What is better DataMelt or Apache Spark? Getting the right Data Analytics Software product is as straightforward as evaluating the solid and weak features and terms offered by DataMelt and Apache Spark. Here it's also possible to match their all round scores: 8.0 for DataMelt vs. 9.8 for Apache Spark. Or you can look at their general user satisfaction rating, 98% for DataMelt vs. 97% for Apache Spark.
We suggest that you take some time to examine their unique features and determine which one is the better alternative for your company. What’s more keep in mind to consider your company’s or industry’s unique case, for example, a multilingual app for a global company or a mobile version to help you work in the field.
Our experts made sure to review all popular Data Analytics Software products offered on the market, but among them these three deserved our special attention: Apache Spark, Apache Hadoop, IBM Business Analytics.
DataMelt is a free and open-source software environment. This means you can use it for free without worrying about any enterprise pricing plan fees.
Meanwhile, if you’re going to use the software environment for commercial purposes, you need to pay for a one-time membership fee of $20 and abide by the restrictions and conditions specified under the Creative-Commons License.
Once you become a member of the DataMelt project, you can get a commercial usage license as well as access DataMelt’s full documentation, source code and script repositories, help system, and other important files and resources.
Apache Spark is delivered based on the Apache License, a free and liberal software license that allows you to use, modify, and share any Apache software product for personal, research, commercial, or open source development purposes for free. Thus, you can use Apache Spark with no enterprise pricing plan to worry about.
Apache Spark integrates with some open source projects developed by The Apache Software Foundation as well as with third-party systems such as the following:
Apache Hadoop
Apache Mesos
HDFS (Hadoop Distributed File System)
Apache Cassandara
Apache HBase
Apache Hive
Hadoop YARN
Kubernetes
EC2
Languages Supported
English
Chinese
German
Hindi
Japanese
Spanish
French
Russian
Italian
Dutch
Portugese
Polish
Turkish
Swedish
Arabic
Prominent Clients
Anson Abraham, Data Czar, Envisagenics, Inc; Kartik Chavan, Data Analyst, The University of Texas at Arlington; Sunil Dhage, Big Data Analyst, PSL Group
Available Devices
Windows
Linux
Android
iPhone/iPad
Mac
Web-based
Windows Mobile
Company Size
Small Business
Large Enterprises
Medium Business
Freelancers
Available Support
email
phone
live support
training
tickets
General Info
An easy-to-use, lightning-fast, and unified analytics software solution developed to handle computational processes and tasks on high volumes of data, process SQL queries for structured data, streamline the creation of scalable and fault-tolerant applications that execute live stream data processing, and implement graph analytics and computation in a flexible and fast manner.
In the event you are still having second thoughts about which app will work best for your business it might be a good idea to check out each service’s social metrics. Such metrics are usually an indicator of how popular each service is and how big is its online presence. For example, in case of Facebook DataMelt has 10 likes on their official page while Apache Spark profile is liked by 1552 people.
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