At AWS re:Invent 2016, AWS announced Amazon Lightsail, Amazon’s Virtual Private Server solution. Lightsail is targeted at the VPS market, with a simpler, albeit less flexible solution compared to EC2. But with Lightsail, users can get up-and-running quickly with a Linux server with many pre-bundled application packages, such as WorkPress, a LAMP stack, or Redmine.
Like many AWS solutions, Lightsail provides a way to create snapshots of your servers. However, the process of creating Lightsail instance snapshots is a manual one: you must create snapshots using the web front-end, or the AWS CLI.
Today, we are announcing the first of our Lightsail actions: Create Lightsail Instance Snapshots.
Amazon Athena is an interactive query service where you can query your data in Amazon S3 using standard SQL statements. Amazon Athena only reads your data, it will not add to or modify it. So you can think of it as only being able to execute SELECT statements.
Today, we’re going to take closer look at Amazon Athena pricing and how you can reduce your Athena costs.
Amazon Athena pricing is based on the bytes scanned. Anything you can do to reduce the amount of data that’s being scanned will help reduce your Amazon Athena query costs.
In our previous article, Partitioning Your Data With Amazon Athena, we partitioned our data into folders to reduce the amount of data scanned. But those partitions were being loaded into our Athena table manually.
In this article, we will show how to load the partitions automatically.
Amazon Athena pricing is based on the bytes scanned. Anything you can do to reduce the amount of data that’s being scanned will help reduce your Amazon Athena query costs.
In our previous article, Getting Started with Amazon Athena, JSON Edition, we stored JSON data in Amazon S3, then used Athena to query that data.
In this article, we will partition the data, and compare the results.
Amazon Athena pricing is based on the bytes scanned. That is a little ambiguous. To clarify, it’s based on the bytes read from S3. It’s not based on the bytes loaded into Athena.
So, you can reduce the costs of your Athena queries by storing your data in Amazon S3 in a compressed format.
In our previous article, Getting Started with Amazon Athena, JSON Edition, we stored JSON data in Amazon S3, then used Athena to query that data.
In this article, we will compress the JSON data, and compare the results.
At AWS re:Invent 2016, Amazon announced Amazon Athena, a query service allowing you to execute SQL queries on your data stored in Amazon S3. Athena can query against CSV files, JSON data, or row data parsed by regular expressions. Using Amazon Athena, you don’t need to extract and load your data into a database to perform queries against your data.
Amazon Athena is not a full CRUD database system. It can only query data. So you basically can only perform SELECT queries on your data in S3. You cannot execute any INSERT, UPDATE, or DELETE queries.
We wanted to do some experimenting with Athena to see what it can do.
Whether you are a small AWS user or a large enterprise, security and auditing should be front-of-mind when it comes to your AWS account.
AWS CloudTrail provides a way to record all AWS API calls. Logs are saved to an Amazon S3 bucket for you. You can read and process the logs yourself, or you can use one of the many third-party tools and services available to you.
Today, we’re happy to announce a new action available to you: Create CloudTrail Trails.
This week is AWS re:Invent. It’s bigger than ever, spanning 3 hotels: The Venetian, The Mirange, and The Wynn & Encore. So make sure you have some good walking shoes.
As you’re walking around AWS re:Invent this year, you’ll notice different coloured lanyards holding everyone’s badges. Ever wondered what the different colours mean? Here’s the list.
AWS Directory Service is a managed Microsoft Active Directory solution. Using this service, you can manage the identities for your organization, and by joining EC2 instances to your directory domain, authorize access to your AWS resources.
AWS Elastic Beanstalk is a deployment service for your web applications. By configuring your Elastic Beanstalk environment, AWS can manage the scaling and high-availability for your web application. Supported environments include NodeJS, Ruby, and .NET, among others.
If you have an existing .NET application, or are building one that utilizes “Windows Authentication”, you can deploy your application using Elastic Beanstalk, and use Directory Service as your Windows authentication directory.
Ideally, we would implement this combination using off-the-shelf Elastic Beanstalk AMI images. However, due to an issue with Elastic Beanstalk hostnames being re-used, we’ll need to create a custom AMI image to be used by our Elastic Beanstalk applications.
The custom AMI will only address the hostname issue. This way, once AWS fixes it on their end, the custom AMI can be done away with.
We have employed these techniques in our own internal applications and we wanted to share them with anyone else who wanted to do the same.
This article will walk you through these steps:
Today, we are excited to unveil Action Exclusions. Action Exclusions are a way to exclude certain AWS resources from being acted upon by Skeddly actions. This allows you to take a subset of your AWS resources out of Skeddly control for a short or long period of time.
For example, you can exclude an EC2 instance from being stopped by Skeddly for the next 12 hours.