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AWS Lambda Serverless Architecture Course I Certification

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What will you learn from this course?

Discover the basics of AWS Lambda, API Gateway, DynamoDB, and Step Functions (Full of Demos and Hands-On)

To expedite development and deployment, use the Serverless Framework with AWS SAM.

To automate serverless deployment, use AWS CI/CD technologies such as Code Commit, Code Build, and Code Pipeline.

What is lambda architecture?

The Lambda architecture uses batch as well as stream processing in order to handle the massive amount of data. This architecture is mainly used for data- processing as it can manage a big amount of data easily and efficiently. Through lambda architecture, you can get an accurate output of batch data and can stream it on a real-time basis to experience the view of live data. You can also combine two or more two outputs before displaying them. Lambda architecture is attached with massive growing data and its live analysis. Lambada requires a data model which has an immutable data source because it can act as a system of record.

A hybrid approach makes batch-processing and stream-processing approaches available via lambda architecture, which is a means of processing massive volumes of data also known as Big Data. The problem of calculating arbitrary functions is handled by using the Lambda architecture.

Organizations and companies use Lambda Architecture to gather a batch pipeline with a live stream pipeline to get access to the data. As data is growing at a fast rate, Lambda act as the best processing deployment strategy.

What is AWS Lambda?

AWS Lambda's serverless computing service is based on functions. It is used only in the computing layer of serverless apps. The purpose of AWS Lambda is to build event-driven apps that can be triggered by various AWS events.

AWS Lambda, a serverless, event-driven computing solution, allows you to run code for virtually any type of application or backend service without deploying or managing servers. Lambda can be triggered by over 200 AWS services and software-as-a-service (SaaS) apps, and you just pay for what you use. When many events occur at the same time, Lambda simply creates multiple copies of the function to handle them. Lambda is a sort of function as a service.

What is meant by serverless?

The phrase "serverless" often refers to serverless programs. Serverless applications do not necessitate the setup or administration of servers.

What are the applications of AWS Lambda Architecture?

Data processing and management

The processing of big data may be separated into two independent data pipelines. one where vast volumes of data are acquired from multiple sources and then stored in a dispersed form. This is then scrutinized in order to gain the precise viewpoint required for sound business judgments. A batch processing pipeline is another name for the approach utilized. To put it simply, data pipeline architecture collects and routes data to give analysis and business insight. The information is extracted, modified, and then entered into the database.

Real-time data streaming

Streaming Data Pipeline is another data processing approach utilized in Lambda architecture, which includes analyzing information while it is still in motion. Real-time data is used in this calculation. The framework that may be used for this is Apache Spark. Data is separated into smaller batches in Spark, which are then kept in memory, processed, and released. Because the computation is performed in memory, the approach utilized in this situation reduces latency.

What are the advantages of AWS Lambda Architecture?

Latency

Raw data is indexed at the serving layer so that end users may query and study all accessible historical data. Because batch indexing takes time, there is typically a large time frame of data that is temporarily unavailable to end users for study. Using stream processing methods, the speed layer decreases the temporal window of unanalyzable data by immediately indexing recent data that is currently not quarriable in the serving layers. This helps to reduce the intrinsic latency of the batch.

 Extensibility

The Lambda Architecture is built on distributed, scale-out solutions that may be extended by introducing new nodes, but it does not define which technologies should be used. This is possible in the data source, the batch layer, the servicing layer, and the speed layer. As a result, irrespective of the amount of data you need to handle, you can use the Lambda Architecture.

Tolerance for errors

The Lambda Architecture is based on distributed applications that have fault tolerance, so if a node fails, the load may be handled by other nodes. Because all data is kept in the batch layer, any indexing difficulties that develop in the serving or speed layers may be resolved by simply rerunning the indexing process at the batch/serving layers, enabling the speed layer to resume indexing the most current data.

Who can take this course?

Developers interested in mastering any level (basic to advanced) Serverless

Those who want to learn AWS Lambda and Serverless Architecture are developers and architects.

DevOps experts looking to create serverless methods for continuous integration and delivery

 

Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
Welcome guide document 10 Pages
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Welcome guide document 10 Pages
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