| 1. Accumulators.mp4 | 6.7 MB | ||
| 1. Course Overview.mp4 | 11.8 MB | ||
| 1. Introduction to Pair RDD.mp4 | 2.7 MB | ||
| 1. Introduction to Running Spark in a Cluster.mp4 | 6.3 MB | ||
| 1. Introduction to Spark SQL.mp4 | 6 MB | ||
| 1. RDD Basics.mp4 | 3.8 MB | ||
| 1. Spark Architecture.mp4 | 4.5 MB | ||
| 1.1 Apache-Spark-Python-Slides.pdf.pdf | 5.5 MB | ||
| 10. Actions.mp4 | 12.9 MB | ||
| 10. Join Operations.mp4 | 8.1 MB | ||
| 11. Extra Learning Material How are Big Companies using Apache Spark.html | 614.4 B | ||
| 11. Solution to Sum of Numbers Problem.mp4 | 3.6 MB | ||
| 12. Important Aspects about RDD.mp4 | 2.5 MB | ||
| 13. Summary of RDD Operations.mp4 | 3.9 MB | ||
| 14. Caching and Persistance.mp4 | 8.4 MB | ||
| 2. Create Pair RDDs.mp4 | 6.2 MB | ||
| 2. Create RDDs.mp4 | 3.8 MB | ||
| 2. Introduction to Spark.mp4 | 3.6 MB | ||
| 2. Solution to StackOverflow Survey Follow-up Problem.mp4 | 2.3 MB | ||
| 2. Spark Components.mp4 | 8.1 MB | ||
| 2. Spark SQL in Action.mp4 | 24 MB | ||
| 2. Spark-submit.mp4 | 4.5 MB | ||
| 3. Broadcast Variables.mp4 | 12.8 MB | ||
| 3. Filter and MapValue Transformations on Pair RDD.mp4 | 12.6 MB | ||
| 3. Install Java and Git.mp4 | 15.8 MB | ||
| 3. Run Spark Application on Amazon EMR (ElasticMapReduce) cluster.mp4 | 28.8 MB | ||
| 3. Spark Data Sources.html | 614.4 B | ||
| 3. Spark SQL practice House Price Problem.mp4 | 3.6 MB | ||
| 4. Extra Learning Material Avoid These Mistakes While Writing Apache Spark Program.html | 409.6 B | ||
| 4. Git URL.html | 204.8 B | ||
| 4. Map and Filter Transformation.mp4 | 16.5 MB | ||
| 4. Reduce By Key Aggregation.mp4 | 10.5 MB | ||
| 4. Spark SQL Joins.mp4 | 12.9 MB | ||
| 5. Dataframe or RDD.mp4 | 4.3 MB | ||
| 5. Set up Spark.mp4 | 27 MB | ||
| 5. Solution for the Average House Problem.mp4 | 5.5 MB | ||
| 5. Solution to Airports by Latitude Problem.mp4 | 3.4 MB | ||
| 6. Dataframe and RDD Conversion.mp4 | 6.5 MB | ||
| 6. FlatMap Transformation.mp4 | 5.8 MB | ||
| 6. Group By Key Transformation.mp4 | 9.2 MB | ||
| 6. Winutils URL.html | 409.6 B | ||
| 7. Performance Tuning of Spark SQL.mp4 | 4.7 MB | ||
| 7. Run our first Spark job.mp4 | 8.8 MB | ||
| 7. Set Operations.mp4 | 15.1 MB | ||
| 7. Sort By Key Transformation.mp4 | 5.6 MB | ||
| 8. Sampling with Replacement and Sampling without Replacement.html | 409.6 B | ||
| 8. Solution for the Sorted Word Count Problem.mp4 | 5.7 MB | ||
| 9. Data Partitioning.mp4 | 6.4 MB | ||
| 9. Solution for the Same Hosts Problem.mp4 | 3.3 MB | ||
| Read Me.txt | 102.4 B | ||
| Torrent_downloaded_from_Demonoid_-_www.demonoid.pw_.txt | 102.4 B | ||
| TutsGalaxy.com.txt | 0 B | ||
| ▲ 53 total files | |||
Description
What is this course about:
This course covers all the fundamentals about Apache Spark with Python and teaches you everything you need to know about developing Spark applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adapt Apache Spark for building big data processing pipeline and data analytics applications.
This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom. And much much more.
What will you learn from this lecture:
In particularly, you will learn:
An overview of the architecture of Apache Spark.
Develop Apache Spark 2.0 applications with PySpark using RDD transformations and actions and Spark SQL.
Work with Apache Spark’s primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
Scale up Spark applications on a Hadoop YARN cluster through Amazon’s Elastic MapReduce service.
Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.
Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators.
Best practices of working with Apache Spark in the field.
Big data ecosystem overview.
Why should you learn Apache Spark:
Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world.
Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks.
Apache Spark is the next-generation processing engine for big data.
Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you have access to that same big data technology right on your desktop.
Apache Spark is becoming a must tool for big data engineers and data scientists.
What programming language is this course taught in?
This course is taught in Python. Python is currently one of the most popular programming languages in the world! It’s rich data community, offering vast amounts of toolkits and features makes it a powerful tool for data processing. Using PySpark (the Python API for Spark) you will be able to interact with Apache Spark’s main abstraction, RDDs, as well as other Spark components, such as Spark SQL and much more!
Let’s learn how to write Spark programs with PySpark to model big data problems today!
30-day Money-back Guarantee!
You will get 30-day money-back guarantee from Udemy for this course.
If not satisfied simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked.
Are you ready to take your big data analysis skills and career to the next level, take this course now!
You will go from zero to Spark hero in 4 hours.
Who is the target audience?
Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field.
Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.
Data scientists or data engineers who want to advance their career by improving their big data processing skills.
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Udemy - Apache Kafka Series - Confluent Schema Registry and REST Proxy Posted by
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