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IBM Cloud Pak for Data VS Pandas

Compare IBM Cloud Pak for Data VS Pandas and see what are their differences

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IBM Cloud Pak for Data logo IBM Cloud Pak for Data

Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • IBM Cloud Pak for Data Landing page
    Landing page //
    2023-02-11
  • Pandas Landing page
    Landing page //
    2023-05-12

IBM Cloud Pak for Data features and specs

  • Unified Platform
    IBM Cloud Pak for Data offers a unified platform that integrates various data management tasks, including data collection, processing, governing, and analyzing. This cohesion facilitates streamlined workflows and reduces the complexity involved in managing disparate tools.
  • Scalability
    The platform is designed to scale according to business needs, from small datasets to large-scale enterprise environments. Kubernetes-based containerization allows for efficient resource allocation and scalability.
  • AI and Machine Learning Integration
    IBM Cloud Pak for Data comes with built-in AI and machine learning capabilities, enabling organizations to leverage advanced analytics and predictive modeling directly within the platform.
  • Flexible Deployment Options
    Users can deploy IBM Cloud Pak for Data across multiple environments such as on-premises, private cloud, and public cloud, offering flexibility to meet various business and regulatory requirements.
  • Security and Compliance
    The platform includes robust security features that help ensure data protection and compliance with various regulatory standards, including GDPR and CCPA.
  • Integration with Existing Systems
    IBM Cloud Pak for Data supports APIs and connectors for seamless integration with existing systems and data sources, enabling smoother data flow and reducing the need for extensive custom development.
  • Comprehensive Toolset
    The platform offers a wide range of tools for data governance, data science, data engineering, and business analytics, providing a comprehensive solution for end-to-end data management.

Possible disadvantages of IBM Cloud Pak for Data

  • Learning Curve
    Given its comprehensive and feature-rich nature, IBM Cloud Pak for Data may have a steep learning curve, particularly for users who are new to IBM products or advanced data management tools.
  • Cost
    Depending on the scale of deployment and required features, the platform can be relatively expensive, potentially making it less suitable for smaller organizations with limited budgets.
  • Complexity
    The extensive capabilities and modular architecture can introduce complexity, requiring skilled personnel for effective implementation and management.
  • Dependency on IBM Ecosystem
    Organizations that are heavily invested in non-IBM technologies might find it challenging to integrate IBM Cloud Pak for Data seamlessly with their existing ecosystem.
  • Vendor Lock-In
    There is a risk of vendor lock-in, as committing to IBM Cloud Pak for Data can make it difficult to switch to alternative solutions without significant effort and cost.
  • Hardware Requirements
    Organizations opting for on-premises deployments may face significant hardware requirements, which could necessitate additional capital investment.
  • Customization Needs
    Depending on the specific needs of the organization, substantial customization might be required to tailor the platform to fit unique business processes and workflows.

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.

IBM Cloud Pak for Data videos

+ Add

IBM Cloud Pak for Data - Product Walkthrough

More videos:

  • Review - Overview of IBM Cloud Pak for Data

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

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

Category Popularity

0-100% (relative to IBM Cloud Pak for Data and Pandas)

IBM Cloud Pak for Data

Pandas

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
33 33%
Data Dashboard
67% 67
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using IBM Cloud Pak for Data and Pandas. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare IBM Cloud Pak for Data and Pandas

IBM Cloud Pak for Data Reviews

10 Best Big Data Analytics Tools For Reporting In 2022
IBM Cloud Pak for Data is a fully-integrated, cloud native, data and AI platform designed for sophisticated DataOps and business analytics solutions. IBM boasts a potential for a 25-65% reduction in extract, transform, load (ETL) requests by eliminating the complexities of data integration of different data types and structures using Cloud Pak for Data. You can customize...
Source: theqalead.com

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

Social recommendations and mentions

Based on our record, Pandas seems to be more popular. 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.

IBM Cloud Pak for Data mentions (0)

We have not tracked any mentions of IBM Cloud Pak for Data yet. Tracking of IBM Cloud Pak for Data recommendations started around Mar 2021.

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 / 17 days 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 1 month 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 / about 1 month 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 / 3 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 / 9 months ago
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What are some alternatives?

When comparing IBM Cloud Pak for Data and Pandas, you can also consider the following products

Azure Databricks - Azure Databricks is a fast, easy, and collaborative Apache Spark-based big data analytics service designed for data science and data engineering.

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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

data.world - The social network for data people

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

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