This assessment is a graded quiz based on the module covered in this week.

In this module, you will learn about the Hive scripting language and its usage for mining data from Hadoop clusters. Hive provides an SQL dialect called Hive Query Language (abbreviated HiveQL or just HQL) for querying data stored in a Hadoop cluster. Hive is most suited for data warehouse applications, where relatively static data is analyzed, fast response times are not required, and when the data is not changing rapidly. Hive makes it easier for developers to port SQL-based applications to Hadoop, compared with other Hadoop languages and tools. Like all SQL dialects in widespread use, it does not fully conform to any particular revision of the ANSI SQL standard. It is perhaps closest to MySQL’s dialect, but with significant differences. Hive supports several sizes of integer and floating-point types, a boolean type, and character strings of arbitrary length. Lastly, taking a real-world data set, you will load it in the Ambari environment for analysis using HDFS and HQL. You will go through the process of creating tables, loading data, and analyzing it using a Hive Query Language.

This assessment is a graded quiz based on the modules covered this week. 

In this module, you will learn about the Pig Latin scripting language and how you can leverage it to query big data on Hadoop clusters. You will also learn about the different data types and commands available in the Pig Latin language and how they can be used to define and manipulate data in the Hadoop ecosystem. Furthermore, you will be to work on a practical example of a publicly available data set to run Pig Latin scripts for data analysis.

In this module, you will be introduced to the need for NoSQL databases. You will also get introduced to HBase, a NoSQL database, and its role in the Hadoop ecosystem. You will learn about the CAP theorem and how it affects the trade-offs between choosing the different NoSQL database options available on Hadoop. You will also learn about CAP consistency, availability, and partition tolerance in detail and how they affect our choice of technology to access and manipulate data on Hadoop. Lastly, you will get insights into other emerging cloud-based NoSQL solutions.

This assessment is a graded quiz based on the modules covered this week.

In this module, you will be introduced to the popular Apache Spark platform for Big Data processing. You will explore the key components of Apache Spark that provide significant benefits in distributed computing. You will also be introduced to the Resilient Distributed Datastores (RDD) and the Spark DataFrames. Furthermore, you will be introduced to Spark SQL and Spark Streaming.

This assessment is a graded quiz based on the module covered in this week.

In this module, you will learn about MLlib, which is used for making predictions on large datasets that need distributed processing. You will be working on regression and classification tasks for large datasets. Then, a hands-on exercise with streaming data from the twitter API is implemented. This is a predictive streaming application to show participants an end-to-end big data scenario.

Course Wrap-Up Video