From Data Ingestion to Visualization: Building a Powerful Data Pipeline with Python and AWS services

In this video, we will learn about three important components of building a data pipeline: writing data into a Kinesis stream using Python, transforming the data using Lambda functions and storing it in DynamoDB, and finally visualizing and indexing the data using Kibana and Elasticsearch.

Firstly, we will walk through a Python program that demonstrates how to write data into a Kinesis stream using the Boto3 library. This is an essential step in building a data pipeline as it enables data ingestion from a variety of sources.

Next, we will look at how to use Lambda functions to transform the data before storing it in DynamoDB. This step allows for data cleaning, normalization, and other transformations that may be required before storing the data in a database.

Finally, we will explore how to use Kibana for data visualization and Elasticsearch for indexing. These tools allow for easy exploration and analysis of the data stored in DynamoDB, enabling us to make informed decisions and derive valuable insights from our data. By the end of this video, you will have a solid understanding of the different components of a data pipeline and how they can be used together to build powerful data-driven applications.
------------------------------------------------------------------------------------------------------------------------------
lets connect on...
----------------------

--------------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------

**Hashtags**

#AWSkinesis
#awslambda
#awsdynamodb
#awsopensearch