For managing big data, there are several tools like Python, R-Language, Hadoop, Apache Spark, MongoDB, Apache Cassandra, Elastic Search, etc.
Python
But, if anything makes Python unique, it is its open-source status. This allows it to be a very collaborative Big Data tool, where the users who use said software share their uses, improving the platform for the benefit of all those who use Python.
The only drawback of this Big Data tool is its slow execution speed. Despite this, it is usually a software that has many features to integrate tasks where there are no heavy calculations.
R-Language

As with Python, one of the most outstanding points of the R programming language is its collaborative philosophy, since it has an open-source license. This allows users to access a large number of libraries created by the R community. Another favorable aspect is the RStudio tool, which offers a syntax editor that supports code execution, as well as tools for tracing, debugging, and workspace management.
Although R indeed is one of the most widely used Big Data tools, it is complex software to use, since it is more like the language of mathematics than other programming languages. Despite this, R continues to stand out as one of the best Big Data tools on the market.
Hadoop
Another of the most important tool for managing big data is Hadoop. This tool, also with an open-source license, is considered the standard framework for storing large volumes of data. Also, this tool is used to analyze and process data. Its importance in the Big Data sector is such that companies like Facebook or Yahoo make use of it.
Its main advantages over other similar Big Data tools are:
- Ability to store and process large amounts of any type of data instantly.
- Computing power that allows processing Big Data at high speed.
- Hardware fault tolerance. That is, if a node fails, the jobs are redirected to other modes to ensure that processing does not fail.
- Automatic storage of copies.
- Flexibility in data storage and processing.
- Low cost, given its open-source license.
- Scalability to grow data systems.
Despite the multiple advantages that Hadoop offers, its complexity in use can be an inconvenience for all those who want to start with Big Data tools.
Apache Spark
One of the great advantages of Apache Spark is that it supports a wide range of programming languages. Therefore, its users can program using different languages such as Java, Scala, Python, or R.
Finally, another aspect to highlight about Apache Spark is that its speed in memory can be 100 times faster than Hadoop. Similarly, on disk, it can be up to 10 times faster than MapReduce.
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MongoDB
Apache Cassandra
Apache Cassandra is one of the most widely used tool for managing big data. It is a distributed database with which high performance can be obtained in the input and output of data. Its usability is quite simple and it is also easy to scale. It is fault-tolerant, even though it is a high-performance database.
Thus, Apache Cassandra is a brilliant solution for many Big Data projects. However, it is not a suitable tool to host a conventional data warehouse, that is, Cassandra is not the best option for enterprise data storage.
ElascticSearch
The most relevant functionality of Elasticsearch is the permission to index and analyze large volumes of data in real time and query them. One of the most used examples is full-text queries. And since the data is indexed, the results offered by Elasticsearch are very fast.
In this way, with Elasticsearch you can perform complex text searches, as well as visualize the status of each node. Another advantage is its easy scalability in case more power is needed.
Apache Storm
This Big Data tool is very useful for monitoring processes. For example, Apache Storm can be used to extract information from social networks or data sources with high volatility in their data.

Apache Drill
In Big Data, the possibility of integrating tools under the same software is, in many cases, fundamental. In this aspect, Apache Drill stands out, an SQL query engine that supports a wide variety of databases and file systems such as:
- HBase
- MongoDB
- MapR-DB
- HDFS
- MapR-FS
- Amazon S3
- Azure Blob Storage
- Google Storage
- Swift
- NAS
- Local Files
In addition to its great versatility in the databases it supports, Apache Drill allows you to join data from different stores under the same interface such as ODBC.