The marketing sector makes use of the data collected from different phases of the customer journey. As they analyze the data, they are known to establish the metrics and create certain actionable insights that are used to invest in the customers, thereby generating revenue.
The developer and data scientist in the marketing sector makes use of different containers for various services such as data preparation, data collection, statistical analysis performance, and machine learning model development. As marketing data collection is increasing faster, you need a specific solution to handle the costs, scale, and total count of the necessary data analytics integrations.
From this write-up, you can seek information about a solution that scales and performs with dynamic traffic. Thus, it is cost-optimized for specific on-demand consumption. It makes the right use of the synchronous container-based data science apps, which are deployed with different asynchronous container-based architectures on AWS Lambda. Thus, the serverless architecture is known to automate different serverless data analytics workflows, which make use of different event-based prompts.
What is serverless computing?
Serverless cloud computing allows server management and self-service provisioning. In the era of big data, cost management and dynamic scaling are recognized as crucial factors behind the success of analytics platforms. Thus, the specific server architecture performs as several cloud platforms like Microsoft Azure, AWS, and different open source technologies release different services, in which the code execution scales downwards and upwards, catering to the needs.
The primary benefit of serverless cloud computing is that the developer does not need to worry about different servers. So, it is essential for the developers to concentrate on the code. The 3rd party services handle the infra design in which the code executes on the containers through Function as a Service. Hence, they communicate with the backend as a service to accomplish the data storage needs.
Serverless for Big Data
As the platforms manage the workloads, you do not need the additional team to handle the Spark and Hadoop clusters. Now, we will talk about the different benefits of choosing data analytics on aws:
You do not need to worry about the infrastructure management.
As you work on different ETL and analytical platforms, you will come across the guys setting up Hadoop clusters and Spark. The developers make use of Kube Clusters, thereby releasing them on different containers. To track and scale different resources, cost optimization needs an ample amount of resources and efficacy. Hence, serverless data engineering helps make the life of the manager and developer free from hassles, as you do not need to be stressed out about infrastructure.
Affordability indicates that you need to pay for the code execution time only. It indicates that, as the deployed function is idle and the client is not using it, you do not need to carry the hassle of paying for the infrastructural costs. Thus, you do not need to pay the cloud platform for the insurance on an hourly basis. Henceforth, the cloud service is going to charge for the specific execution time only. For instance, you will have different microservices, endpoints, and APIs that are used less frequently. For the specific case type, you will be charged as the APIs are being called.
Scaling on demand
The platforms track the deployed code resource usage and scaling upwards and downwards according to the uses. Thus, the developer needs to be stressed out about scalability.
Fault tolerance and ready-made availability
The primary architecture service providers offer ready-made high availability, which indicates that the deployed app will not be down. It is similar, as we are using Nginx for the app and having different servers deployed.
Serverless real-time data analytics
Now, we will discuss the ways to set up real-time data analytics:
Data collection layer
Data sources such as IoT analytics and Twitter streaming are known to continuously push data into real-time analytical platforms. Henceforth, the primary task on such platforms is the creation of the unified data collection layer, in which all such data sources are defined and written to the data stream in real time, which is processed by the data processing engines. Thus, it is possible to make the right use of AWS Cloud DataFlow for the AWS platforms, Amazon Nifi for the open source platforms to define different streaming sources, Azure DataFactory for different Azure platforms, and social media streaming, which loads the data from different Twitter streaming endpoints, thereby writing the same to different real-time streams.
Data processing layer
It is the layer that includes data preprocessing such as data preparation, data validation, data cleaning, and data transformation. In the specific layer, you can execute the analytics in real time on the streaming data through Windows. While performing it on the real-time stream, you need the data processing platform, which has the ability to process the data with constant throughput and write the data to the data serving layer.
Such a layer has the responsibility to serve the results, which are created by the data processing layer, to potential users. Hence, the specific layer is dynamically scalable as they need to serve a bunch of potential users for the visualization in real time. There are primarily two different kinds of the Serving Layer which include:
You can make use of DynamoDB NOSQL Datastore as the serving layer. You can create the Rest API, and the Dashboard utilizes the REST API to view the results in real time. You can utilize Google Cloud Datastore and Azure Cosmos DB for it.
Speaking of AWS, you can opt for DynamoDB streams as the serving layer. Here, the Data Processing layer writes the results, after which the WebSocket Server will consume the results from DynamoDb. Thus, the WebSocket-based dashboard clients will view the data in real time.