Serverless. Analytics. No servers needed. Such a cool concept, isn’t it? Serverless Analytics in AWS is used for storing and querying data in AWS without writing any code for the back-end infrastructure. And you don’t need a single server to do that. If you are reading this article, chances are you are interested in implementing analytics for your business. We will explore how to set up serverless analytics using AWS Lambda functions on your web application hosted on Amazon Web Services (AWS).
What Is Serverless Analytics In AWS?
Serverless analytics is a new way to look at data. It’s not just a different way to store or process data; it’s an entirely new approach to using and thinking about data.
The term serverless was first coined by Amazon Web Services (AWS) in 2014 when it introduced Lambda, its first serverless computing platform. Serverless architecture is designed for scalability, flexibility, agility, and cost-efficiency. The main benefit of this type of architecture is that it eliminates the need to manage infrastructure and pay for idle resources. Instead, you simply pay for what you use — automatically!
How Does it Work?
Serverless Analytics uses the cloud to analyze data and deliver insights. It typically involves an event-driven architecture that generates events by applications, web servers, or devices. These events are then captured in a data store such as Amazon Kinesis Streams or DynamoDB, allowing you to store and query your data.
By using a managed service such as Amazon EMR or AWS Glue, you can run batch jobs on your data that process the information into something useful. You might use these batch jobs to generate reports or create aggregated statistics to get insights into your business’ performance.
With serverless analytics, you don’t have to worry about installing and maintaining custom software or servers as everything is managed by AWS Lambda and other services like Amazon S3 and Amazon DynamoDB.
What is AWS Lambda?
AWS Lambda is a managed service for running code in response to events and automatically managing the compute resources for you.
This service runs your code in the cloud, so you don’t have to think about servers and operating systems. You can use AWS Lambda to build event-driven applications that respond quickly to new information.
AWS Lambda empowers you to run code without managing servers. You can set up your code to be triggered by other AWS services or call it directly from your application using the API or AWS SDKs. You pay only for the compute time you consume – there is no charge when your code is not running (though there may be other costs, such as data transfer).
The Benefits of Serverless Analytics
Serverless analytics is a relatively new field that offers several benefits. We have pointed out some of the key benefits of serverless analytics:
- Low cost:Serverless analytics is free or close to it. You don’t have to pay for servers or maintenance. You pay only when your code runs in the cloud, and you can even use a free tier for small projects.
- Lightweight code:The platform handles all of the scaling and load balancing for you so that you can concentrate on writing code rather than managing infrastructure.
- Flexibility: You’re not locked into any specific programming language or framework because there is no provisioning involved in getting started with serverless analytics — just write your code and deploy it!
- Simplified infrastructure management: You don’t need dedicated teams to manage your infrastructure — all that is taken care of by a cloud provider (AWS). This saves time, money, and resources for businesses that want to focus on their core competencies rather than spending time managing an entire stack of technology. With serverless analytics, you can simply set up your code and let it run without worrying about managing the underlying infrastructure.
- Ease of use: Many companies want access to powerful tools like machine learning and artificial intelligence but don’t have the technical know-how or resources to implement them successfully on their own. Serverless analytics provides an easy way for non-technical users — such as business analysts and project managers — to access these capabilities by providing simple interfaces that make it possible for anyone to build models and run analysis without needing any prior knowledge of machine learning techniques or programming languages such as Python or Scala.
How to Implement Serverless Analytics with Amazon Lambda?
Step 1: Create an AWS Lambda function
To create a serverless analytics application, you need to create a Lambda function to handle the processing. The process is simple: You just have to choose the type of Lambda function (Java or Python), define your code, and upload it.
Step 2: Configure your Lambda function
This step will configure your Lambda function by providing a name, description, and triggers for when it should run. You will also add an event source and configure credentials for AWS Lambda to access your data store.
Step 3: Set up a data store for your application
You can use any data store that supports the AWS Lambda API. We recommend Amazon DynamoDB because it allows you to scale capacity and performance as needed quickly. To use DynamoDB, you need to create an account on the AWS Management Console and ensure that you have enough storage space to keep your data.
Step 4: Deploy your code
Once everything is configured correctly, it’s time to deploy the Lambda function. You can deploy it by creating an AWS CloudFormation template. This will create all the resources needed to run the Lambda function on AWS Lambda. Before deploying your application, you need to ensure that all dependencies are installed correctly.
Turbocharge your Analytics Initiatives with Serverless Analytics
With serverless analytics in AWS, you can unload data without worrying about operating and maintaining infrastructure.
AWS currently holds 41.5% of the cloud computing market, more than any other company combined – Microsoft Azure (29.4%), Google Cloud (3.0%), and IBM (2.6%). One of the many reasons behind its popularity is its self-serve serverless model reduces development time and minimizes operational complexity, letting analysts get their data faster and with greater agility. With the most crucial data readily available through a wide range of query and visualization tools, users can make better business decisions based on timely insight. Ultimately, that’s something all companies crave – and serverless analytics makes it possible.
If the concept of serverless analytics sounds exciting to you and want get started with it, connect the experts at Ascentt 26to start right away.