Apache Spark Advanced Topics Training
Apache Spark Advanced Topics Training
Order this unique Training E-Learning course Apache Spark Advanced Topics online, 1 year 24/7 access to rich interactive videos and tests.
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Apache Spark Advanced Topics E-Learning
Order this unique E-Learning course today!
✔️ 1 year 24/7 access to rich interactive videos with voice support.
✔️ Includes progress monitoring via reporting tests to immediately test your knowledge.
Why choose this course?
Apache Spark is a powerful open-source framework that provides advanced big data analytics capabilities combined with speed and ease of use. This course focuses on the advanced features of Spark, allowing you to:
- Learn to understand and apply Spark Core better.
- Spark Streaming mastered for real-time data analysis.
- Used Spark SQL to manage and query data at scale.
- Gain knowledge of MLlib for machine learning, GraphX for graph computation, and SparkR for data science.
This training combines theory and practice to take your skills to the next level, ideal for professionals who want to tackle complex big data challenges.
Who should participate?
This course is perfect for:
- Big data analysts who want to expand their knowledge with advanced Spark capabilities.
- Data scientists who want to integrate machine learning and graph applications.
- Software developers who want to build optimized big data solutions.
- IT professionals who need a deeper understanding of Spark functionality.
Course content
Spark RDDs
Course: 1 Hour, 13 Minutes
- Course Introduction
- Review of Spark Stack
- Defining Lazy Evaluation
- Examining RDD Lineage
- Pre-partitioning RDDs
- Storing RDDs in Serialized Form
- Performing Numeric Operations
- Creating Custom Accumulators
- Optimizing Broadcasts
- Piping to External Applications
- Tuning Garbage Collection
- Performing Batch Importing
- Determining Memory Consumption
- Tuning Data Structures
- Minimizing Memory Usage of Reduce Tasks
- Setting the Levels of Parallelism
Data Frames and Spark SQL
Course: 43 Minutes
- Creating DataFrames
- Interoperating with RDDs
- Examining the Load and Save Functions
- Reading and Writing Parquet Files
- Using JSON Dataset as a DataFrame
- Reading and Writing Data in Hive Tables
- Reading and Writing Data Using JDBC
- Running Thrift JDBC/ODBC Server
- Practice: Tuning Spark
Course: 9 Minutes
Exercise: Tuning Spark
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Streaming Analytics
Course: 54 Minutes
- Course Introduction
- Examining Discretized Streams
- Ingesting TCP Socket Input Streams
- Reading File Input Streams
- Receiving Akka Actor Input Streams
- Consuming Kafka Input Streams
- Ingesting Flume Input Streams
- Setting Up Kinesis Input Streams
- Configuring Twitter Input Streams
- Implementing Custom Input Streams
- Describing Receiver Reliability
Transformations on DStreams
Course: 1 Hour, 19 Minutes
- Using UpdateStateByKey Operations
- Performing Transform Operations
- Performing Window Operations
- Performing Join Operations
- Using Output Operations on DStreams
- Using Data Frames and SQL Operations
- Using Learning Algorithms with MLlib
- Persisting Stream Data in Memory
- Enabling and Configuring Checkpointing
- Deploying Applications
- Monitoring Applications
- Reducing Batch Processing Times
Performance Tuning
Course: 19 Minutes
- Setting Batch Intervals
- Tuning Memory Usage
- Examining the Semantics of Fault Tolerance
Practice: Transformations on Dstreams
Course: 6 Minutes
- Exercise: Perform Transformations on DStreams
Machine Learning with MLlib
Course: 1 Hour, 12 Minutes
- Course Introduction
- Describing Data Types
- Examining Basic Statistics
- Exploring Linear SVMs
- Performing Logistic Regression
- Using Naive Bayes
- Creating Decision Trees
- Using Collaborative Filtering with ALS
- Clustering with K-means
- Clustering with Latent Dirichlet Allocation (LDA)
- Analyzing with Frequent Pattern Mining
GraphX
Course: 57 Minutes
- Examining the Property Graph
- Exploring the Graph Operators
- Performing Analytics with Neighborhood Aggregation
- Messaging with Pregel API
- Building Graphs
- Examining Vertex and Edge RDDs
- Optimizing Representation Through Partitioning
- Measuring Vertices with PageRank
R and Spark
Course: 37 Minutes
- Installing SparkR
- Running SparkR
- Using Existing R Packages
- Exposing RDDs as Distributed Lists
- Interoperating with DataFrames
- Using Parquet Files
- Running on a Cluster
Practice: Use MLlib
Course: 10 Minutes
- Exercise: Use MLlib
Get started today!
✔️ Learn advanced techniques to effectively tackle big data challenges.
✔️ Gain access to a comprehensive interactive learning experience.
✔️ Test and track your progress with reports and exercises.
Order your course now and become an expert in Apache Spark Advanced Topics!
| Language | English |
|---|---|
| Qualifications of the Instructor | Certified |
| Course Format and Length | Teaching videos with subtitles, interactive elements and assignments and tests |
| Lesson duration | 7:42 Hours |
| Progress monitoring | Yes |
| Access to Material | 365 days |
| Technical Requirements | Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge. |
| Support or Assistance | Helpdesk and online knowledge base 24/7 |
| Certification | Certificate of participation in PDF format |
| Price and costs | Course price at no extra cost |
| Cancellation policy and money-back guarantee | We assess this on a case-by-case basis |
| Award Winning E-learning | Yes |
| Tip! | Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform. |
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