AWS EMR Tutorial [FULL COURSE in 60mins]

Johnny Chivers

8 chapters8 takeaways21 key terms5 questions

Overview

This comprehensive tutorial provides a hands-on guide to Amazon Elastic MapReduce (EMR), a managed cluster platform for big data processing. It covers essential setup, including VPC and Cloud9 environment configuration, and securely managing access with key pairs. The course delves into launching EMR clusters with various frameworks like Spark, Hive, and Pig, and demonstrates how to submit and monitor ETL jobs. It also explores using notebooks for interactive analysis, orchestrating workflows with Step Functions, and implementing auto-scaling for cost and performance optimization. The tutorial emphasizes practical application through a workshop booklet and GitHub resources, making it suitable for aspiring data engineers and scientists.

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Chapters

Configure a Virtual Private Cloud (VPC) with a public subnet and NAT gateway for isolated cloud resources.
Set up a Cloud9 IDE instance within the VPC, utilizing a free-tier EC2 instance for cost-effectiveness.
Generate an RSA key pair in .pem format for secure access to EMR resources.
Upload the key pair to the Cloud9 environment and secure it with chmod 400 permissions.
Download and unzip project resources from GitHub for use throughout the tutorial.

Properly configuring the network and development environment is crucial for secure and efficient interaction with EMR clusters and for ensuring all participants are working with the same tools and code.

Creating a VPC named 'emr tutorial' and launching a Cloud9 IDE instance within it, using the 'emr-tutorial.pem' key pair.

EMR is a managed cluster platform simplifying big data frameworks like Spark, Hive, and Pig.
A cluster consists of a master node (orchestration), core nodes (task execution and data storage), and optional task nodes (task execution only).
Resource management is handled by YARN (Yet Another Resource Negotiator).
Data can be stored on the Hadoop Distributed File System (HDFS) on core nodes or, more commonly in AWS, on Amazon S3, with EMR accessing it via EMRFS.
Each node also has a local file system for operating system and software, which is not typically used for big data processing.

Understanding the roles of different nodes and storage options is fundamental to designing, deploying, and optimizing EMR clusters for specific big data workloads.

Distinguishing between a master node that coordinates tasks and core nodes that store data and run computations.

Create an EMR cluster using advanced options to select specific applications like Hadoop, Spark, Hive, and Pig.
Configure the cluster's network settings to use the previously created VPC and a specific subnet (e.g., 1b).
Specify the number of master, core, and task nodes (e.g., 1 master, 2 core, 0 task nodes).
Select the correct EC2 key pair (e.g., 'emr-tutorial') for SSH access.
Adjust security group inbound rules to allow SSH (port 22) and custom TCP (port 9443) from the Cloud9 instance's public IP for cluster access and notebook connectivity.

Correctly configuring cluster applications, networking, node types, and security settings ensures the cluster is set up for the intended workloads and accessible from the development environment.

Launching a cluster named 'emr-touch' with Hadoop, Spark, Hive, and Pig, and configuring security groups to allow SSH from the Cloud9 instance's IP.

Spark is a fast, in-memory processing engine that supports multiple languages (Java, Scala, R, Python) and is a market leader in big data.
Set up an S3 bucket with 'files', 'input', 'output', and 'logs' folders to store data and job artifacts.
Upload the 'trip_data.csv' file to the 'input' folder in S3.
Execute a Spark ETL job using a Python script ('spark-etl.py') via the EMR cluster's command line, transforming CSV data to Parquet format.
Monitor job execution and view logs through the EMR console's 'Application User Interfaces' (Spark History Server) or directly via SSH.

Spark's efficiency and flexibility make it ideal for ETL tasks, and understanding how to deploy, run, and monitor Spark jobs on EMR is a core skill for data engineers.

Running a Spark job that reads 'trip_data.csv' from S3 input, converts it to Parquet, and writes it to the S3 output folder.

Access JupyterHub on the EMR cluster via HTTPS using the master node's IP address and port 9443.
Bypass browser security warnings by typing 'thisisunsafe' to access the login page.
Log in using provided credentials (username/password) and create a new 'PiSpark' notebook.
Execute notebook cells sequentially to import libraries, define S3 bucket paths, read data, count records, print schema, and add new columns (e.g., 'current_date').
EMR notebooks offer an alternative interactive environment for running similar code and analysis.

Notebooks provide an interactive and exploratory environment for data scientists and engineers to analyze data, develop ETL logic, and visualize results directly on the cluster.

Using a PiSpark notebook to read taxi trip data, display the top 20 rows, show the data schema, and add a 'current_date' column.

EMR Steps allow you to submit jobs (like Spark or Hive) to a cluster as a sequence of tasks.
Hive is a data warehousing solution that provides an SQL-like interface for querying data stored in various formats on distributed file systems.
Create a Hive table over existing data in S3 by executing an HQL script ('hive-taxi-test.hql') in the Hive CLI, specifying the correct S3 bucket location.
Query the created Hive table using SQL commands (e.g., `SELECT DISTINCT ... FROM ny_taxi_test;`).
Alternatively, submit an HQL script ('ny-taxi.hql') as an EMR Step, defining input and output locations in S3 for processing.

EMR Steps and Hive enable batch processing and SQL-based data warehousing on large datasets, providing structured access and analysis capabilities.

Creating a Hive table named 'ny_taxi_test' over CSV data in S3 and then querying it to retrieve distinct taxi IDs.

Pig is an abstraction layer over MapReduce, offering a scripting language (Pig Latin) for parallel data processing, though often slower than Spark.
Upload a Pig script ('my-taxi.pig') to an S3 'files' location.
Submit the Pig script as an EMR Step, defining S3 input and output paths for processing the data.
AWS Step Functions can orchestrate complex workflows by chaining multiple EMR steps (Spark, Hive, Pig) into a single, event-driven process.
Step Functions manage failures, retries, and service integrations, allowing developers to focus on ETL logic rather than orchestration.

Pig provides an alternative scripting approach for data processing, while Step Functions offer robust workflow automation, crucial for managing complex, multi-stage data pipelines.

Using Step Functions to automatically execute Spark, Hive, and Pig steps on an EMR cluster in sequence, disabling the cluster upon completion.

EMR Auto Scaling dynamically adjusts the number of nodes in a cluster based on predefined policies.
Configure cluster scaling policies by setting minimum and maximum node counts.
Define custom scaling rules based on metrics like 'apps running' (e.g., add a node if apps running >= 2 for 5 minutes, remove if apps running < 2 for 5 minutes).
Set cooldown periods (e.g., 60 seconds) to prevent rapid scaling fluctuations.
Utilize Step Functions to trigger workloads that test the auto-scaling policies, observing the cluster resize based on demand.

Implementing auto-scaling is essential for optimizing EMR cluster performance by providing necessary resources during peak loads and reducing costs by scaling down during idle periods.

Setting up a custom scaling policy that adds a node when 2 or more applications are running and removes a node when fewer than 2 applications are running, observing the cluster resize.

Key takeaways

1Amazon EMR simplifies running big data frameworks by managing cluster infrastructure and providing integrations with tools like Spark, Hive, and Pig.
2Securely configuring network (VPC), access (key pairs), and security groups is paramount before launching EMR clusters.
3EMR clusters consist of master, core, and optional task nodes, with YARN managing cluster resources.
4Data processing can leverage S3 as a primary storage layer, accessed by EMR via EMRFS, offering scalability and cost benefits over HDFS alone.
5Spark's in-memory processing and multi-language support make it a highly efficient and flexible choice for ETL tasks compared to older frameworks like MapReduce.
6Notebooks (JupyterHub, EMR Notebooks) provide interactive environments for developing, testing, and analyzing data directly on the cluster.
7EMR Steps and Step Functions enable batch job submission and complex workflow orchestration, automating multi-stage data pipelines.
8Auto Scaling policies are critical for balancing performance and cost by dynamically adjusting cluster size based on workload demands.

Key terms

Amazon EMRElastic MapReduceVPC (Virtual Private Cloud)Cloud9 IDEKey PairMaster NodeCore NodeTask NodeYARNHDFS (Hadoop Distributed File System)EMRFS (EMR File System)S3 (Simple Storage Service)SparkHivePigETL (Extract, Transform, Load)ParquetJupyterHubEMR StepsStep FunctionsAuto Scaling

Test your understanding

1What are the primary roles of the master, core, and task nodes in an EMR cluster?
2How does Spark's in-memory computing contribute to its faster processing speeds compared to disk-based frameworks like Hadoop MapReduce?
3Why is it important to configure security groups to allow SSH access from your Cloud9 instance to the EMR master node?
4Describe the process of submitting a Spark ETL job as an EMR Step and how you would monitor its execution.
5How can AWS Step Functions be used to orchestrate multiple EMR jobs (e.g., Spark, Hive, Pig) into a single automated workflow?