In this article, you can learn which big data tools are the best on the market compared to the rest, and why. Below will be a detailed overview of each tool and its functionality.
What Big Data is all about
Big Data is a system of different approaches, tools, and techniques that are used to handle different types of data (structured and unstructured), very large volumes, and with a variety of contexts, to bootstrap results that are viewed by humans. Big Data is essentially a good alternative to traditional database management systems and Business Intelligence solutions.
Big data doesn’t mean any amount of data or data in general. It’s data assessment and processing methods that allow for distributed processing of information.
Big data plays an important role in international business because the more data that comes in for processing, the more accurate the analysis, and the final result will be. This, in turn, will lead to more efficient decision-making and cost reduction.
Apache Hadoop review
We want to start our big data analytics tools comparison from one of the best big data tools on the market Apache Hadoop. This program copes very well with the processing of data, especially large amounts of data. It is also an open and free big data storage system along with a set of utilities, libraries, frameworks, and distributions for development.
Hadoop consists of four parts:
- HDFS is a file system that is needed to run on common, standard hardware
- MapReduce is a distributed computing program that is used for parallel computing
- YARN- a technology intended for cluster management
- Libraries- to work the rest of the modules with HDFS
X-plenty is a large-scale cloud service offering ETL solutions and data pipeline tools. One of its features is that it handles different types of data and integrates with different sources, repositories, and databases.
- The simple data conversion process
- REST API
- Easy and comfortable to use
- High-level security
- Different data sources
- The approach aimed directly at customers
Spark is a robust data analytics program whose main feature is to work with big data, through distributed computing in RAM, which increases processing speed. It is very similar to Hadoop in many ways but uses other types of computation, including interactive queries and streaming processing.
This program is designed for a wide range of tasks, such as iterative algorithms, interactive queries, and streaming.
Cassandra is a free and open-source program, and it stores values as key-value pairs. Because of its architectural structures, Apache Cassandra has the following advantages:
- Wide coverage and reliability due to the lack of a central server
- Well-adaptable data schema
- High bandwidth
- Proprietary SQL-like query language
- Adjustable consistency and replication support
- Automatically configured problem resolution