Big data refers to volumes of structured and unstructured data. Big data technology is the umbrella term for data frameworks, including tools and techniques used to investigate and transform data. Those tools are software that incorporate data mining, data storage, data sharing, and data visualization.
Companies use big data technologies and tools to assess and predict behavior on a wide scale in order to improve decision-making processes. Ultimately, these can help companies reduce operating costs, offer better products and services, and see how their consumers are spending, resulting in more profits and growth. Let’s see which tools are some of the best, what they offer, and some of their notable features.
Tableau
Tableau is a business intelligence and analytics software solution. It presents a variety of integrated products that aid organizations in visualizing and understanding their data. Tableau can handle all data units and is easy to use for both technical and non-technical users, and it provides real-time personalized dashboards.
Notable features:
- No-code data queries.
- VizQL data visualization technology.
- Its in-memory data engine called “Hyper.”
Zoho Analytics
Zoho analytics system is a cloud-based reporting and business intelligence program that can handle everything from analytics to sales. It offers businesses everything they need in an effective customer relationship management platform and can be used to create insightful reports and dashboards for informed decision-making.
Notable features:
- Detailed reports.
- Comprehensive gamification models.
- Marketing tools.
Talend
Talend is a massive open-source data platform that offers data integration and data management solutions. It helps companies in making real-time decisions and become more data-driven. By using this technology, data becomes more accessible, its quality enhances, and it can be moved quickly to the target systems.
Notable features:
- Rapid speed of results.
- Batch Data Movement.
- Data Replication & Synchronization.
Hadoop
Hadoop is a software framework written in Java that provides cross-platform support. It’s used for distributed file systems and managing large amounts of files. The MapReduce programming model is used to process large data datasets. Some companies that use Hadoop include Amazon Web services, IBM, Intel, Microsoft, Facebook, etc.
Notable features:
- HDFS (Hadoop Distributed File System).
- Highly scalable.
- Support for POSIX-style file system extended attributes.
- Brings flexibility in data processing.
Xplenty
Xplenty platform is used to integrate, process, and prepare data for analytics on the cloud. This software allows you to combine, store, and prepare data for cloud analytics. Its user-friendly visual design aids in the implementation of ETL, ELT, and replication solutions. Xplenty is a comprehensive toolkit for creating data pipelines using low-code and no-code methods. It has marketing, sales, service, and development solutions.
Notable features:
- Elastic and scalable cloud platform.
- Instant access to a number of data sources.
- The rich expression language that allows implementing complex data preparation functions.
- The API component for advanced customization and flexibility.
R-Language
R is one of the most popular statistical analysis programs available. It is an open-source software environment written in C, Fortran, and R. Its use cases include data analysis, data processing, estimation, and graphical display. Statisticians and data miners utilize this platform widely.
Notable features:
- The vastness of the package ecosystem.
- Unmatched Graphics.
- Charting benefits.
Cassandra
Cassandra system employs CQL (Cassandra Structure Language) to interact with the database. It has a set of features, including simple ring architecture, automated replication, and easy log-structured storage. It offers concrete solutions to get the speed, scale, and availability that large volumes of data demand.
Notable features:
- Log-structured storage.
- Automated replication.
- Linear scalability.
- Simple Ring architecture.
MongoDB
MongoDB is a document-oriented database written in C, C++, and JavaScript. This open-source tool is a NoSQL database program that supports multiple operating systems. This tool lets users combine and store data of multivariate types without compromising the powerful indexing options, data access, and validation rules.
Notable features:
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- Uses BSON format.
- Sharding, Indexing, Replication.
- Server-side execution of javascript.
- Support for multiple technologies and platforms.
HPCC
HPCC—which stands for High-Performance Computing Cluster—is a complete big data solution over a highly scalable supercomputing platform supporting data parallelism, pipeline parallelism, and system parallelism. It is written in C++ and a data-centric programming language known as ECL (Enterprise Control Language).
Notable features:
- Software architecture applied on commodity computing clusters.
- Parallel data processing.
- Supports high-performance online query applications.
- Graphical IDE for simplified development, testing, and debugging.
Apache Spark
Apache Spark is an open-source data analytics, machine learning, and fast cluster computing platform. It’s written in Scala, Java, Python, and R. With in-built features for streaming, SQL, machine learning, and graph processing support, Spark earns the cite as the speedest and common generator for big data transformation and cleansing.
Notable features:
- Great APIs and lazy execution.
- Real-time stream processing.
- In-Memory Computation.
Datawrapper
Datawrapper is an open-source platform for data visualization that aids its users to generate simple, precise, and embeddable charts very quickly.
Notable features:
- Performs well with a wide range of platforms.
- Brings all the charts in one place.
- Great customization and export options.
- Requires zero coding.
Some of the analytics and visualization tools for big data included in our list were open-source tools, while the others were paid ones. Depending on the project or your job position, you need to choose the right big data tool wisely in order to achieve and deliver the best results. We hope to have guided you through the options and provided a clear summary of all these great tools.