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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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5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.

5 HBase Vs. RDBMS Top Functional Differences

5 HBase Vs. RDBMS Top Functional Differences


Here're the differences unlock now.

Random Accessing


HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database.

Database Rules


RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties.


HBase follows BASE properties and implements complex queries.
Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS.

Storage


From small to medium storage application there is the use of RDBMS that provides the solution with MySQL and PostgreSQL whose size increase with concurrency and performance. 


Codd's rules always need to keep in mind while extending the size of the database in the use of data processing.

Data Integrity


RDBMS focuses on and emphasizes consistency, referential integrity, abstraction from the physical layer, and complex queries through SQL language.

Takeaway

  • There is no single-point failure in HBASE. You always have backup data.
  • The server regions have the flexibility to share or rebalance the load among the servers.
  • Automatic partition helps to distribute its workload among servers. It happens with its in-built feature of HBASE.
  • The cost involved in the maintenance of HBASE is comparatively low.


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