Monday, June 10, 2024

RedShift — How to Import CSV/JSON Files into RedShift Serverless

In this tutorial, you will learn "How to Import CSV/JSON Files into RedShift Serverless with an Easy to Go Example" in Amazon Web Services.

Amazon Redshift Serverless provides a flexible, scalable, and cost-effective solution for data analytics, making it easier for organizations to leverage the power of Redshift without the overhead of managing infrastructure.

Source to AWS Official documentation is here: In this video I will use the "Author from scratch" option to demonstrate how AWS Lambda is working by passing a string argument to the function and returning a specified output based on the input value. It is like a Hello World example. But if you are learning AWS, this could be a good start. Finally, you will learn how to test your Lambda function by simulating various scenarios by changing input parameters. Enjoy! 🚀AWS Lambda is a serverless computing solution offered by Amazon Web Services. It enables you to run code without setting up or handling servers, helping you to focus only on application logic rather than infrastructure management. It lets you run code without thinking about servers. 🔍Serverless Computing: AWS Lambda uses the serverless computing model, which means you only pay for the compute time you need and there are no charges while your code is not running. This makes it extremely cost-effective, particularly in applications with irregular or unexpected workloads. 🔍Event-Driven Architecture: Lambda functions are triggered by events such as data changes in Amazon S3 buckets, Amazon DynamoDB table updates, HTTP requests through Amazon API Gateway, or custom events from other AWS or third-party services. This event-driven architecture enables you to create responsive, scalable apps. 🔍 Support for Multiple Programming Languages: Lambda supports several programming languages, including Node.js, Python, Java, Go, Ruby, and .NET Core. You can write your Lambda functions in the language of your choice, making it flexible for developers with different skill sets. 🔍Auto Scaling: AWS Lambda automatically adjusts your functions based on incoming traffic. It can handle thousands of requests per second and does not require manual scaling configurations. Lambda scales resources transparently, ensuring that your functions are highly accessible and responsive. 🔍Integration with AWS Ecosystem: Lambda seamlessly connects with other AWS services, allowing you to construct sophisticated and efficient processes. For example, you may design serverless applications that process data from Amazon S3, generate notifications via Amazon SNS, and store results in Amazon DynamoDB—all without maintaining servers or infrastructure. 🔍Customization and Control: While Lambda abstracts away server management, it still allows you to customize your runtime environment, define memory and timeout settings, and configure environment variables. This lets you fine-tune your functionalities to satisfy specific needs. 🔍You pay only for the compute time that you consume — there is no charge when your code is not running. With Lambda, you can run code for virtually any type of application or backend service, all with zero administration. 🔍Lambda responds to events : Once you create Lambda functions, you can configure them to respond to events from a variety of sources. Try sending a mobile notification, streaming data to Lambda, or placing a photo in an S3 bucket. 🔍 AWS Lambda streamlines the process of developing and deploying applications by automating infrastructure management responsibilities, allowing developers to concentrate on creating code and providing business value. ⭐To learn more, please follow us - ⭐To Learn more, please visit our YouTube channel at - ⭐To Learn more, please visit our Instagram account at - ⭐To Learn more, please visit our twitter account at - ⭐To Learn more, please visit our Medium account at -

Wednesday, June 5, 2024

DataBricks — Returning Customers within 7 days in PySpark Dataframe

PySpark is a Python API for Apache Spark, whereas Apache Spark is an Analytical Processing Engine for large scale sophisticated distributed data processing and machine learning applications.

If you are working as a PySpark developer, data engineer, data analyst, or data scientist for any organization then it requires you to be familiar with dataframes because data manipulation is the act of transforming, cleansing, and organising raw data into a format that can be used for analysis and decision making.