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Pandas VS Amazon Kinesis

Compare Pandas VS Amazon Kinesis and see what are their differences

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Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Amazon Kinesis logo Amazon Kinesis

Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Amazon Kinesis Landing page
    Landing page //
    2022-01-28

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Amazon Kinesis features and specs

  • Real-time data processing
    Amazon Kinesis allows for real-time processing of data streams, enabling rapid ingestion and analysis of data as it arrives.
  • Scalability
    Kinesis is highly scalable and can handle massive volumes of streaming data, expanding automatically to meet your needs.
  • Fully managed service
    As a fully managed service, Kinesis handles infrastructure maintenance, provisioning, and scaling, reducing operational overhead.
  • Integration with AWS ecosystem
    Kinesis integrates seamlessly with other AWS services such as Lambda, Redshift, S3, and Elasticsearch, facilitating comprehensive data workflows.
  • Multiple data stream applications
    The service supports different types of data stream applications including data delivery, analytics, and real-time processing, making it versatile.
  • Security
    Offers robust security through integration with AWS Identity and Access Management (IAM), encryption at rest with AWS Key Management Service (KMS), and in-transit encryption.

Possible disadvantages of Amazon Kinesis

  • Cost
    While pricing is scalable, costs can escalate quickly with high data throughput and storage requirements, potentially becoming expensive for large-scale implementations.
  • Complex setup and management
    Despite being a managed service, the initial setup and tuning of Kinesis can be complex and may require specialized knowledge.
  • Latency
    Although designed for real-time data processing, there can be minor latency involved that might not fit ultra-low latency requirements.
  • Limited data retention
    Kinesis typically supports up to 7 days of data retention in streams, which might be insufficient for use cases requiring longer retention periods without extra storage solutions.
  • API Rate Limits
    API access to Kinesis is subject to rate limits, which could impact applications requiring high-frequency data ingestion and retrieval.
  • Dependence on AWS services
    Tight integration with AWS services can pose a challenge for organizations looking for a multi-cloud or cloud-agnostic strategy.

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Analysis of Amazon Kinesis

Overall verdict

  • Yes, Amazon Kinesis is a good option for organizations that need to process and analyze large streams of data in real-time. Its scalability, ease of integration with existing AWS infrastructure, and advanced features make it a preferred choice for many enterprise-level applications.

Why this product is good

  • Amazon Kinesis is generally considered a robust choice for real-time data processing because it can ingest, buffer, and process streaming data at scale. It offers features like durable storage, the ability to handle high throughput with low latency, and seamless integration with other AWS services. This makes it particularly well-suited for applications that require real-time analytics, data lake integrations, or reacting to changing data streams with minimal delay.

Recommended for

  • Organizations dealing with large quantities of streaming data
  • Businesses needing real-time data analytics and processing
  • Developers looking for seamless integration with AWS services
  • Teams wanting to build real-time machine learning models
  • Companies implementing IoT solutions requiring data streaming

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Amazon Kinesis videos

+ Add

AWS Big Data - Amazon Kinesis Analytics Introduction and Demonstration

More videos:

  • Review - Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Data Lake Ingestion

Category Popularity

0-100% (relative to Pandas and Amazon Kinesis)

Pandas

Amazon Kinesis

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Pandas and Amazon Kinesis

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Amazon Kinesis Reviews

Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Amazon Kinesis was built to handle massive amounts of data, allowing it to be uploaded to a Redshift cluster. After the event stream is read and the data is transformed, it is placed into a table in Amazon SCTS in an Amazon ES domain. Thus, there is no need to use a server (instead, you need to integrate AWS ETL and AWS Lambda).
Source: visual-flow.com
6 Best Kafka Alternatives: 2022’s Must-know List
Kinesis enables streaming applications to be managed without additional infrastructure management. This highly scalable platform can process data from various sources with low latency. Known for its speed, ease of use, reliability, and capability of cross-platform replication, Amazon Kinesis is one of the most popular Kafka Alternatives. It is used for many purposes,...
Source: hevodata.com
Top 15 Kafka Alternatives Popular In 2021
Amazon Kinesis, also known as Kinesis Streams, is a popular alternative to Kafka, for collecting, processing, and analyzing video and data streams in real-time. It offers timely and insightful information, streaming data in a cost-effective manner with complete flexibility and scalability. It is easy to ingest data encompassing audios, videos, app logs, etc. It offers an...
Source: www.spec-india.com
16 Top Big Data Analytics Tools You Should Know About
Amazon Kinesis is a massively scalable, cloud-based analytics service which is designed for real-time applications.
Source: www.analytixlabs.co.in

Social recommendations and mentions

Based on our record, Pandas should be more popular than Amazon Kinesis. It has been mentiond 219 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 2 months ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 2 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 10 months ago
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Amazon Kinesis mentions (26)

  • FINTECH SCALABILITY
    Real-Time Processing — With Amazon Kinesis and Amazon DynamoDB, fintech firms can analyze transactions instantly, identify fraud before it happens. - Source: dev.to / 3 months ago
  • Top 7 Kafka Alternatives For Real-Time Data Processing
    Amazon Kinesis is a fully managed real-time data streaming service by AWS, designed for large-scale data ingestion and processing. - Source: dev.to / 9 months ago
  • AWS Operational issue – Multiple services in us-east-1
    Https://aws.amazon.com/kinesis/ > Amazon Kinesis Data Streams is a serverless streaming data service that simplifies the capture, processing, and storage of data streams at any scale. I'd never heard of that one. - Source: Hacker News / 10 months ago
  • Event-Driven Architecture on AWS
    Event Consumers: Services that actively listen for events and respond accordingly. These consumers can be easily implemented using microservices, AWS Lambda or Amazon Kinesis (for ingesting, processing, and analyzing streaming data in real-time). - Source: dev.to / about 1 year ago
  • AWS DEV OPS Professional Exam short notes
    When you see Amazon Kinesis as an option, this becomes the ideal option to process data in real time. Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit... - Source: dev.to / about 1 year ago
View more

What are some alternatives?

When comparing Pandas and Amazon Kinesis, you can also consider the following products

NumPy - NumPy is the fundamental package for scientific computing with Python

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

OpenCV - OpenCV is the world's biggest computer vision library

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

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