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How to Read a CSV File from Amazon S3 Using Python (With Headers and Rows Displayed)

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  Introduction If you’re working with cloud data, especially on AWS, chances are you’ll encounter data stored in CSV files inside an Amazon S3 bucket . Whether you're building a data pipeline or a quick analysis tool, reading data directly from S3 in Python is a fast, reliable, and scalable way to get started. In this blog post, we’ll walk through: Setting up access to S3 Reading a CSV file using Python and Boto3 Displaying headers and rows Tips to handle larger datasets Let’s jump in! What You’ll Need An AWS account An S3 bucket with a CSV file uploaded AWS credentials (access key and secret key) Python 3.x installed boto3 and pandas libraries installed (you can install them via pip) pip install boto3 pandas Step-by-Step: Read CSV from S3 Let’s say your S3 bucket is named my-data-bucket , and your CSV file is sample-data/employees.csv . ✅ Step 1: Import Required Libraries import boto3 import pandas as pd from io import StringIO boto3 is...
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How Hadoop is Better for Legacy data

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Here is an interview question on legacy data. You all know that a lot of data is available on legacy systems. You can use Hadoop to process the data for useful insights.


How Hadoop is Better for Legacy data


1. How should we be thinking about migrating data from legacy systems?


Treat legacy data as you would any other complex data type. 


HDFS acts as an active archive, enabling you to cost-effectively store data in any form for as long as you like and access it when you wish to explore the data.


And with the latest generation of data wrangling and ETL tools, you can transform, enrich, and blend that legacy data with other, newer data types to gain a unique perspective on what’s happening across your business.


2. What are your thoughts on getting combined insights from the existing data warehouse and Hadoop?


Typically one of the starter use cases for moving relational data off a warehouse and into Hadoop is active archiving. 


This is the opportunity to take data that might have otherwise gone to the archive and keep it available for historical analysis.


The clear benefit is being able to analyze data for the types of extended time periods that would not otherwise be cost feasible (or possible) in traditional data warehouses. 


An example would be looking at sales, not just in the current economic cycle, but going back 3 – 5 years or more across multiple economic cycles.


You should look at Hadoop as a platform for data transformation and discovery, compute-intensive tasks that aren’t a fit for a warehouse. 

Then consider feeding some of the new data and insights back into the data warehouse to increase its value.


3. What’s the value of putting Hadoop in the Cloud?

The cloud presents a number of opportunities for Hadoop users. 


Time to benefit through quicker deployment and eliminating the need to maintain cluster infrastructure Good environment for running proofs-of-concept and experimenting with Hadoop.


Most Internet of Things data is cloud data.


Running Hadoop in the cloud enables you to minimize the movement of that data The elasticity of the cloud enables you to rapidly scale your cluster to address new use cases or add more storage and compute.

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