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Apache Ambari VS Pandas

Compare Apache Ambari VS Pandas and see what are their differences

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Apache Ambari logo Apache Ambari

Ambari is aimed at making Hadoop management simpler by developing software for provisioning, managing, and monitoring Hadoop clusters.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Apache Ambari Landing page
    Landing page //
    2023-01-08
  • Pandas Landing page
    Landing page //
    2023-05-12

Apache Ambari features and specs

  • Centralized Management
    Apache Ambari provides a centralized platform to manage, monitor, and provision Hadoop clusters efficiently. This feature simplifies the administration tasks by offering a single interface for managing cluster operations.
  • User-Friendly Interface
    Ambari offers a graphical user interface (GUI) that is intuitive and easy to use, enabling administrators to manage clusters without requiring extensive command-line knowledge.
  • Automated Installation
    It supports automated installation and configuration of Hadoop components, reducing the complexity and time required to set up a cluster.
  • Real-time Monitoring
    Ambari provides real-time insights into cluster health and performance through a variety of metrics and dashboards, allowing for proactive management.
  • Extensibility
    The platform is designed to be extensible, allowing developers to write custom alerts and metrics, thus adapting the system to meet specific needs.

Possible disadvantages of Apache Ambari

  • Resource Intensive
    Ambari can consume significant system resources, especially in larger clusters, which could impact performance if resources are not adequately provisioned.
  • Limited Support for Non-Hadoop Ecosystems
    The primary focus of Apache Ambari is on Hadoop ecosystems, and it lacks extensive support for non-Hadoop big data technologies, which can limit its applicability in heterogeneous environments.
  • Complexity for Small Clusters
    For smaller Hadoop deployments, the use of Ambari might be overkill and add unnecessary complexity due to its comprehensive nature.
  • Dependency on Updates
    Users can encounter compatibility issues or bugs following updates, which can require troubleshooting and delay important operations.
  • Steep Learning Curve for Customization
    While it is extensible, customization in Ambari can have a steep learning curve, demanding deeper technical knowledge to implement specific configurations or custom components.

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.

Apache Ambari videos

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Pandas videos

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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 Apache Ambari and Pandas)

Apache Ambari

Pandas

Data Dashboard
24 24%
Data Dashboard
76% 76
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Development
100 100%
Development
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Apache Ambari and Pandas

Apache Ambari Reviews

We have no reviews of Apache Ambari yet.
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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 a lot more popular than Apache Ambari. While we know about 219 links to Pandas, we've tracked only 1 mention of Apache Ambari. 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.

Apache Ambari mentions (1)

  • In One Minute : Hadoop
    Ambari, A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heatmaps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in... - Source: dev.to / over 2 years ago

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 / 7 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 / 23 days 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 / 26 days 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 / 8 months ago
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What are some alternatives?

When comparing Apache Ambari and Pandas, you can also consider the following products

Apache Pig - Pig is a high-level platform for creating MapReduce programs used with Hadoop.

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

Apache HBase - Apache HBase – Apache HBase™ Home

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

Apache Mahout - Distributed Linear Algebra

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

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