26.91)
- Introduction to streaming
- Implement stream processing in Spark using Dstreams
- Stateful transformations using sliding windows
- Graph Libraries (14.30)
- The Marvel social network using Graphs