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  5. Data Miner vs KNIME

Data Miner vs KNIME

OverviewComparisonAlternatives

Overview

Data Miner
Data Miner
Stacks7
Followers21
Votes0
KNIME
KNIME
Stacks53
Followers46
Votes0

Data Miner vs KNIME: What are the differences?

Data Miner: Extract Data From any Website in Seconds. It is a Google Chrome extension that helps you scrape data from web pages and into a CSV file or Excel spreadsheet; KNIME: Create and productionize data science using one easy and intuitive environment. It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

Data Miner and KNIME can be categorized as "Data Science" tools.

Some of the features offered by Data Miner are:

  • Scrape with one click
  • No coding
  • No bots

On the other hand, KNIME provides the following key features:

  • Access, merge, and transform all of your data
  • Make sense of your data with the tools you choose
  • Support enterprise-wide data science practices

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Detailed Comparison

Data Miner
Data Miner
KNIME
KNIME

It is a Google Chrome extension that helps you scrape data from web pages and into a CSV file or Excel spreadsheet.

It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

Scrape with one click; No coding; No bots; Privacy; Custom Scraping; Form Filling Automation
Access, merge, and transform all of your data; Make sense of your data with the tools you choose; Support enterprise-wide data science practices; Leverage insights gained from your data
Statistics
Stacks
7
Stacks
53
Followers
21
Followers
46
Votes
0
Votes
0
Integrations
Datadog
Datadog
GraphPipe
GraphPipe
Keras
Keras
Polyaxon
Polyaxon
Google Chrome
Google Chrome
Comet.ml
Comet.ml
Python
Python
Apache Spark
Apache Spark
R Language
R Language
TensorFlow
TensorFlow
Apache Hive
Apache Hive
Apache Impala
Apache Impala
Keras
Keras
H2O
H2O

What are some alternatives to Data Miner, KNIME?

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

NumPy

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

PyXLL

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

SciPy

SciPy

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Dataform

Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

PySpark

PySpark

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Anaconda

Anaconda

A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

Dask

Dask

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

Pentaho Data Integration

Pentaho Data Integration

It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.

StreamSets

StreamSets

An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

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