Solving 10 Hadoop'able Problems
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- Explore the Hadoop big data Ecosystem in a nutshell
- Process payment data from an event stream using the streaming API: Payment Analyzer
- Detect BOT traffic using Spark Streaming, make log data queryable, and investigate customer data
- Supply Chain analysis - find top-seller items in a streaming way, enhance top-seller items
- Analyze Customer churn amounts quantitatively with DataFrame queries
- Perform IoT sensor data analysis with device response to system failures and data streams
- High-performance computation with neighborhood aggregations
- Page ranking using Spark GraphX
- Threat Analysis – Analyzing weblogs for suspicious activity and anomalies in network traffic
- Extract information from unstructured text via Spark DataFrames
- Perform sentiment analysis of posts using Logistic Regression, and find the author of a post
- Find what product users want to buy using Cloudera Sandbox Toolkit
- Use movie history to suggest content, and test and experiment with Recommendation Enginec
- Knowledge of solving data problems is required
The Apache Hadoop ecosystem is a popular and powerful tool to solve big data problems. With so many competing tools to process data, many users want to know which particular problems are well suited to Hadoop, and how to implement those solutions.
To know what types of problems are Hadoop-able it is good to start with a basic understanding of the core components of Hadoop. You will learn about the ecosystem designed to run on top of Hadoop as well as software that is deployed alongside it. These tools give us the building blocks to build data processing applications. This course covers the core parts of the Hadoop ecosystem, helping to give a broad understanding and get you up-and-running fast. Next, it describes a number of common problems as case-study projects Hadoop is able to solve. These sections are broken down into sections by different projects, each serving as a specific use case for solving big data problems.
By the end of this course, you will have been exposed to a wide variety of Hadoop software and examples of how it is used to solve common big data problems.
About the Author
Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservice architectures and functional programming. He dedicates considerable time and effort to be better every day. Recently, he's been delving into big data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.
Tomasz thinks that we should always try to consider different solutions and approaches to solving a problem. Recently, he was a speaker at several conferences in Poland - Confitura and JDD (Java Developer's Day) and also at Krakow Scala User Group.
He also conducted a live coding session at Geecon Conference.
- Data Engineers, and Machine Learning and Data analysts