AWS is a powerful tool and a great application is to use it to create data pipeline to collect, process, and analyze large amounts of data in real-time. Using a combination of AWS services, we can create a data pipeline that can handle a number of use cases, including data analytics, real-time processing of IoT data, and logging and monitoring.
The key components to build a data pipeline are:
- Amazon Kinesis
- Amazon Glue
- Amazon S3
Developing a data pipeline on AWS might seem complex, but through this blog I aim to help you understand it and help you build a data pipeline on your own.
Let's look at a guided step-by-step process of creating an AWS data pipeline.
Amazon Kinesis
Amazon Kinesis enables to create a stream of data that can be read and processed in real-time. It is a fully managed service that makes it easy to collect, process, and analyze streaming data.
Code to create a new stream and put data into a Kinesis stream using the AWS SDK for Python
AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service that helps to prepare and load data for analytics.
Using Glue, we can create a job to read data from Kinesis stream and write it to an S3 bucket.
Code to create a Glue job using the AWS SDK for Python
AWS S3
AWS S3 is a fully managed object storage service to store and retrieve any amount of data, at any time, from anywhere on the web.
S3 is designed for 99.999999999% (11 9's) of durability, and stores data redundantly across multiple devices in multiple facilities. This makes it an ideal solution for use cases such as data archiving, backup and recovery, and disaster recovery.
Code to create an S3 bucket using the AWS SDK for Python
Additionally Amazon Redshift can be used to run complex queries on your data, and generate insights and reports in near real-time.
AWS ETL Glue can be used for data cleaning, filtering, and transformation tasks on your data, and load it into a target data store, such as Redshift.
Building a data pipeline on AWS is a powerful way to move data efficiently, and with the right tools and techniques, it can be done quickly and easily. With the AWS services you can build robust, scalable, and cost-effective data pipelines that can handle a wide variety of use cases.
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