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What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

A data pipeline is a set of actions that extracts data from numerous sources. It is a computerized process where the system takes columns from the database and merges them with other columns from this API. It also combines subset rows and corresponding values, alternates NAs with the median and loads them in this other database. This is known as a “job”, and pipelines are made of many jobs. Generally, the endpoint for a data pipeline is a data lake, such as Hadoop, S3, or a relational database. An ideal data pipeline should have the following properties:Low Occurrence Inactivity: Data scientists should have accessibility to the data. Users should be able to raise a query to recover the recent event data in the pipeline. Usually, this happens in minutes or seconds of the event being directed to the data collection endpoint. Scalability: A data pipeline should be able to gauge billions of data points, and product sales. Collaborative Querying: A highly operational data pipeline should support both long-running batch queries and minor interactive queries that allow data scientists to discover tables and comprehend the data scheme.Versioning: You should be able to edit and customize your data pipeline and event definitions without damaging the framework Monitoring: Data tracking and monitoring are important to check if the data is dispatched properly. In case of a failure, immediate alerts should be generated through tools such as PagerDuty.Testing: You should be able to test your data pipeline with test events that do not end up in your data lake or database, but that do test components in the pipeline.Do You want to Get AWS Certified? Learn about various AWS Certification in detailData Pipeline- UsageHere are a few things you can do with Data Pipeline.Convert received data to a common format.Prepare data for investigation and imagining.Travel between databases.Share data processing logic across web apps, batch jobs, and APIs.Power your data ingestion and integration tools.Input large XML, CSV, and fixed-width files.Substitute batch jobs with real-time dataNote that the Data Pipeline does not levy a specific structure on your data. All the data flowing through your pipelines can follow the same plan or an alternative NoSQL approach. The NoSQL feature offers a diverse structure to the data that can be altered at any point in your pipeline.What are the Types of DataData is typically defined with the following labels:Raw Data: This is on processed data stored in the message encoding format which is used to send tracking events, such as JSON. Processed Data: Processed data is raw data that has been deciphered into event-specific formats, with an applied plan.Cooked Data: Processed data that has been amassed or abridged is referred to as cooked data.The Evolution of Data PipelinesOver the past two decades the framework for accumulating and analyzing data been drastically changed. Earlier users would store data locally through log files, today we have modern systems that can trace data activity and use machine learning for real-time solutions. There are four different approaches to pipelines:Flat File Era: Data is saved locally on game serversDatabase Era: Data is staged in flat files and then loaded into a databaseData Lake Era: Data is stored in Hadoop/S3 and then loaded into a DBServerless Era: Managed services are used for storage and queryingEach of the steps supports the grouping of greater data sets. But it ultimately depends on the goal of the company to decide how the data is to be utilized and distributed.Application of Data PipelinesMetadata: Data Pipeline lets users connect metadata to each separate record or field.Data processing: Dataflows when processed and broken into smaller units, are easier to work with. It also quickens the process and saves on memory.Adapting to Apps: Data Pipeline adjusts to your applications and services. It occupies a small footprint of less than 20 MB on disk and in RAM. Flexible Data Components: Data Pipeline comes with readers and writers integrated to stream the inflow or outflow of data. There are also stream operators for controlling this data flow.Data Pipeline TechnologiesSome examples of products used in building data pipelines. These tools are used by engineers to find competent results and enhance the system’s performance and reach; Data warehousesETL toolsData Prep toolsLuigi: a workflow timetable that can be used to manage jobs and processes in Hadoop and similar systems.Python / Java / Ruby: programming languages used to transcribe processes in many of these systems.AWS Data Pipelines: another workflow management service that charts and implements data movement and processesKafka: a real-time streaming platform that allows you to move data between systems and applications, can also transform or react to these data streams.Types of data pipeline solutionsThe following list shows the most popular types of pipelines available:Batch: Batch processing is most valuable of all as it lets you move huge volumes of data at a steady interval.Real-time: These tools are improved to develop data in real time. Cloud native: These tools are optimized to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, and are a cost effective and quick technique to enhance the infrastructure.Open source: These tools are a cheaper alternative to a vendor. Open source tools are often inexpensive but require technical know-how on the part of the user. The platform is open for all to optimise and edit any way they want. AWS Data PipelineAWS Data Pipeline is a web service that supports dependable process and transfer data between a diverse range of AWS services, as well as on-premises data sources. With the AWS Data Pipeline, you can frequently keep in contact with the data and back where it’s deposited. Developers can also customize the data, convert and modify it at scale, and resourcefully allocate the results to other AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.AWS Data Pipeline aids you in creating an intricate data processing network. It takes care of all the data monitoring, tracking and optimization tasks. AWS Data Pipeline also allows you to change the data that was previously protected in the on-site data storage facility.Decoding Data Pipelines Let’s look into the process of assigning, transferring, altering and storing data via pipelines; Sources: First and foremost, we decide where we get the data from. Data can be accessed from different sources and in different formats. RDBMS, Application APIs, Hadoop, NoSQL, cloud sources, are a few primary sources. After the data is retrieved, it has to pass through the security controls and follow set protocols. next, the data schema and statistics are gathered about the source to simplify pipeline design.List of common terms related to data science Joins: It is common for data to be shared from different sources as part of a data pipeline.Extraction: Some separate data elements may be implanted in bigger fields. In some cases numerous values are clustered together. Or, distinct values may need to be removed- data pipelines allow all that. Standardization: Data needs to be consistent. It should follow a unit of measure, dates, attributes such as color or size, and codes related to industry standards.Correction: Data, especially raw data can contain a lot of errors. Some common errors are- invalid fields that are not present or abbreviations that need to be extended. There may also be corrupt records that need to be detached or studied in an isolated process.Loads: Once the data is ready, it needs to be loaded into a system for scrutiny. The endpoint is generally an RDBMS, a data warehouse, or Hadoop. Each destination has its own set of regulations and restrictions that need to be followed. Automation: Data pipelines are usually completed many times, and characteristically set on a schedule. This simplifies the error detection process and aids monitoring by sending regular reports to the system.Moving Data Pipelines Many corporations have hundreds or thousands of data pipelines. Companies shape each pipeline with one or more technologies, and each pipeline might follow a different approach. Datasets often start with an establishment’s customer base. But there are cases where they will also initiate with their assumed departments within the organization itself. Thinking of data as events simplifies the process. Events are logged in, integrated and then transmuted across the pipeline. The data is then changed and altered to suit the systems that they are moved to. Moving data from place to place means that different end users can use it more methodically and accurately. Users can now access the data from one place rather than refer to multiple sources. Good data pipeline architecture will be able to provide justification for all sources of events. It would also have an explanation or reason to support the setups and schemes caring for these datasets. Event frameworks help you get hold of events from your applications a lot faster. This is achieved by making an event log that can then be processed for use.ConclusionA career in data science is a very profitable decision considering the revolutionary discoveries made in the field each day. We hope that this information was useful in helping the reader understand all about data pipelines and why they are important.
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What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

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  • by Joydip Kumar
  • 06th Sep, 2019
  • Last updated on 06th Sep, 2019
  • 11 mins read
What Is a Data Pipeline? What Are the Properties and Types of Data Pipeline Solutions

A data pipeline is a set of actions that extracts data from numerous sources. It is a computerized process where the system takes columns from the database and merges them with other columns from this API. It also combines subset rows and corresponding values, alternates NAs with the median and loads them in this other database. This is known as a “job”, and pipelines are made of many jobs. Generally, the endpoint for a data pipeline is a data lake, such as Hadoop, S3, or a relational database. An ideal data pipeline should have the following properties:

Low Occurrence Inactivity: Data scientists should have accessibility to the data. Users should be able to raise a query to recover the recent event data in the pipeline. Usually, this happens in minutes or seconds of the event being directed to the data collection endpoint. 

Scalability: A data pipeline should be able to gauge billions of data points, and product sales. 

Collaborative Querying: A highly operational data pipeline should support both long-running batch queries and minor interactive queries that allow data scientists to discover tables and comprehend the data scheme.

Versioning: You should be able to edit and customize your data pipeline and event definitions without damaging the framework 

Monitoring: Data tracking and monitoring are important to check if the data is dispatched properly. In case of a failure, immediate alerts should be generated through tools such as PagerDuty.

Testing: You should be able to test your data pipeline with test events that do not end up in your data lake or database, but that do test components in the pipeline.

Do You want to Get AWS Certified? Learn about various AWS Certification in detail

Data Pipeline- Usage

Here are a few things you can do with Data Pipeline.

  • Convert received data to a common format.
  • Prepare data for investigation and imagining.
  • Travel between databases.
  • Share data processing logic across web apps, batch jobs, and APIs.
  • Power your data ingestion and integration tools.
  • Input large XML, CSV, and fixed-width files.
  • Substitute batch jobs with real-time data

Note that the Data Pipeline does not levy a specific structure on your data. All the data flowing through your pipelines can follow the same plan or an alternative NoSQL approach. The NoSQL feature offers a diverse structure to the data that can be altered at any point in your pipeline.

What are the Types of Data

Types of Data in AWS Data Pipeline

Data is typically defined with the following labels:

Raw Data: This is on processed data stored in the message encoding format which is used to send tracking events, such as JSON. 

Processed Data: Processed data is raw data that has been deciphered into event-specific formats, with an applied plan.

Cooked Data: Processed data that has been amassed or abridged is referred to as cooked data.

The Evolution of Data Pipelines

Over the past two decades the framework for accumulating and analyzing data been drastically changed. Earlier users would store data locally through log files, today we have modern systems that can trace data activity and use machine learning for real-time solutions. There are four different approaches to pipelines:

  • Flat File Era: Data is saved locally on game servers
  • Database Era: Data is staged in flat files and then loaded into a database
  • Data Lake Era: Data is stored in Hadoop/S3 and then loaded into a DB
  • Serverless Era: Managed services are used for storage and querying

Each of the steps supports the grouping of greater data sets. But it ultimately depends on the goal of the company to decide how the data is to be utilized and distributed.

Application of Data PipelinesApplication of Data Pipelines in AWS

Metadata: Data Pipeline lets users connect metadata to each separate record or field.

Data processing: Dataflows when processed and broken into smaller units, are easier to work with. It also quickens the process and saves on memory.

Adapting to Apps: Data Pipeline adjusts to your applications and services. It occupies a small footprint of less than 20 MB on disk and in RAM. 

Flexible Data Components: Data Pipeline comes with readers and writers integrated to stream the inflow or outflow of data. There are also stream operators for controlling this data flow.

Data Pipeline Technologies

Some examples of products used in building data pipelines. These tools are used by engineers to find competent results and enhance the system’s performance and reach; 

  • Data warehouses
  • ETL tools
  • Data Prep tools
  • Luigi: a workflow timetable that can be used to manage jobs and processes in Hadoop and similar systems.
  • Python / Java / Ruby: programming languages used to transcribe processes in many of these systems.
  • AWS Data Pipelines: another workflow management service that charts and implements data movement and processes
  • Kafka: a real-time streaming platform that allows you to move data between systems and applications, can also transform or react to these data streams.

Types of data pipeline solutions

The following list shows the most popular types of pipelines available:

Batch: Batch processing is most valuable of all as it lets you move huge volumes of data at a steady interval.

Real-time: These tools are improved to develop data in real time. 

Cloud native: These tools are optimized to work with cloud-based data, such as data from AWS buckets. These tools are hosted in the cloud, and are a cost effective and quick technique to enhance the infrastructure.

Open source: These tools are a cheaper alternative to a vendor. Open source tools are often inexpensive but require technical know-how on the part of the user. The platform is open for all to optimise and edit any way they want. 

AWS Data Pipeline

AWS Data Pipeline is a web service that supports dependable process and transfer data between a diverse range of AWS services, as well as on-premises data sources. With the AWS Data Pipeline, you can frequently keep in contact with the data and back where it’s deposited. Developers can also customize the data, convert and modify it at scale, and resourcefully allocate the results to other AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.

AWS Data Pipeline aids you in creating an intricate data processing network. It takes care of all the data monitoring, tracking and optimization tasks. AWS Data Pipeline also allows you to change the data that was previously protected in the on-site data storage facility.

Decoding Data Pipelines 

Let’s look into the process of assigning, transferring, altering and storing data via pipelines; 

Sources: First and foremost, we decide where we get the data from. Data can be accessed from different sources and in different formats. RDBMS, Application APIs, Hadoop, NoSQL, cloud sources, are a few primary sources. After the data is retrieved, it has to pass through the security controls and follow set protocols. next, the data schema and statistics are gathered about the source to simplify pipeline design.

List of common terms related to data science 

Joins: It is common for data to be shared from different sources as part of a data pipeline.

Extraction: Some separate data elements may be implanted in bigger fields. In some cases numerous values are clustered together. Or, distinct values may need to be removed- data pipelines allow all that. 

Standardization: Data needs to be consistent. It should follow a unit of measure, dates, attributes such as color or size, and codes related to industry standards.

Correction: Data, especially raw data can contain a lot of errors. Some common errors are- invalid fields that are not present or abbreviations that need to be extended. There may also be corrupt records that need to be detached or studied in an isolated process.

Loads: Once the data is ready, it needs to be loaded into a system for scrutiny. The endpoint is generally an RDBMS, a data warehouse, or Hadoop. Each destination has its own set of regulations and restrictions that need to be followed. 

Automation: Data pipelines are usually completed many times, and characteristically set on a schedule. This simplifies the error detection process and aids monitoring by sending regular reports to the system.

Moving Data Pipelines 

Many corporations have hundreds or thousands of data pipelines. Companies shape each pipeline with one or more technologies, and each pipeline might follow a different approach. Datasets often start with an establishment’s customer base. But there are cases where they will also initiate with their assumed departments within the organization itself. Thinking of data as events simplifies the process. Events are logged in, integrated and then transmuted across the pipeline. The data is then changed and altered to suit the systems that they are moved to. 

Moving data from place to place means that different end users can use it more methodically and accurately. Users can now access the data from one place rather than refer to multiple sources. Good data pipeline architecture will be able to provide justification for all sources of events. It would also have an explanation or reason to support the setups and schemes caring for these datasets. 

Event frameworks help you get hold of events from your applications a lot faster. This is achieved by making an event log that can then be processed for use.

Conclusion

A career in data science is a very profitable decision considering the revolutionary discoveries made in the field each day. We hope that this information was useful in helping the reader understand all about data pipelines and why they are important.

Joydip

Joydip Kumar

Solution Architect

Joydip is passionate about building cloud-based applications and has been providing solutions to various multinational clients. Being a java programmer and an AWS certified cloud architect, he loves to design, develop, and integrate solutions. Amidst his busy work schedule, Joydip loves to spend time on writing blogs and contributing to the opensource community.


Website : http://geeks18.com/

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What is AWS CLI and How to Install it?

Whether you are a small business or a big organisation, you must be familiar with Amazon Web Services. AWS is the world’s most widely used cloud computing platform – something that lets you move faster, operate more securely and save substantial costs. AWS offers 165 services spanning a wide range including computing, tools for the Internet of Things, developer tools, deployment, analytics, database, networking, etc. The AWS Command Line Interface (CLI) is a unified tool to manage your AWS services.Do you want to Get AWS Certified? Learn about various  AWS Certification in detailWhat is AWS CLI?The AWS Command Line Interface (CLI) is a unified tool that manages the AWS services for you. You only have to download and configure one simple tool to control a plethora of AWS services. They are automated through scripts and help you implement a certain level of automation. It is indeed AWS CLI that makes AWS so dynamic and easy to use.How to Install AWS CLI?Putting AWS CLI to use in your AWS involves a few steps.There are a few different ways to install it – you need to choose what works for your system.Every operating system has a different method of installation.Some generic steps are followed after the installation.Then comes the configuration.You will also need to upgrade it timely. Follow these steps to successfully install and configure the AWS CLI for use.Ways to Install:You can effectively install the AWS Command Line Interface (AWS CLI) using:pipa virtual environmenta bundled installerWhat do you need?Unix, macOS, Linux, WindowsPython 3 version 3.3+ or Python 2 version 2.6.5+It is important to know that you may not be able to use an older version of Python with all AWS Services. Update to a newer version if there are Insecure Platform Warning or deprecation notices. To find out what version you currently have, visit:  https://github.com/aws/aws-cli/blob/master/CHANGELOG.rst.1. Installing the AWS CLI Using pipPip is the main distribution method for the AWS CLI on macOS, Windows and Linux. It is a package manager for Python.Installing the current AWS CLI VersionIf you have pip and a supported version of Python, use the following command to install the AWS CLI. Use the pip3 command if you have Python version 3+ installed:$ pip3 install awscli --upgrade –userThe --upgrade option commands pip3 to upgrade the requirements that are already installed. The --user option commands pip3 to install the program to a subdirectory of the user directory. Doing this avoids the complication of modifying libraries used by your operating system.Upgrading to the latest versionUse the pip list -o command to identify packages that are "outdated”:$ aws --version aws-cli/1.16.170 Python/3.7.3 Linux/4.14.123-111.109.amzn2.x86_64 botocore/1.12.160 $ pip3 list -o Package Version Latest Type ---------- -------- -------- ----- awscli 1.16.170 1.16.198 wheel botocore 1.12.160 1.12.188 wheelNow, run pip install --upgrade to get the latest version:$ pip3 install --upgrade --user awscli Collecting aws cli Downloadinghttps://files.pythonhosted.org/packages/dc/70/b32e9534c32fe9331801449e1f7eacba6a1992c2e4af9c82ac9116661d3b/awscli-1.16.198-py2.py3-none-any.whl (1.7MB) |████████████████████████████████| 1.7MB 1.6MB/s Collecting botocore==1.12.188 (from awscli) Using cached https://files.pythonhosted.org/packages/10/cb/8dcfb3e035a419f228df7d3a0eea5d52b528bde7ca162f62f3096a930472/botocore-1.12.188-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: docutils>=0.10 in ./venv/lib/python3.7/site-packages (from awscli) (0.14) Requirement already satisfied, skipping upgrade: rsa=3.1.2 in ./venv/lib/python3.7/site-packages (from awscli) (3.4.2) Requirement already satisfied, skipping upgrade: colorama=0.2.5 in ./venv/lib/python3.7/site-packages (from awscli) (0.3.9) Requirement already satisfied, skipping upgrade: PyYAML=3.10; python_version != "2.6" in ./venv/lib/python3.7/site-packages (from awscli) (3.13) Requirement already satisfied, skipping upgrade: s3transfer=0.2.0 in ./venv/lib/python3.7/site-packages (from awscli) (0.2.0) Requirement already satisfied, skipping upgrade: jmespath=0.7.1 in ./venv/lib/python3.7/site-packages (from botocore==1.12.188->awscli) (0.9.4) Requirement already satisfied, skipping upgrade: urllib3=1.20; python_version >= "3.4" in ./venv/lib/python3.7/site-packages (from botocore==1.12.188->awscli) (1.24.3) Requirement already satisfied, skipping upgrade: python-dateutil=2.1; python_version >= "2.7" in ./venv/lib/python3.7/site-packages (from botocore==1.12.188->awscli) (2.8.0) Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in ./venv/lib/python3.7/site-packages (from rsa=3.1.2->awscli) (0.4.5) Requirement already satisfied, skipping upgrade: six>=1.5 in ./venv/lib/python3.7/site-packages (from python-dateutil=2.1; python_version >= "2.7"->botocore==1.12.188->awscli) (1.12.0) Installing collected packages: botocore, awscli Found existing installation: botocore 1.12.160 Uninstalling botocore-1.12.160: Successfully uninstalled botocore-1.12.160 Found existing installation: awscli 1.16.170 Uninstalling awscli-1.16.170: Successfully uninstalled awscli-1.16.170 Successfully installed awscli-1.16.198 botocore-1.12.1882. Installing the AWS CLI in a Virtual EnvironmentAnother option is to install the AWS CLI in a virtual environment to separate the tool and its dependencies. You can also use a different Python version for this purpose.3. Installing the AWS CLI Using an InstallerUse the bundled installer for automated and offline installation on Unix, macOS, and Linux. It includes the AWS CLI, its dependencies, and a shell script that is responsible for the installation. For Windows, try MSI installer. Both these methods simplify the initial installation. Installation of Linux or UnixBoth the platforms have identical installation process. You need to have Python’s latest version. We recommend using the bundled installer for this. The steps are as follows:1. To begin the installation:curl "https://s3.amazonaws.com/aws-cli/awscli-bundle.zip" -o "awscli-bundle.zip"2. Unzip the downloaded package:unzip awscli-bundle.zip3. Run the installation:sudo ./awscli-bundle/install -i /usr/local/aws -b /usr/local/bin/awsUsing the -b option allows you to use the AWS CLI from any directory.Installation on Amazon LinuxThe AWS Command Line Interface comes preinstalled on both Amazon Linux and Amazon Linux 2. Below are the steps to install:1. Identify currently installed version:$ aws --version aws-cli/1.16.116 Python/3.6.8 Linux/4.14.77-81.59.amzn2.x86_64 botocore/1.12.1062. Use pip3 to install the latest version of the AWS CLI. If you run the command from within a Python virtual environment (venv), then you don't need to use the --user option.$ pip3 install --upgrade --user awscli3. Add the install location to the beginning of the PATH variable.$ export PATH=/home/ec2-user/.local/bin:$PATH4. Verify that you're running new version with aws --version.$ aws --version aws-cli/1.16.116 Python/3.6.8 Linux/4.14.77-81.59.amzn2.x86_64 botocore/1.12.106Installation on WindowsThe AWS Command Line Interface can be installed on Windows by using a standalone installer or through a pip - a package manager for Python> Through InstallerDownload the appropriate MSI installer.Run the downloaded MSI installer or the setup file.Follow these instructions:By default, the CLI installs to C:\Program Files\Amazon\AWSCLI (64-bit version) or C:\Program Files (x86)\Amazon\AWSCLI (32-bit version). To confirm the installation, use the aws --version command at a command prompt (open the Start menu and search for cmd to start a command prompt).C:\> aws --version aws-cli/1.16.116 Python/3.6.8 Windows/10 botocore/1.12.106If Windows is unable to find the program, you might need to close and reopen the command prompt to refresh the path, or add the installation directory to your PATH environment variable manually.> Through Pip1. Open Start menu→Command Prompt2. Verify that both Python and pip are installed correctly:C:\> python --version Python 3.7.1 C:\> pip3 --version pip 18.1 from c:\program files\python37\lib\site-packages\pip (python 3.7)3. Install AWS CLI via pipC:\> pip3 install awscli4. Check if the installation went rightC:\> aws --version aws-cli/1.16.116 Python/3.6.8 Windows/10 botocore/1.12.106To upgrade to the latest version:C:\> pip3 install --user --upgrade awscliInstallation on MAC OS> Through Installer1. Download the  AWS CLI Bundled Installer$ curl "https://s3.amazonaws.com/aws-cli/awscli-bundle.zip" -o "awscli-bundle.zip"2. Unzip the package$ unzip awscli-bundle.zip3. Run the installation$ sudo ./awscli-bundle/install -i /usr/local/aws -b /usr/local/bin/awsThis command installs the AWS CLI to /usr/local/aws and creates the symlink aws in the /usr/local/bin directory. Using the -b option to create a symlink eliminates the need to specify the install directory in the user's $PATH variable. It enables users to run the AWS CLI by typing “aws” from any directory.If you want to see an explanation of the -i and -b options, use the -h option$ ./awscli-bundle/install -hthe commands summarized for easy cut and paste at the command line.curl "https://s3.amazonaws.com/aws-cli/awscli-bundle.zip" -o "awscli-bundle.zip" unzip awscli-bundle.zip sudo ./awscli-bundle/install -i /usr/local/aws -b /usr/local/bin/aws> Through PIP1. Download and install the latest version of Python from Python.org.2. Download and run the pip3 installation script provided by the Python Packaging Authority$ curl -O https://bootstrap.pypa.io/get-pip.py $ python3 get-pip.py --user3. Use pip3 to install the AWS CLI. We recommend using the pip3 command if you use Python version 3+$ pip3 install awscli --upgrade --user4. See if AWS CLI is installed correctly$ aws --version AWS CLI 1.16.116 (Python 3.6.8)To upgrade to the latest version, run the command:$ pip3 install awscli --upgrade --userInstallation on Ubuntu> Through APT Package Manager1. Update the package repository cache$ sudo apt-get update2. Install AWS CLI with the following command$ sudo apt-get install awsclipress y and then press to continue. Your screen should look something like this: 3. Now that it’s installed, check if it’s working properly or not$ aws --version> Through PIPAWS CLI being a Python module itself makes it easy for users who install it through PIP to update it on a regular basis. Assuming you have Python 3, follow the below steps to install:1. Install Python PIP with the following command$ sudo apt-get install python3-pipPress y and then press to continue2. Install AWS CLI using PIP with the following command$ pip3 install awscli --upgrade --user3. Run AWS CLI with the following command$ python3 -m awscli --versionAfter InstallationAfter you have successfully installed AWS CLI, you need to set the Path to Include the AWS CLI in your system.> LINUXFind out the folder in which pip installed the AWS CLI$ which aws /home/username/.local/bin/awsYou can reference this as “~/.local/bin/” because of the reason that “/home/username” corresponds to ~ in Linux OSIn case you don't know where Python is installed, run this command$ which python /usr/local/bin/pythonIf this is the same folder you added to the path while installing pip, there’s nothing else to be done. Otherwise, perform those same steps again, adding this additional folder to the path.> WINDOWSThe Windows System PATH tells your PC where it can find specific directories:C:\> where awsC:\Program Files\Amazon\AWSCLI\bin\aws.exeFind out where the aws program is installedC:\> where c:\ awsC:\Program Files\Python37\Scripts\awsIf the command returns the following error, then it is not in the system PATH and you can't run it by typing its name.C:\> where c:\ awsINFO:Could not find files for the given pattern.In that case, you need to add the path manually. First, you need to search where it is installed on your computer:C:\> where /R c:\ awsc:\Program Files\Amazon\AWSCLI\bin\aws.exec:\Program Files\Amazon\AWSCLI\bincompat\aws.cmdc:\Program Files\Amazon\AWSCLI\runtime\Scripts\awsc:\Program Files\Amazon\AWSCLI\runtime\Scripts\aws.cmd...To modify your PATH variable (Windows)Press the Windows key and enter environment variables.Choose the Edit environment variables for your account.Choose the PATH →Edit.Add the path to the Variable value field. For example: C:\new\pathClick OK twice to apply the new settings.Close any running command prompts and reopen the command prompt window.> MAC OSLocate Python$ which python /usr/local/bin/pythonThe output might be the path to a symlink, not the actual program. Run ls -al to see where it points.$ ls -al /usr/local/bin/python ~/Library/Python/3.7/bin/python3.6Pip install programs in the same folder as the Python application. Add this folder to your PATH variable.To modify the PATH variable for macOS (and Linus or Unix):1. Find the shell profile script in the user folder. In case you don’t know which shell you have, run echo $SHELL2. Through the following, add an export command to the profile scriptexport PATH=~/.local/bin:$PATHThis adds a path, ~/.local/bin in this example, to the current PATH variable.3. The updated profile can now be loaded into your current session$ source ~/.bash_profile
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Test Drive Your First Istio Deployment using Play with Kubernetes Platform- Cloud Computing

As a full stack Developer, if you have been spending a lot of time in developing apps recently, you already understand a whole new set of challenges related to Microservice architecture. Although there has been a shift from bloated monolithic apps to compact, focused Microservices for faster implementation and improved resiliency but the fact is  developers have to really worry about the challenges in integrating these services in distributed systems which includes accountability for service discovery, load balancing, registration, fault tolerance, monitoring, routing, compliance, and security.Let us understand the challenges faced by the developers and operators with the Microservice Architecture in details. Consider a 1st Generation simple Service Mesh scenario. As shown below, Service (A) communicates to Service (B). Instead of communicating directly, the request gets routed via Nginx. The Nginx finds a route in Consul (A service discovery tool) and automatically retries to form the connection on HTTP 502’s happen.                                                                    Figure: 1.0 – 1st Gen Service Mesh                                                      Figure:1.1 – Cascading Failure demonstrated with the increase in the number of servicesBut, with the advent of microservices architecture, the number is growing ever since. Below are the  listed challenges encountered by both developers as well as operations team:How to make these growing microservices communicate with each other?Enabling the load balancing architectures over these microservices.Providing role-based routing for the microservices.How to implement outgoing traffic on these microservices and test canary deployment?Managing complexity around these growing pieces of microservices.Implementation of fine-grained control for traffic behavior with rich-routing rules.Challenges in implementing Traffic encryption, service-to-service authentication, and strong identity assertions.In a nutshell, although you could enable service discovery and retry logic into application or networking middleware, the fact is that service discovery becomes tricky to make it right.Enter Istio’s Service Mesh“Service Mesh” is one of the hottest buzzwords of 2018. As the name suggests, it’s a configurable infrastructure layer for a microservices app. It lays out the network of microservices that make up applications and enables interactions between them. It makes communication between service instances flexible, reliable, and fast. The mesh provides service discovery, load balancing, encryption, authentication and authorization, support for the circuit breaker pattern, and other capabilities.Istio is completely an open source service mesh that layers transparently onto existing distributed applications. Istio v1.0 got announced last month and is ready for production. It is written completely in Go Language and its a fully grown platform which provides APIs that let it integrate into any logging platform, or telemetry or policy system. This project adds a very tiny overhead to your system. It is being hosted on GitHub. Istio’s diverse feature set lets you successfully, and efficiently, run a distributed microservice architecture, and provides a uniform way to secure, connect, and monitor microservices.Figure-1.2: Istio’s CapabilityThe Istio project adds a very tiny overhead to your system. It is being hosted on GitHub. Last month, Istio 1.0 release went public and ready for production environment.What benefits does Istio bring?Istio lets you connect, secure, control, and observe services.It helps to reduce the complexity of service deployments and eases the strain on your development teams.It provides developers and DevOps fine-grained visibility and control over traffic without requiring any changes to application code.It provides CIOs with the necessary tools needed to help enforce security and compliance requirements across the enterprise.It provides behavioral insights & operational control over the service mesh as a whole.Istio makes it easy to create a network of deployed services with automatic Load Balancing for HTTP, gRPC, Web Socket & TCP Traffic.It provides fine-grained control of traffic behavior with rich routing rules, retries, failovers, and fault injection.It enables a pluggable policy layer and configuration API supporting access controls, rate limits and quotas.Istio provides automatic metrics, logs, and traces for all traffic within a cluster, including cluster ingress and egress.It provides secure service-to-service communication in a cluster with strong identity-based authentication and authorization.If you want to deep-dive into Istio architecture, I highly recommend the official Istio website.It’s Demo Time !!!Under this blog post, I will showcase how Istio can be setup on Play with Kubernetes (PWK) Platform for a free of cost. In case you’re new, Play with Kubernetes rightly aka PWK is a labs site provided by Docker. It is a playground which allows users to run K8s clusters in a matter of seconds. It gives the experience of having a free CentOS LinuxVirtual Machine in the browser. Under the hood Docker-in-Docker (DinD) is used to give the effect of multiple VMs/PCs.Open  to access Kubernetes Playground.Click on the Login button to authenticate with Docker Hub or GitHub ID.Once you start the session, you will have your own lab environment.Adding First Kubernetes NodeClick on “Add New Instance” on the left to build your first Kubernetes Cluster node. It automatically names it as “node1”. Each instance has Docker Community Edition (CE) and Kubeadm already pre-installed. This node will be treated as the master node for our cluster.Bootstrapping the Master NodeYou can bootstrap the Kubernetes cluster by initializing the master (node1) node with the below script. Copy this script content into bootstrap.sh file and make it executable using “chmod +x bootstrap.sh” command.When you execute this script, as part of initialization, the kubeadm write several configuration files needed, setup RBAC and deployed Kubernetes control plane components (like kube-apiserver, kube-dns, kube-proxy, etcd, etc.). Control plane components are deployed as Docker containers.Copy the above kubeadm join token command and save it for the next step. This command will be used to join other nodes to your cluster.Adding Worker NodesClick on “Add New Node” to add a new worker node.Checking the Cluster StatusVerifying the running PodsInstalling Istio 1.0.0Istio is deployed in a separate Kubernetes namespace istio-system. We will verify it later. As of now, you can copy the below content in a file called install_istio.sh and save it. You can make it executable and run it to install Istio and related tools.You should be able to see screen flooding with the below output.As shown above, it will enable the Prometheus, ServiceGraph, Jaeger, Grafana, and Zipkin by default.Please note – While executing this script, it might end up with the below error message –unable to recognize "install/kubernetes/istio-demo.yaml": no matches for admissionregistration.k8s.io/, Kind=MutatingWebhookConfigurationThe error message is expected.As soon as the command gets executed completely, you should be able to see a long list of ports which gets displayed at the top center of the page.Verifying the ServicesExposing the ServicesTo expose Prometheus, Grafana & Servicegraph services, you will need to delete the existing services and then use NodePort instead of ClusterIP so as to access the service using the port displayed on the top of the instance page. (as shown below)You should be able to access Grafana page by clicking on “30004” port and Prometheus page by clicking on “30003”.You can check Prometheus metrics by selecting the necessary option as shown below:Under Grafana Page, you can add “Data Source” for Prometheus and ensure that the dashboard is up and running:Congratulations! You have installed Istio on Kubernetes cluster. Below listed services have been installed on K8s playground:Istio Controllers and related RBAC rulesIstio Custom Resource DefinitionsPrometheus and Grafana for MonitoringJeager for Distributed TracingIstio Sidecar Injector (we'll take a look next section)Installing IstioctlIstioctl is configuration command line utility of Istio. It helps to create, list, modify and delete configuration resources in the Istio system.Deploying the Sample BookInfo ApplicationNow Istio is installed and verified, you can deploy one of the sample applications provided with the installation- BookInfo. This is a simple mock bookstore application made up of four services that provide a web product page, book details, reviews (with several versions of the review service), and ratings - all managed using Istio.Deploying BookInfo ServicesDefining the Ingress Gateway:Verifying BookInfo ApplicationAccessing it via Web URLYou should now be able the BookInfo Sample as shown below:Hope, this Istio deployment Kubernetes tutorial helped you to successfully install Istio on Kubernetes. In the future blog post, I will deep dive into Istio Internal Architecture, traffic management, policies & telemetry in detail.We hoped this article helped you get familiar with the concept. If you want to know more about it and get certified, you can try the AWS certification course offered by KnowledgeHut.
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Test Drive Your First Istio Deployment using Play ...

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SSHing into Ubuntu EC2 instance post blocking port 22 with UFW - Cloud Computing

IntroductionThis blog is in reference to a troubleshooting situation in Amazon Web Services when you have configured firewall setting in your ubuntu ec2 or remote instance and is not able to login via PuTTY through SSH as the instance. Here, we will see how to insert SSH into the instance in a certain situation when you are logged out of that instance.During configuration of SSL security, we may accidentally or purposely block SSH for the instance to make the instance secure. But, what if we again want the same instance to SSH for certain changes.  Below is the highlighted configuration of the instance. Here, you can see that all the instances have all ports opened to everything.Here are the configuration changes which you have made on the login into instance:$ sudo apt-get update $ sudo apt-get install nginx $ sudo apt-get install ufw  Check UFW Status and Rules At any time, you can check the status of UFW with this command:$ sudo ufw status verbose By default, UFW will be disabled so you should see something like this:Output: Status: inactive If UFW is active, the output will say that it's active, and it will list the rules that are set. For example, if the firewall is set to allow SSH (port 22) connections from anywhere, the output might look something like this:Output: Status: active Logging: on (low) Default: deny (incoming), allow (outgoing), disabled (routed) New profiles: skip To                         Action      From --                         ------      ---- 22/tcp                     ALLOW IN Anywhere $ sudo ufw deny ssh $ sudo ufw status verbose Output: Status: active Logging: on (low) Default: deny (incoming), allow (outgoing), disabled (routed) New profiles: skip To                         Action      From --                         ------      ---- 22/tcp                     DENY    Anywhere If you kicked or logged out of the instance once the changes are done, you will be seeing the below results.On SSH into the instance with your Public DNS through PuTTY below are the results which you are seeing as an error i.e. Network error: Connection Timed Out  Below error shows that even after all ports were opened outside, the instance is not able to SSH because of firewall software of Ubuntu at the system level. Let’s see how to resolve this kind of system related issue.Solution to the issue:Step 1: Take an image of the EC2 instance by selecting the instance ->Image-Create ImageStep 2: Provide specification Image name, tick on no reboot and push the create image buttonStep 3: Then Select the image and click on launchStep 4: Go to instance type, select and click on NextStep 5: In the configuration instance, write the below commands under Advanced Details and click on next:#!/bin/bash sudo ufw allow ssh sudo ufw allow 22 sudo ufw allow 443 sudo ufw allow 8080 sudo ufw allow 80 sudo ufw status  sudo ufw enable Step 6: Click next and next tab and add security group similar as providedStep 7: Review and launch the instance and then try to SSH to the instance through PuTTY. You will be now able to add SSH inside the instance with this and you can terminate the old instance as the new instance with all the setup same as that of the old instance without any issue except the public IP and private IP change.Best Practices of Firewall Configuration & Port Blocking:Ensure that the Security Groups will allow a specific IP addresses which are within the VPN Range of the Environment.Use of NACL for allowing and blocking the IP addresses or subnets for a specific Port by using allow and deny rules. A network ACL contains a numbered list of rules that we evaluate in order, starting with the lowest numbered rule. This helps to determine whether traffic is allowed in or out of any subnet associated with the network ACL. The highest number that you can use for a rule is 32766. We recommend that you start by creating rules in increments (for example, increments of 10 or 100) so that you can insert new rules.Use of Bastion Host for accessing critical servers and environment is always a better option to increase the security of the system or environment. We hope that this article gave you a clear understanding of how AWS services work. You can understand how cloud computing works by making yourself familiar with these services. To learn more about AWS, you can check out the other blogs and the AWS certification course by KnowledgeHut.
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SSHing into Ubuntu EC2 instance post blocking port...

IntroductionThis blog is in reference to a trouble... Read More