Download Spark SQL & Hadoop (For Data Scientists & Big Data Analysts)

What you would learn in Spark SQL & Hadoop (For Data Scientists & Big Data Analysts) course?

Apache Spark is one of the most well-known systems to process extensive data.

Apache Hadoop continues to be used by numerous companies that want to save data on their premises. Hadoop lets these organizations efficiently store massive datasets across the spectrum of gigabytes up to petabytes.

The number of open positions for data science extensive data analysis, the roles of data engineering increase, and so will the need for people knowledgeable about Spark and Hadoop technology to fulfill these roles.

This course is designed especially for researchers, prominent data analysts, and data engineers who want to harness the potential of Hadoop and Apache Spark to make sense of the massive data.

This course will assist those who want to interact with big data or begin creating production-ready applications to create data that can be further analyzed with Spark SQL in a Hadoop environment.

The course is suitable for students at university and recent graduates who are eager to learn more about and experience with Spark & Hadoop and for anyone who wants to improve their SQL skills in a massive data-driven environment with Spark-SQL.

The course was designed to be short and give students the necessary and adequate amount of knowledge to allow them to use Hadoop & Spark without getting lost in the knowledge about the older APIs at a low level like RDDs.

When they have solved the problems contained in this course, students begin to build the capabilities and the confidence required to deal with real-world scenarios which may occur in a working environment.

(a) There are less than 30 issues that are covered in the course. They cover HDFs commands, essential data engineering tasks, and data analyses.

(b) Completely formulated solutions to every problem.

(c) Included is The Verulam Blue virtual machine, an environment that comes with a spark Hadoop cluster already installed to try your hand on the issues.

• The VM includes the Spark Hadoop environment, which allows students to write and read data into and out of the Hadoop file system, as well as create metastore tables for Hive's Hive megastore.

• All the data students will require for their issues are already stored on HDFS, and there is no reason for students to perform any additional work.

• The VM also comes with Apache Zeppelin installed. It is a notebook specific to Spark and is like that of Python's Jupyter notebook.

Students will be able to gain hands-on experience in a Spark Hadoop environment as they do:

• Converting data values in a particular format stored within HDFS to new values for data or a different data format, then writing the data into HDFS.

• Importing data from HDFS to be used in Spark applications and then writing the results into HDFS using Spark.

• Writing and reading files in a variety of formats for files.

• Conducting regular extract, transform, and load (ETL) operations on data with Spark API. Spark API.

• Metastore tables can be used as input sources or as an output sink for Spark-based applications.

• Understanding the basics of querying data using Spark.

• Filtering data using Spark.

• Writing queries that compute the aggregate statistic.

• Connecting disparate data sets using Spark.

• Sorting or ranking data.

Course Content:

• Students will gain hands-on experience in the Spark Hadoop environment that's free and available for download in this course.
• Students will have the opportunity to work on Data Engineering and Data Analysis Problems by using Spark on the Hadoop cluster within the sandbox environment provided as a part of the course.
• Issue HDFS commands.
• Converting data values in a particular format stored inside HDFS to new values for data or a different data format, and then writing the data into HDFS.
• Data is loaded from HDFS to usage in Spark applications, and then writing the results into HDFS by using Spark.
• Writing and reading files in a variety file formats.
• Executing the standard extract, transform, and load (ETL) operations on data using the Spark API.
• Metastore tables can be used as input sources or output sink for Spark-based software.
• Utilizing the knowledge of the basics of querying data in Spark.
• Filtering data using Spark.
• Writing queries that compute the aggregate statistic.
• Joining disparate datasets with Spark.
• Sorting or ranking data.

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