Tag: Amazon Web Services

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully-featured services from data centres globally.

More information is available on the Amazon Web Services website.

Categories
Data & Analytics

Using Python & AWS To Extract WordPress API Data

SharkPythonWordPressChest800600

In this post, I use popular Python modules & AWS managed serverless services to extract WordPress API data.

Table of Contents

Introduction

Last year, I tested my Python skills by analysing amazonwebshark’s MySQL database with Python. I could have done the same in 2024, but I wouldn’t have learned anything new and it felt a bit pointless. One of my YearCompass 2023-2024 goals is to build more, so I instead decided to create a data pipeline using popular Python modules & AWS services to extract my WordPress data using their API.

A data pipeline involves many aspects, which future posts will explore. This post focuses on extracting data from my WordPress database and storing it as flat files in AWS.

Firstly, I’ll discuss my architectural decisions for this part of the pipeline. Then I’ll examine the functions in my Python script that interact with AWS and perform data extraction. Finally, I’ll bring everything together and explain how it all works.

Architectural Decisions

In this section, I examine my architectural decisions and outline the pipeline’s processes.

Programming Language

My first decision concerned which programming language to use. I’m using Python here for several reasons:

  • I use Python at work and am always looking to refine my skills.
  • Several AWS services natively support Python.
  • Python SDKs like Boto3 and awswrangler support my use case.

Data Extraction

Next, I chose what data to extract from my WordPress MySQL database. I’m interested in the following tables, which are explained in greater detail in 2023’s Deep Dive post:

In November I migrated amazonwebshark to Hostinger, whose MySQL remote access policy requires an IP address. While this isn’t a problem locally, AWS is a different story. I’d either need an EC2 instance with a static IP, or a Lambda function with several networking components. These are time and money costs I’d prefer to avoid, so no calling the database.

Fortunately, WordPress has an API!

WordPress API

The WordPress REST API lets applications interact with WordPress sites by sending and receiving data as JSON objects. Public content like posts and comments are publicly accessible via the API, while private and password-protected content requires authentication.

While researching options, I stumbled across MiniOrange‘s Custom API for WordPress plugin. It has a simple interface and a good feature list:

Custom API for WordPress plugin allows you to create WordPress APIs / custom endpoints / REST APIs. You can Fetch / Modify / Create / Delete data with an easy-to-use graphical interface. You can also build custom APIs by writing custom SQL queries for your WP APIs.

https://plugins.miniorange.com/custom-api-for-wordpress

This meant I could start using it straight away!

The free plan lets users create as many endpoints as needed. But it also has a pretty vital limitation – API key authentication is only possible on their Premium plan. In the free plan, all endpoints are public!

Now let me be clear – this isn’t necessarily a problem. After all, the WordPress API is public! And my WordPress data doesn’t contain any PII or sensitive data. No – the risk I’m trying to address here isn’t a security one.

Public endpoints can be called by anyone or anything at any time. With WordPress, they have dedicated, optimised resources that auto-scale on demand. Whereas I have one Hostinger server that is doing every site process. Could it be DDoSed into oblivion by tons of API calls from bad actors? Do I want to find out?

As I’m using the plugin’s free tier here, I’ll mitigate my risks by:

  • Adding random strings to the endpoints to make them less guessable.
  • Not showing the endpoints in my script or this post.

So ok – how will I get the API endpoints then?

Parameters

Next, I need to decide how my script will get the endpoints to query and the S3 bucket name to store the results.

With previous scripts, I’ve used features like gitignore and dot sourcing to hide parameters I don’t want to expose. While this works, it isn’t ideal. Dot sourcing breaks if the file paths change, and even with gitignore any credentials are still hardcoded into the script locally.

A better approach is to use a process similar to a password manager, where an authenticated user or role can request and receive credentials using secure channels. AWS has two services for this requirement: AWS Secrets Manager and AWS Systems Manager Parameter Store.

Secrets Manager Vs Parameter Store

Secrets Manager is designed for managing and rotating sensitive information like database credentials, API keys, and other types of secrets. Conversely, Parameter Store is designed for storing configuration data, including plaintext or sensitive information, in a hierarchical structure.

I’m using Parameter Store here for two reasons:

Storage

Next, I need to decide where to store the API data. And I’m already using AWS for parameters, so I was always going to end up using S3. But what makes S3 an obvious fit here?

  • Integration: S3 is one of the oldest and most mature AWS services. It is well supported by both the Python SDK and other AWS services like EventBridge, Glue and Athena for processing and analysis.
  • Scalability: S3 will accept objects from a couple of bytes to terabytes in size (although if I’m generating terabytes of data here something is very wrong!). I can run my script at any time and as often as I want, and S3 will handle all the data it receives.
  • Cost: S3 won’t be entirely free here because I’ll be creating and accessing lots of data during testing. But even so, I expect it to cost me pence. I’m not keeping versions at this stage either, so my costs will only be for the current objects.

Much has been written about S3 over the years, so I’ll leave it at this.

Use Of Flat Files

Finally, let’s examine the decision to store flat files in the first place. The data is already in a database – why duplicate it?

Decoupling: Putting raw data into S3 at an early stage of the pipeline decouples the database at that point. Databases can become inaccessible, corrupted or restricted. The S3 data would be completely unaffected by these database issues, allowing the pipeline to persist with the available data.

Reduced Server Load: Storing data in S3 means the rest of the pipeline reads the S3 objects instead of the database tables. This reduces the Hostinger server’s load, letting it focus on transactional queries and site processes. S3 is almost serving as a read replica here.

Security: It is simpler for AWS services to access data stored in S3 than the same data stored on Hostinger’s server. AWS services accessing server data require MySQL credentials and a whitelisted IP. In contrast, AWS services accessing S3 data require…an IAM policy.

Architectural Diagram

This is an architectural diagram of the expected process:

  1. User triggers the Python function.
  2. Python interacts with AWS Python SDK.
  3. SDK calls Parameter Store for WordPress & S3 parameters. These are returned to Python via the SDK.
  4. Python calls WordPress API. WordPress API returns data.
  5. Python writes API data to S3 bucket via the SDK.

Setup & Config

I completed some local and cloud configurations before I started writing my Python script to extract WordPress API data. This section explores my laptop setup and AWS infrastructure.

Local Machine

I’m using Windows 10 and WSL version 2 to create a Linux environment with the Ubuntu 22.04.3 LTS distribution. I’m using Python 3.12, with a fresh Python virtual environment for installing my dependencies.

AWS Data Storage

I already have an S3 bucket for ingesting raw data, so that’s sorted. I made a wordpress-api prefix in that bucket to partition the uploaded data.

This bucket doesn’t have versioning enabled because it has a high object turnover. Versioning is unneeded and could get very expensive without a good lifecycle policy! While this would be simple to do, it’s a wasted effort at this point in the pipeline.

Another factor against versioning is that I can recreate S3 objects from the MySQL database. As objects are reproducible, there is no need for the delete protection offered by versioning.

AWS Parameters

I’m using Parameter Store to hold two parameters: my S3 bucket name and my WordPress API endpoints. Each of these uses a different parameter type.

The S3 bucket name is a simple string that uses the String Parameter Type. This is intended for blocks of text up to 4096 characters (4kb). The API endpoints are a collection of strings generated by the WordPress plugin. I use the StringList Parameter Type here, which is intended for comma-separated lists of values. This lets me store all the endpoints in a single parameter, optimising my code and reducing my AWS API calls.

Python Script

In this section, I examine the various parts of my Python script that will extract data from the WordPress API. This includes functions, methods and intended functionality.

Advisory

Before continuing I want to make something clear. This advisory is on my amazonwebshark artefacts GitHub repo, but it bears repeating here too:

Artefacts within this post have been created at a certain point in my learning journey. They do not represent best practices, may be poorly optimised or include unexpected bugs, and may become obsolete.

If I find better ways of doing these processes in future then I’ll link to or update posts where appropriate.

Logging

Firstly, I’ll sort out some logging.

The logging module is a core Python library, so I can import it without a pip install command. I then use logging‘s basicConfig function to set my desired parameters:

Python
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s]: %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S"
    )

level sets the logging level to start at. logging.INFO records information about events like authentications, conversions and confirmations.

format sets how the logs will appear in the console. Sections enclosed by % and ( )s are placeholders that will be formatted as strings. Other characters are printed as-is. Here, my logs will return as Date/Time [Log Level]: Log Message.

datefmt sets the date/time format for format‘s asctime using the same directives as time.strftime().

These settings will give me logs in the style of:

2024-01-11 09:44:39 [INFO]: Parameter found.
2024-01-11 09:44:39 [INFO]: API endpoints returned.
2024-01-11 09:44:39 [INFO]: Getting S3 parameter...
2024-01-11 09:44:39 [WARNING]: S3 parameter not found!

This lets me keep track of what stage Python is at when I extract WordPress API data.

boto3 Session

To call the AWS services I want to use, I need to create a boto3 session. This object represents a single connection to AWS, encapsulating options including the configuration settings and credentials. Without this, Python cannot access AWS Parameter Store, and so cannot extract WordPress API data.

To begin, I run pip install boto3 in the terminal. I then script the following:

Python
import logging
import boto3

session = boto3.Session()

This code snippet performs two new actions:

  • Imports the boto3 module
  • Instantiates an instance of the boto3 module’s Session class.

As Session has no arguments, it will use the first AWS credentials it finds. In AWS, these will be from the Lambda function’s IAM role. No problems there. But I have several AWS profiles on my laptop, and my default profile is for a different AWS account!

In response, I can set an AWS profile using VSCode’s launch.json debugging object. By adding "env": {"AWS_PROFILE": "{my_profile_name}"} to the end of the configurations list, I can specify which local AWS profile to use without altering the Python script itself:

JSON
{
	"version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Current File",
            "type": "python",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal",
            "justMyCode": true,
            "env": {"AWS_PROFILE": "profile"}
        }
    ]
}

Functions

This section examines my Python functions that extract WordPress API data. Each function has an embedded GitHub Gist and an explanation of the arguments and processes.

Get Parameters Function

Firstly, I need to get my parameter values from AWS Parameter Store.

Here, I define a get_parameter_from_ssm function that expects two arguments:

  • ssm_client: the boto3 client used to contact AWS.
  • parameter_name: the name of the required parameter.

I use type hints to annotate parameter_name and the returned object type as strings. For a great introduction to type hints, take a look at this short video from AWS Mad Lad Matheus Guimaraes:

I then create a try except block containing a response object which uses the ssm_client.get_parameter function to try getting the requested parameter. If this fails, the AWS error is logged and a blank string is returned. The parameter value is returned if successful.

I am capturing the AWS exceptions using the botocore module because it provides access to the underlying error information returned by AWS services. When an AWS service operation fails, it usually returns an error response that includes details about what went wrong. botocore can access these responses programmatically and log more exception details than the Python default.

I now have two additional changes to my main script:

Python
import logging
import boto3
import botocore

session = boto3.Session()
client_ssm = session.client('ssm')
  • botocore needs to be imported, so I add import botocore to the script. I don’t need to install botocore because it was installed with boto3.
  • I need a Simple Systems Manager (SSM) client to interact with AWS Systems Manager Parameter Store. I create an instance of the SSM client using my existing session and assign it to client_ssm. I can now use client_ssm throughout my script.

Get Filename Function

Next, I want to get each API endpoint’s filename. The filename has some important uses:

  • Logging processes without using the full endpoint.
  • Creating S3 objects.

A typical endpoint has the schema https://site/endpointname_12345/. There are two challenges here:

  • Extracting the name from the string.
  • Removing the name’s random characters.

I define a get_filename_from_endpoint function, which expects an endpoint argument with a string type hint and returns a new string.

Firstly, my name_full variable uses the rsplit method to capture the substring I need, using forward slashes as separators. This converts https://site/endpointname_12345/ to endpointname_12345.

Next, my name_full_last_underscore_index variable uses the rfind method to find the last occurrence of the underscore character in the name_full string.

Finally, my name_partial variable uses slicing to extract a substring from the beginning of the name_full string up to (but not including) the index specified by name_full_last_underscore_index. This converts endpointname_12345 to endpointname.

If the function is unable to return a string, an exception is logged and a blank string is returned instead.

No new imports are needed here. So let’s move on!

Call WordPress API Function

My next function queries a given API endpoint and handles the response.

Here, I define a get_wordpress_api_json function that expects three arguments:

  • requests_session
  • api_url: the WordPress API URL with a string type hint.
  • api_call_timeout: the number of seconds to wait for a response before timing out.

requests.Session is a part of the Requests library, and creates a session object that persists across multiple requests. I can now use the same session throughout the script instead of constantly creating new ones.

I open a try except block and create a response object. requests.Session attempts to call the API URL. If the response status code is 200 OK then the response is returned as a raw JSON dictionary.

This function can fail in three ways:

  • The status code isn’t 200. While this includes 3xx, 4xx and 5xx codes, it also includes the other 2xx codes. This was deliberate, as any 2xx responses other than 200 are still unusual, and something I want to know about.
  • The API call times out.
  • Requests throws an exception.

In all cases, the function raises an exception and doesn’t proceed. This was a conscious choice, as an API call failure represents a critical and unrecoverable problem with the WordPress API that should ring alarm bells.

As I’m using the Requests module now, I need to run pip install requests in the terminal and add import requests to my script. I then create my requests session in the same way as my boto3 session.

I’m also now using json – another pre-installed core Python module ready for import:

Python
import logging
import json
import requests
import boto3
import botocore

session = boto3.Session()
client_ssm = session.client('ssm')
requests_session = requests.Session()

S3 Upload Function

Finally, I need to put my JSON data into S3

I define a put_s3_object function that expects four arguments:

  • s3_client: the boto3 client used to contact AWS.
  • bucket: the S3 bucket to create the new object in
  • name: the name to use for the new object
  • json_data: the data to upload

I give string type hints to the bucket, name and json_data arguments. This is especially important for json_data because of what I plan to do with it.

I open a try except block and try to use put_s3_object to upload the JSON data to S3. In this context:

  • Body is the JSON data I want to store.
  • Bucket is the S3 bucket name from AWS Parameter Store.
  • Key is the S3 object key, using an f-string that includes the name from my get_filename_from_endpoint function.

The JSON data is created by my get_wordpress_api_json function, which returns that data as a dictionary. Passing a dictionary to put_s3_object‘s Body argument will throw a parameter validation error because its type is invalid for the Body parameter. json_data‘s string type hint will help prevent this scenario.

Moving on, the S3 client’s put_object function attempts to upload the data to the S3 bucket’s wordpress-api prefix as a new JSON object. If this operation succeeds, the function returns True. If it fails, a botocore exception is logged and the function returns False.

While no new imports are needed, I do now need an S3 client alongside the SSM one to allow S3 interactions:

Python
session = boto3.Session()
client_ssm = session.client('ssm')
client_s3 = session.client('s3')
requests_session = requests.Session()

Script Body

This section examines the body of my Python script. I look at the script’s flow, the objects passed to the functions and the responses to successful and failed processes.

Variables

In addition to the imports and sessions already listed, I have some additions:

  • The S3 bucket and WordPress API Parameter Store names.
  • An api_call_timeout value for the WordPress API requests in seconds.
  • Three endpoint counts used for monitoring failures, successes and overall progress.
Python
# Parameter Names
parametername_s3bucket = '/s3/lakehouse/name/raw'
parametername_wordpressapi = '/wordpress/amazonwebshark/api/mysqlendpoints'

# Counters
api_call_timeout = 30
endpoint_count_all = 0
endpoint_count_failure = 0
endpoint_count_success = 0

Getting The Parameters

The first part of the script’s body handles getting the AWS parameters.

Firstly, I pass my SSM client and WordPress API parameter name to my get_parameter_from_ssm function.

If successful, the function returns a comma-separated string of API endpoints. I transform this string into a list using .split(",") and assign the list to api_endpoints_list. Otherwise, an empty string is returned.

This empty string is unchanged by .split(",") and is assigned to api_endpoints_list. This is why get_parameter_from_ssm returns a blank string if it hits an exception. split(",") has no issues with a blank string, but throws attribute errors with returns like False and None.

I then check if api_endpoints_list contains anything using if not any(api_endpoints_list). return ends the script execution if the list contains no values, otherwise the number of endpoints is recorded.

A similar process happens with the S3 bucket parameter. My get_parameter_from_ssm function is called with the same SSM client and the S3 parameter name. This time a simple string is returned, so no splitting is needed. This string is assigned to s3_bucket, and if it’s found to be empty then return ends the current execution.

If both api_endpoints_list and s3_bucket pass their tests, the script moves on to the next section.

Getting The Data

The second part of the script’s body handles getting data from the API endpoints.

Firstly, I open a for loop for each endpoint in api_endpoints_list. I pass each endpoint to my get_filename_from_endpoint function to get the name to use for logging and object creation. This name is assigned to object_name.

object_name is then checked. If found to be empty, the loop skips that endpoint to prevent any useless API calls and to preserve the existing S3 data. The failure counter increments by 1, and continue ends the current iteration of the for loop.

Once the name is parsed, my Requests session, timeout values and current API endpoint are passed to the get_wordpress_api_json function. This function returns a JSON dictionary that I assign to api_json. api_json is then checked and, if empty, skipped from the loop using continue.

Next, I need to transform the api_json dictionary object before an S3 upload attempt. If I pass api_json to S3’s put_object as is, the Body parameter throws a ParamValidationError because it can’t accept dictionaries. I use the json.dumps function to transform api_json to a JSON-formatted string and assign it to api_json_string, which put_object‘s Body parameter can accept.

I can now pass my S3 client, S3 bucket name, object_name and api_json_string to my put_s3_object function. This function’s output is assigned to ok, which is then checked and updates the success or failure counter as appropriate.

Once all APIs are processed, the loop ends and the final success and failure totals are logged.

Adding A Handler

Finally, I encapsulate the script’s body into a lambda_handler function. Handlers let AWS Lambda run invoked functions, so I’ll need one when I deploy my script to the cloud.

Resources

The full Python script has been checked into the amazonwebshark GitHub repo, available via the button below. Included is a requirements.txt file for the Python libraries used to extract the WordPress API data.

GitHub-BannerSmall

Summary

In this post, I used popular Python modules & AWS managed serverless services to extract WordPress API data.

I took a lot away from this! The script was a good opportunity to practise my Python skills and try out unfamiliar features like type hints, continue and requests.Session. Additionally, I made several revisions to control flows, logging and error handling that were triggered by writing this post. The script is clearer and faster as a result.

With the script complete, my next step will be deploying it to AWS Lambda and automating its execution. So keep an eye out for that! If this post has been useful, the button below has links for contact, socials, projects and sessions:

SharkLinkButton 1

Thanks for reading ~~^~~

Categories
Architecture & Resilience

amazonwebshark’s Abandoned 2019 AWS Architecture

IndianaSharkCake800600

In this post, I respond to January 2024’s T-SQL Tuesday #170 Invitation by examining amazonwebshark’s abandoned 2019 AWS architecture.

tsql tuesday

Table of Contents

Introduction

amazonwebshark is two years old today!

One of a kind 500

I wrote an analysis post last year, and when deciding on the second birthday’s topic I saw this month’s T-SQL Tuesday invitation from Reitse Eskens:

“What projects did you abandon but learn a lot from?”

One immediately sprang to mind! Since this T-SQL Tuesday falls on amazonwebshark’s second birthday, it seemed a good time to evaluate it.

Rewind to 2019. I was new to AWS and was studying towards their Certified Cloud Practitioner certification. To that end, I set up an AWS account and tried several tutorials including an S3 static website.

After earning the certification, I kept the site going to continue my learning journey. I made the site into a blog and chose a snappy (Groan – Ed) name…amazonwebshark. In fact, that site is still around!

I’ll start by looking at the site architecture, then examine what went wrong and end with how it influenced the current amazonwebshark site. For the rest of this post, I’ll refer to amazonwebshark 2019 as awshark2019 and the current version as awshark2021.

How awshark2019 Was Built

In this section, I examine the architecture behind awshark2019.

Hugo Static Site Generator

Hugo is an open-source static site generator written in the Go programming language. Go is known for its efficiency and performance, making Hugo’s build process very fast.

Hugo’s content files are written in Markdown which enables easy post creation and formatting. These Markdown posts are then converted to static HTML files at build time. The built site has a file system structure and can be deployed to platforms like traditional web servers, content delivery networks (CDNs), and cloud storage services.

Speaking of which…

S3 Static Site

awshark2019 has been operating out of a public S3 bucket since its creation:

2024 01 04 S3WebsiteBucketOverview

This won’t be a particularly technical section, as the AWS documentation and tutorial are already great resources for this S3 feature. So let’s talk about the benefits of static sites instead:

  • Since static websites consist of pre-built HTML, CSS, and JavaScript files, they load quickly and can scale rapidly.
  • Static websites are inherently more secure and maintainable because there’s no server-side code execution, database vulnerabilities or plugin updates.
  • All site processing is done before deployment, so the only ongoing cost is for storage. awshark2019 weighs in at around 4MB, so in the four years it has been live this has been essentially free.

So far this all sounds good. What went wrong?

Why awshark2019 Failed

In this section, I examine awshark2019’s problems. Why was the 2019 architecture abandoned?

Unclear Objectives

Firstly, awshark2019 had no clear purpose.

In my experience, good blogs have their purpose nailed down. It could be automation, data, biscuits…anything as long as it becomes consistent and plays to the creator’s strengths.

With awshark2019, some posts are about S3 Static Sites and Billing Alerts. These are good topics to explore. However, almost half of the posts are about creating the site and are in a web design category. But the blog isn’t about web design, and I’ve never been a web designer!

Rounding things off, the About page is…the Hugo default. So who is the site for? If I, as the blog creator, don’t know that then what chance does anyone else have?

Poor Content

Secondly, as awshark2019’s objectives were unclear the content was…not very good. The topic choices are disjointed, some of the posts are accidental documentation rehashes and ultimately there’s little value.

Let’s take the example of Adding An Elastic IP To An Amazon Linux EC2 Instance. The post explores the basics, shows the AWS console changes and mentions costs. This is fine, but there’s not much else here. If I wrote this post today, I’d define a proper use case and explore the problem more by pinging the instance’s IP before and after a stoppage. This shows the problem instead of telling it.

Another post examines Setting Up A Second AWS Account With AWS Organizations. There’s more here than the IP address post, but there’s no context. What am I doing with the second account? Why does my use case support the use of AWS Organisations? What problems is it helping me solve?

There’s nothing in these posts that I can’t get from the AWS documentation and no new insights for readers.

Awkward To Publish

Finally, awshark2019 was too complex to publish. More accurately, Hugo’s deployment process wasn’t the problem. The way I was doing it was.

Hugo sites can be deployed in several ways. These centre around putting files and folders in a location accessible by the deployment service. So far so good.

But instead of automating this process, I had a horrible manual workflow of creating and testing the site locally, and then manually overwriting the existing S3 objects. This quickly got so tedious that I eventually ran out of enthusiasm.

What I Learned

In this section, I examine what I learned from the abandoned 2019 architecture when creating awshark2021.

Decide On Scope

My first key awshark2021 decision was the blog’s purpose.

While ‘Welcome To My Blog’ posts are something of a cliche, I took the time to write Introducing amazonwebshark as a standard to hold myself to:

By writing about my experiences I can check and confirm my understanding of new topics, give myself points of reference for future projects and exam revision, evidence my development where necessary and help myself out in the moments when my imposter syndrome sees an opportunity to strike.

Introducting amazonwebshark: What Is amazonwebshark For?

awshark2021 took as much admin away as possible, letting me explore topics and my curiosity instead. amazonwebshark was, and is, a place for me to:

  • Try things
  • Make mistakes
  • Improve myself
  • Be creative

While this is firstly a technology and cloud computing blog, I allow myself some freedom (for example the Me category) as long as the outcome is potentially useful. To this end, I’ve also written about life goals, problem-solving and public speaking.

Add Value

Secondly, let’s examine the posts themselves.

I probably average about eight hours of writing per post. I want to get the most out of that time investment, so I try to ensure my posts add value to their subject matter. There’s no set process for this, as value can take many forms like:

  • Examining how I apply services to my situation or use case.
  • Raising awareness of topics with low coverage.
  • Detailing surprising or unexpected event handling.

My attitude has always been that I’m not here to tell people how and why to do things. I’m here to tell people how and why I did things. Through this process, I can potentially help others in the technology community while also helping myself.

Post introspection and feedback have led to improvements in my working practises like:

It could be argued that amazonwebshark is a big ongoing peer review. It’s made me a better engineer and has hopefully helped others out too.

Keep It Simple

Finally, let’s discuss architecture.

awshark2021 is a WordPress blog, currently hosted on Hostinger servers. While this architecture isn’t free and has tradeoffs, it offers a fast, reliable deployment path managed by organisations specialising in this field.

This is exactly what I wanted for awshark2021:

…my main focus was to get the ball rolling and get something online. I’ve wanted to start a blog for some time, but have run into problems like knowledge gaps, time pressures and running out of enthusiasm.

Introducing amazonwebshark: Why Didn’t You Use AWS For Hosting?

I enjoy writing, so my priority is there. If I begin seriously considering a serverless amazonwebshark, one of the core tests will be the deployment process. For now, the managed services I’m paying for meet my needs and let me focus on creativity over admin.

Summary

In this post, I responded to January 2024’s T-SQL Tuesday #170 Invitation by examining amazonwebshark’s abandoned 2019 AWS architecture.

It’s unfair to blame the architecture. Rather, my implementation of it was at fault. awshark2019 was a good idea but suffered from poor and over-ambitious architectural decisions. I’ve considered deleting it. But if nothing else it reminds me of a few things:

  • I won’t always get it right first time.
  • It doesn’t have to be perfect.
  • Enjoy the process.

awshark2019’s lessons have allowed awshark2021 to reach two years. Happy birthday!

If this post has been useful, the button below has links for contact, socials, projects and sessions:

SharkLinkButton 1

Thanks for reading ~~^~~

Categories
Me

Attitudes Towards Certification

SharkHoodyStudy800600

In this post, I examine my attitudes toward certification and how I use my certifications after earning them.

Table of Contents

Introduction

In October 2023, I renewed my AWS SysOps Administrator Associate certification. I was going to write about how I did it, but I used similar resources to those in my Developer Associate post from March 2022.

So I’m writing a different post instead. Many people examine how they earn cloud certifications, but few explore the reasons why. Here, I’ll share my attitudes and motivations toward earning and renewing a cloud certification, and how I apply my new knowledge after the fact.

Self Validation

For me, the biggest certification benefit is the confidence it gives me that I know what I’m talking about.

I’ve mentioned previously that I have anxiety and imposter syndrome issues. One way I approach that is to keep my technical knowledge current and relevant. This goes beyond the cloud. I have DataCamp and A Cloud Guru subscriptions for honing other areas, and various newsletters and feeds for news and opinions.

Certifications let me distil my knowledge into the recognised and understood forms of certification badges. These badges in turn give me the piece of mind that I can validate and evidence my cloud knowledge.

This doesn’t just benefit me…

Knowledge Validation

Having active cloud certifications gives my employer confidence that my cloud knowledge is accurate and reliable.

My employer uses many AWS services across various teams. My AWS certifications have given me T-shaped knowledge that helps me contribute to the success of both Data Engineering and the other tech teams. This knowledge lets me:

Troubleshoot Problems

Lambda function throwing unexpected exceptions? Slow running Athena query? Unreachable EC2 instance? I’ve solved all these problems using certification knowledge applied to real-world use.

For the record, the Lambda’s IAM role was missing actions, the S3 objects needed different partitioning and the instance was missing a security group.

Collaborate Across Teams

Whether preparing for failovers, replacing legacy packages with cloud processes or building APIs, I can work with my non-data colleagues by understanding the AWS services they use and what their outputs tell me.

For example, I don’t directly use AWS services like Systems Manager Patch Manager and API Gateway in my role. But I understand what those services are, what their outputs mean and how they relate to the services I do directly use.

Architect Agile Solutions

When faced with a new requirement or unexpected bug, I can call on my certification knowledge for optimal and appropriate solutions. Should those solutions turn out to be ineffective or undesirable, I can pivot or re-architect accordingly.

For example, when recently repartitioning an S3 bucket I approached the task using Python and boto3. During testing, it became clear this approach would take days to complete.

So I used my AWS CLI SysOps knowledge and refactored the Python code to script out the S3 CLI operations for each object. Then I completed the entire repartition in about two hours using Bash.

Same task. Same result. Different solutions.

Wider View

Studying for and passing certifications exposes me to cloud services I’ve never used, or don’t use often.

AWS constantly changes. Most weeks see new services, features or improvements. Keeping abreast of over two hundred services is difficult, and useful info will inevitably slip through the cracks.

Cloud certifications give me a wider view of the cloud landscape. While I’m primarily a data professional, knowing about services outside that scope improves my diversity and value. It also helps me manage my own cloud accounts. While I’m not responsible for my employer’s security or networking, I am responsible for mine!

Some recent useful discoveries from my SysOps renewal include:

EventBridge Pipes

From Amazon EventBridge’s Product page:

Amazon EventBridge Pipes helps create point-to-point integrations between event producers and consumers with optional transform, filter and enrich steps. EventBridge Pipes reduces the amount of integration code needed to write and maintain when building event-driven applications.

https://aws.amazon.com/eventbridge/pipes/

For me, EventBridge Pipes feels like ETL for events. It sits between event sources and event targets, removing unneeded data and transforming what’s left. As event-driven architectures become increasingly common, EventBridge Pipes have great efficiency and cost-saving potential.

IAM NotAction

I thought this was a misprint until I checked the docs! Where Action matches the action(s) that will be allowed or denied, NotAction matches everything except the specified list of actions.

So, where this policy allows the deletion of any S3 bucket in an account:

"Effect": "Allow",
"Action": "s3:DeleteBucket",
"Resource": "arn:aws:s3:::*"

This policy allows all S3 actions except deleting an S3 bucket in an account:

"Effect": "Allow",
"NotAction": "s3:DeleteBucket",
"Resource": "arn:aws:s3:::*"

S3 Access Points

From Amazon S3’s Product page:

Amazon S3 Access Points simplify data access for any AWS service or customer application that stores data in S3. With S3 Access Points, customers can create unique access control policies for each access point to easily control access to shared datasets.

Customers with shared datasets including data lakes, media archives, and user-generated content can easily scale access for hundreds of applications by creating individualized access points with names and permissions customized for each application.

https://aws.amazon.com/s3/features/access-points/

S3 Access Points look like they can take lots of pain out of bucket policies and IAM config. This would be a big help with sharing datasets, controlling object access and setting environment variables.

Summary

In this post, I examine my attitudes toward certification and how I use my certifications after earning them.

Certifications aren’t for everyone, and that’s fine. As regards my attitudes toward certification, they’re great at improving my confidence, expanding my horizons and making me a better, more complete engineer.

AWS have a range of skill builders, workshops and whitepapers to help people on their certification journey. Check them out! If this post has been useful, the button below has links for contact, socials, projects and sessions:

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Thanks for reading ~~^~~