Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Pig Tutorial | Apache Pig Tutorial | What Is Pig In Hadoop? | Apache Pig Arch...Simplilearn
The document discusses key concepts related to the Pig analytics framework. It covers topics like why Pig was developed, what Pig is, comparisons of Pig to MapReduce and Hive, Pig architecture involving Pig Latin scripts, a runtime engine, and execution via a Grunt shell or Pig server, how Pig works by loading data and executing Pig Latin scripts, Pig's data model using atoms and tuples, and features of Pig like its ability to process structured, semi-structured, and unstructured data without requiring complex coding.
Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
This document provides an overview of Spark Pair RDDs and persistence. It defines Pair RDDs as RDDs of key-value pairs that enable aggregations. It describes how to create Pair RDDs in Scala, Python, and Java and covers common Pair RDD transformations like groupByKey, reduceByKey, aggregateByKey, combineByKey, joins, sorting by key, and actions. It also discusses the differences between groupByKey and reduceByKey and demonstrates various Pair RDD functions and persistence levels.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Spark is an open source cluster computing framework for large-scale data processing. It provides high-level APIs and runs on Hadoop clusters. Spark components include Spark Core for execution, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning. The core abstraction in Spark is the resilient distributed dataset (RDD), which allows data to be partitioned across nodes for parallel processing. A word count example demonstrates how to use transformations like flatMap and reduceByKey to count word frequencies from an input file in Spark.
Simplifying Big Data Analytics with Apache SparkDatabricks
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It improves on MapReduce by allowing data to be kept in memory across jobs, enabling faster iterative jobs. Spark consists of a core engine along with libraries for SQL, streaming, machine learning, and graph processing. The document discusses new APIs in Spark including DataFrames, which provide a tabular interface like in R/Python, and data sources, which allow plugging external data systems into Spark. These changes aim to make Spark easier for data scientists to use at scale.
This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
DNS is critical network infrastructure and securing it against attacks like DDoS, NXDOMAIN, hijacking and Malware/APT is very important to protecting any business.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
This document provides an introduction to Apache Hive, including:
- What Apache Hive is and its key features like SQL support and rich data types
- An overview of Hive's architecture and how it works within the Hadoop ecosystem
- Where Hive is useful, such as for log processing, and not useful, like for online transactions
- Examples of companies that use Hive
- An introduction to the Hive Query Language (HQL) with examples of creating tables, loading data, queries, and more.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Introduction to Apache Airflow - Data Day Seattle 2016Sid Anand
Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It includes a DAG scheduler, web UI, and CLI. Airflow allows users to author DAGs in Python without needing to bundle many XML files. The UI provides tree and Gantt chart views to monitor DAG runs over time. Airflow was accepted into the Apache Incubator in 2016 and has over 300 users from 40+ companies. Agari uses Airflow to orchestrate message scoring pipelines across AWS services like S3, Spark, SQS, and databases to enforce SLAs on correctness and timeliness. Areas for further improvement include security, APIs, execution scaling, and on
This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.
Airflow is a workflow management system for authoring, scheduling and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It has features like DAGs to define tasks and their relationships, operators to describe tasks, sensors to monitor external systems, hooks to connect to external APIs and databases, and a user interface for visualizing pipelines and monitoring runs. Airflow uses a variety of executors like SequentialExecutor, CeleryExecutor and MesosExecutor to run tasks on schedulers like Celery or Kubernetes. It provides security features like authentication, authorization and impersonation to manage access.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document compares Network Address Translation (NAT) Gateways and NAT instances in AWS. Some key differences include:
- NAT Gateways are highly available across Availability Zones, while NAT instances require manual failover configuration.
- NAT Gateways have higher bandwidth limits and performance optimized for NAT traffic. NAT instances are limited by the instance type.
- NAT Gateways have a fixed hourly cost, while NAT instance costs depend on instance size and usage.
- Only NAT Gateways can be configured without a public IP address or associated security groups.
AWS Atlanta meetup group Slides from March 20th 2015 group presentation with CloudCheckr COO Aaron Klein speaking about Tracking, Allocating and Optimizing AWS Costs.
Sub topics include Instance and Service Tagging strategies in AWS for Master and child account management.
This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
6) Lazy Evaluation & Instant Evaluation
7) Lineage Graph
8) flatMap and Union
9) Scala Transformations - Union
10) Scala Actions - saveAsTextFile(), collect(), take() and count()
11) More Actions - reduce()
12) Can We Use reduce() for Computing Average?
13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
DNS is critical network infrastructure and securing it against attacks like DDoS, NXDOMAIN, hijacking and Malware/APT is very important to protecting any business.
This document provides an overview of the big data technology stack, including the data layer (HDFS, S3, GPFS), data processing layer (MapReduce, Pig, Hive, HBase, Cassandra, Storm, Solr, Spark, Mahout), data ingestion layer (Flume, Kafka, Sqoop), data presentation layer (Kibana), operations and scheduling layer (Ambari, Oozie, ZooKeeper), and concludes with a brief biography of the author.
The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
This document provides an introduction to Apache Hive, including:
- What Apache Hive is and its key features like SQL support and rich data types
- An overview of Hive's architecture and how it works within the Hadoop ecosystem
- Where Hive is useful, such as for log processing, and not useful, like for online transactions
- Examples of companies that use Hive
- An introduction to the Hive Query Language (HQL) with examples of creating tables, loading data, queries, and more.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Introduction to Apache Airflow - Data Day Seattle 2016Sid Anand
Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It includes a DAG scheduler, web UI, and CLI. Airflow allows users to author DAGs in Python without needing to bundle many XML files. The UI provides tree and Gantt chart views to monitor DAG runs over time. Airflow was accepted into the Apache Incubator in 2016 and has over 300 users from 40+ companies. Agari uses Airflow to orchestrate message scoring pipelines across AWS services like S3, Spark, SQS, and databases to enforce SLAs on correctness and timeliness. Areas for further improvement include security, APIs, execution scaling, and on
This document provides an overview of MapReduce in Hadoop. It defines MapReduce as a distributed data processing paradigm designed for batch processing large datasets in parallel. The anatomy of MapReduce is explained, including the roles of mappers, shufflers, reducers, and how a MapReduce job runs from submission to completion. Potential purposes are batch processing and long running applications, while weaknesses include iterative algorithms, ad-hoc queries, and algorithms that depend on previously computed values or shared global state.
Airflow is a workflow management system for authoring, scheduling and monitoring workflows or directed acyclic graphs (DAGs) of tasks. It has features like DAGs to define tasks and their relationships, operators to describe tasks, sensors to monitor external systems, hooks to connect to external APIs and databases, and a user interface for visualizing pipelines and monitoring runs. Airflow uses a variety of executors like SequentialExecutor, CeleryExecutor and MesosExecutor to run tasks on schedulers like Celery or Kubernetes. It provides security features like authentication, authorization and impersonation to manage access.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document compares Network Address Translation (NAT) Gateways and NAT instances in AWS. Some key differences include:
- NAT Gateways are highly available across Availability Zones, while NAT instances require manual failover configuration.
- NAT Gateways have higher bandwidth limits and performance optimized for NAT traffic. NAT instances are limited by the instance type.
- NAT Gateways have a fixed hourly cost, while NAT instance costs depend on instance size and usage.
- Only NAT Gateways can be configured without a public IP address or associated security groups.
AWS Atlanta meetup group Slides from March 20th 2015 group presentation with CloudCheckr COO Aaron Klein speaking about Tracking, Allocating and Optimizing AWS Costs.
Sub topics include Instance and Service Tagging strategies in AWS for Master and child account management.
This document discusses using Docker on AWS. It describes using Docker to deploy highly scalable applications across multiple AWS regions and availability zones. It also discusses using a private Docker registry hosted on EC2 and S3 to store custom Docker images. Finally, it summarizes using Amazon EC2 Container Service (ECS) for container management on AWS, including concepts like clusters, tasks, and container instances.
A presentation on the microservice Lambda by AWS for creating Lambda packages in the Python language and examples of good and bad use cases for using lambda.
Presented by the AWS Atlanta Meetup group
This document provides steps to integrate Jenkins with Amazon S3 for artifact storage. It demonstrates installing the Jenkins S3 plugin, configuring credentials for an IAM user with S3 access, and configuring a Jenkins job to upload build artifacts like an index.html file to an S3 bucket after a build. With this integration, artifacts can be reliably stored on S3, which is cheaper for storage than other options and allows easy tracking and management of files.
The document discusses Redshift Workload Management (WLM) which allows managing concurrent queries running on Redshift. It covers defining query queues, modifying the WLM configuration, assigning queries to queues, and WLM properties. Tips provided include separating long-running queries, setting total concurrency below 15, avoiding too many queues, and using the superuser queue for troubleshooting. The document also discusses Redshift performance factors and compression encodings.
Test-Driven Infrastructure with CloudFormation and Cucumber. Stelligent
The document discusses test-driven infrastructure using CloudFormation and Cucumber. It recommends writing automated tests with Cucumber to verify infrastructure provisioned by CloudFormation templates, versioning infrastructure scripts and configurations, and integrating infrastructure testing into continuous delivery pipelines. Examples are provided of using Cucumber features and step definitions to test CloudFormation templates for provisioning AWS resources.
This document discusses AWS CloudFormation, which allows users to create and manage AWS resources through templates written in JSON. It describes the basic structure of a CloudFormation template, which includes sections for description, parameters, mappings, resources, and outputs. Parameters allow passing values to the template, mappings specify different settings for different AWS regions, resources define the AWS infrastructure to create, and outputs define values that are returned after stack creation. Examples are provided of basic CloudFormation templates and how to launch, update, and troubleshoot templates.
AWS Certification Paths And Tips for Getting CertifiedAdam Book
The document provides an overview of various AWS certifications, including the Solutions Architect (Associate and Professional levels), Certified Developer (Associate), SysOps Administrator (Associate), and DevOps Engineer (Professional) certifications. It outlines the domains and percentages covered in each exam. The document also provides tips for preparing for AWS certification exams, such as reading documentation, creating a practice AWS account, practicing sample questions, watching relevant videos, and not taking too much time between exams.
A look at AWS web application firewall service from the September meeting of the Atlanta AWS Meetup group
Looking at how the service works with cloudfront along with it's pricing model compared with other WAF offerings.
From development environments to production deployments with Docker, Compose,...Jérôme Petazzoni
In this session, we will learn how to define and run multi-container applications with Docker Compose. Then, we will show how to deploy and scale them seamlessly to a cluster with Docker Swarm; and how Amazon EC2 Container Service (ECS) eliminates the need to install,operate, and scale your own cluster management infrastructure. We will also walk through some best practice patterns used by customers for running their microservices platforms or batch jobs. Sample code and Compose templates will be provided on GitHub afterwards.
The SlideShare 101 is a quick start guide if you want to walk through the main features that the platform offers. This will keep getting updated as new features are launched.
The SlideShare 101 replaces the earlier "SlideShare Quick Tour".
The document discusses Amazon Web Services (AWS) and provides information about AWS regions and availability zones, Elastic Compute Cloud (EC2) instances, Elastic Block Storage (EBS), security groups, Elastic Load Balancing (ELB), and using CloudFormation to define AWS resources like EC2 instances, security groups, and ELBs. It includes pricing information for different types of EC2 instances and reserved capacity options.
AWS Presents: Infrastructure as Code on AWS - ChefConf 2015Chef
Find out how to create automated infrastructure deployments using versioned Infrastructure as Code - CloudFormation templates on AWS. This talk will walk through two example CloudFormation templates. The first template will show how to use CloudFormation via AWS cli commands to create a Chef Server 12 instance and have it upload it’s client validation pem into private S3 bucket also created by the template. The second template will show how to use CloudFormation to create multiple client node instances in AWS EC2 and have them automatically bootstrap into the new Chef 12 Server instance. Links will be provided to the CloudFormation template code used for the demo for example purposes.
https://youtu.be/WXLDdGxfEsI
Hands-On AWS: Java SDK + CLI for Cloud DevelopersMeetu Maltiar
This workshop provides a practical, project-based walkthrough of core AWS services using Java (SDK v2) and AWS CLI. With real code, shell scripts, and architecture patterns, participants learn how to build and deploy scalable cloud-native apps within the AWS Free Tier. Modules include S3, EC2, Lambda, API Gateway, DynamoDB, SNS, SQS, IAM, CloudFormation, and more—culminating in a full-stack Capstone Project using Java Lambda.
1. The document demonstrates how to use various AWS services like Kinesis, Redshift, Elasticsearch to analyze streaming game log data.
2. It shows setting up an EC2 instance to generate logs, creating a Kinesis stream to ingest the logs, and building Redshift tables to run queries on the logs.
3. The document also explores loading the logs from Kinesis into Elasticsearch for search and linking Kinesis and Redshift with Kinesis Analytics for real-time SQL queries on streams.
Презентация подготовлена по материалам выступления Ксении Перепечиной на витебском MiniQ#19, который был проведен 10 октября 2019:
https://vk.com/miniq19;
https://communities.by/events/miniq-vitebsk-19.
Про доклад:
Мы побеседуем про Infrastructure As A Code на примере использования AWS Cloudformation и Serverless Application Model, про особенности этих сервисов и некоторые практические полезные советы по их использованию.
Manage cloud infrastructures using Zend Framework 2 (and ZF1)Enrico Zimuel
The cloud computing is becoming more and more efficient and important for the deploy of web applications in PHP. According with the idea of the Simple Cloud API initiative, the Zend Framework team has developed a new Zend\Cloud\Infrastructre to help developers in the management of cloud infrastructure. In this talk we will present this new class showing some use cases using different vendors.
AWS SSA Webinar 28 - Getting Started with AWS - Infrastructure as CodeCobus Bernard
One of the parts of doing things properly at scale is being able to describe your infrastructure as code and deploy it as such. If we already treat our infrastructure as code, why not apply all the best practices of software delivery to infrastructure delivery.
In this session we look into Infrastructure as Code solutions, best practices and patterns on AWS.
Managed services such as AWS Lambda and API Gateway allow developers to focus on value adding development instead of IT heavy lifting. This workshop introduces how to build a simple REST blog backend using AWS technologies and the serverless framework.
Infrastructure as Code: Tools of the trade.
Presented in collaboration with Manhattan Partners this is an Introduction to Infrastructure as Code (scripted infrastructure), the pros/cons, examples, popular tools and frameworks.
Michael Pearce, DevOps Engineer @ Peak AI.
This document discusses Azure Infrastructure as Code (IAC) and Azure Resource Manager (ARM) templates. It provides an overview of ARM and how it can be used to define and deploy Azure resources from templates. Key points covered include the core structure of ARM templates, using parameters and variables, and examples of deploying templates from the portal, PowerShell, and Azure Quick Start templates. Demos of template deployment and extraction are also mentioned.
DevOps Fest 2019. Alex Casalboni. Configuration management and service discov...DevOps_Fest
Your system is composed of highly decoupled, independent, fast, and modular microservices. But how can they share common configurations, dynamic endpoints, database references, and properly rotate secrets? Based on the size and complexity of your serverless system, you may simply use environment variables or eventually opt for some sort of centralized store. And then how do integrate all of this with monitoring and automation tooling? During this session, I will present the ideal solutions and some of the alternatives available on AWS (such as AWS Systems Manager Parameter Store and AWS Secrets Manager). I will also discuss the best use cases for each solution and the corresponding best practices to achieve the highest standards for security and performance.
AWS Atlanta Meetup - June 19 - AWS organizations - Account StructureAdam Book
AWS Organizations allows you to consolidate multiple AWS accounts into an organization that you can centrally manage. You can organize accounts into organizational units (OUs) and apply different policies to each OU. When you create an organization, you can choose between billing mode, which only controls billing, and full-control mode, which allows for complete account management control.
AWS Atlanta Meetup for April 2019 going over Systems Manager service and the different features and functions of the service including the Run command, Parameter Store, and Inventory
AWS Secrets Manager enables customers to securely store and centrally manage secrets like database credentials and API keys. It integrates with services like RDS to allow automated and safe rotation of secrets without breaking applications. Secrets Manager provides fine-grained access control and auditing of secrets through encryption and permissions. Developers can retrieve secrets from applications using SDKs and APIs.
These slides are from the September 2017 group about the 3 types of Load Balancers in AWS - Classic Load Balancer, Application Load Balancer, and Network Load Balancer
AWS Atlanta meetup CloudFormation conditionals Adam Book
These are the slides from the December 19, 2018 AWS Atlanta Meetup Group. The topic was cloudformation conditionals and using them in your cloud formation templates (both JSON and YAML) to enhance your templates to make them more powerful.
Aws Atlanta meetup - Understanding AWS ConfigAdam Book
AWS Config provides the following services:
- Assesses and retrieves configurations of AWS resources and produces snapshots of current configurations.
- Evaluates AWS resource configurations against rules for desired settings and sends notifications when resources are modified.
- Shows relevant relationships between resources to help with security analysis and troubleshooting.
SSM combined with Simple AD are powerful tools that can help you and your organization get away from things like every user using the Administrator username and password to get into the instances.
These slides are from the AWS Atlanta Meetup group's February 2016 meeting -http://www.meetup.com/AWS-Atlanta/
This document discusses architecting applications on AWS for high availability across multiple regions. It begins by reviewing some notable outages and what is covered by typical SLAs. It then provides an overview of initial steps like using auto scaling, ELB, and CloudWatch. It discusses moving beyond a single availability zone to multiple zones. The main topic is setting up applications across multiple AWS regions for redundancy in case an entire region fails. Key services mentioned for high availability architectures are S3, CloudFront, ELB, CloudWatch, and SQS.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Rock, Paper, Scissors: An Apex Map Learning JourneyLynda Kane
Slide Deck from Presentations to WITDevs (April 2021) and Cleveland Developer Group (6/28/2023) on using Rock, Paper, Scissors to learn the Map construct in Salesforce Apex development.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
"Client Partnership — the Path to Exponential Growth for Companies Sized 50-5...Fwdays
Why the "more leads, more sales" approach is not a silver bullet for a company.
Common symptoms of an ineffective Client Partnership (CP).
Key reasons why CP fails.
Step-by-step roadmap for building this function (processes, roles, metrics).
Business outcomes of CP implementation based on examples of companies sized 50-500.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
5. CloudFormation Template
There are 8 sections of a Cloud formation template, most
of which are optional
Format Version
(optional)
Description (optional)
Metadata (optional)
Mappings (optional)
Parameters(optional)
Conditions(optional)
Resources (required)
Outputs(optional)
6. CloudFormation
Best Practice
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/best-practices.html
As you use Cloud Formation make sure you follow the best
practices for success
• Do Not Embed Credentials in You Templates
• Use AWS-Specific Parameter Types
• Use Parameter Constraints
• Validate Templates Before Using them
• Manage All Stack Resources Through AWS Cloud Formation
7. CloudFormation
Intrinsic Functions
Function Overview
Fn::Base64 returns the Base64 representation of the input string (user data)
Fn::FindInMap returns the value corresponding to keys in a two-level map that is
declared in the Mappings section
Fn::GetAtt returns the value of an attribute from a resource in the template.
Fn::GetAZs returns an array that lists Availability Zones for a specified region.
Fn::Join appends a set of values into a single value, separated by the
specified delimiter.
Fn::Select returns a single object from a list of objects by index.
Ref returns the value of the specified parameter or resource.
8. CloudFormation
Mappings
The Mappings section is optional but is matches a
key to a corresponding set of named values.
If you want to set values based on region, you can
create a mapping that uses the key as the name and
then contains the values you want to specify for each
region.
You cannot include parameters, pseudo parameters, or intrinsic
functions in the Mappings section.
12. Fn::FindInMap
"Resources" : {
"myEC2Instance" : {
"Type" : "AWS::EC2::Instance",
"Properties" : {
"ImageId" : { "Fn::FindInMap" : [ "RegionMap", { "Ref" :
"AWS::Region" }, "32"]},
"InstanceType" : "m1.small" }
}
}
}
This function performs lookups, it accepts a ‘mappings’ object on of
one or two keys and then returns a value
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-findinmap.html
13. Fn::Base64
{ "Fn::Base64" : ”apt-get update –y " }
This function accepts plain text and converts it to Base 64
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-base64.html
14. Fn::Join
"Outputs" : {
"URL" : {
"Description" : "The URL of your demo website",
"Value" : { "Fn::Join" : [ "", [ "http://", { "Fn::GetAtt" : [
"ElasticLoadBalancer", "DNSName" ]}]]}
}
}
This can be used to concatenate various components to produce
things such as a URL.
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-join.html
15. Fn::GetAtt
Some examples of attributes that can be called are:
• EC2 -> PrivateIp
• EC2-> PublicIp
• ElasticLoadBalancing -> DNSName
• IAM::Group -> ARN
• S3 Bucket -> DomainName
• Simple AD -> Alias
As you dynamically create items in your Cloud Formation templates ,
you may need to use some of the Attributes after they are created.
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-getatt.html
16. Fn::GetAtt
"MyEIP" : {
"Type" : "AWS::EC2::EIP",
"Properties" : {
"InstanceId" : { "Ref" : "MyEC2Instance" }
}
}
“Fn:GetAtt” :[ “MyEIP”, “AllocationId” ]
As you dynamically create items in your Cloud Formation templates
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-getatt.html
17. Fn::GetAZs
{ "Fn::GetAZs" : "us-east-1" }
{ "Fn::GetAZs" : { "Ref" : "AWS::Region" } }
The intrinsic function Ref returns to value of the specified
parameter or resource.
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-select.html
NOTE: You can use the Ref function in the Fn::GetAZz function.
18. Fn::Select
{ “Fn::Select” : [ “0”, {”Fn::GetAZs” : “”} ] }
Selects a single object from a list of object and can be paired with
other functions such as Fn::GetAZs
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-select.html
The output is the first Availablity zone in the region where the
template is applied.
Replacing the 0 with a 1 would select the second Availability Zone
19. Fn::Ref
"MyEIP" : {
"Type" : "AWS::EC2::EIP",
"Properties" : {
"InstanceId" : { "Ref" : "MyEC2Instance" }
}
}
The intrinsic function Ref returns to value of the specified
parameter or resource.
For more info
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-
reference-ref.html
20. Cloud Formation Templates
Real World Examples
Photo curtesy
of Stephen Radford via
http://snap.io
#5: AWS already has managed policies for SSM to attached either to your users or Roles.These can be easily found by going to to policy section of IAM and then searching for SSM
#6: Some sections in a template can be in any order.
If you use a tool such as troposphere then the output can be placed out as Alphabetical vs logical if you are used to the templates provided by AWS
#7: With constraints, you can describe allowed input values so that AWS CloudFormation catches any invalid values before creating a stack. You can set constraints such as a minimum length, maximum length, and allowed patterns. For example, you can set constraints on a database user name value so that it must be a minimum length of eight character and contain only alpha-numeric characters.
#8: Intrinsic functions are inbuilt functions provided by AWS to help you manage, reference, and conditionally act upon resources, situations and inputs to a stack
You can compare intrinsic functions to logical operations in programming such as:
If – Else, Case, Switch etc
#9: Although the most used case with mappings is with AMI’s and bits. There are other cases where you can use mappings for quick lookups
#10: This example shows a Mappings section with a map RegionMap, which contains five keys that map to name-value pairs containing single string values. The keys are region names. Each name-value pair is the AMI ID for the 32-bit AMI in the region represented by the key.
#11: This example shows a Mappings section with a map RegionMap, which contains five keys that map to name-value pairs containing single string values. The keys are region names. Each name-value pair is the AMI ID for the 32-bit AMI in the region represented by the key.
#12: This example shows a Mappings section being used in an autoscale group.
#14:
Its useful when other elements in a stack need Base 64 input such as EC2 user data
#15:
One of the best uses of the Join is in the output section and to produce the output endpoint for your users.
#16: Remember to include the DependsOn piece in your resources if you downstream resources needs the attribute of a previously created resource
#20: This is probably the most useful and easiest of the Intrinsic functions I’ve found to date.