5 Great Big Data Tools For The Future – From Hadoop To Cassandra
Data is fast becoming an essential part of small and big companies globally. Treating data as an asset implies using tools to perform data analytics to the vital aspects of your business. The increasing importance and demand for data analytics have generated several global prospects and ideas on data. According to International Data Corporation, Big data and analytics market are likely to hit $189.1 billion in 2019.
Data Analytics Tools
Data analytics tools are tools that offer functionalities that permit aggregation, analysis, and visualization of data quickly. Data Analytics tools are helpful to any and every business who find customer preferences, data, market trends, and others useful. The top data analytics tools are becoming tasking to select as open-source tools are becoming more popular, accessible, and performance-focused. With many possibilities available, the desire for the best Data Analytics tools is on the increase.
There are big and small players in the Data Analytics world that automatically collect, process, and analyze data. To help you out, the compilation of the top 5 Data Analytics tools features some of the best Data Analytics products available today. Assessment of the top 5 Data Analytics tools is based on:
- Functionality
- Learning (and Community Support)
- Popularity
This approach makes it easier for you to decide the best possible data analytics tool for your business. Here are 5 of the most popular big data tools that are currently being used.
- Apache Cassandra
- Apache Hadoop
- Elastic Search
- Presto
- Talend
1. Apache Cassandra
Developed in 2008 by Apache Software Foundation, Apache Cassandra is a free and open-source database management tool. Apache Cassandra is distributed and applies methods based on NoSQL. Data management execution involves the use of cluster forms, which are interconnections to several nodes in multiple data centers. In NoSQL, Apache Cassandra has also termed a ‘column-oriented database.’
Its primary usage comes in big data applications where real-time data are applicable — for instance, sensor devices and social networking platforms. Additionally, Cassandra uses a decentralized architecture, which implies function modules like data partitioning, failure handling, replication, and scaling are available distinctly and work in a cycle. You can learn more from Apache Cassandra documentation.
The key features of Apache Cassandra include:
- Ability to function on less powerful hardware.
- Based on Amazon’s Dynamo, Cassandra architecture implements a key-value database system.
- Cassandra Query Language
- Distributed deployment and high application scalability
- Fault tolerance and decentralized system
- Apache Cassandra performs fast read/write functions.
- Tunable consistency and MapReduce Support
Download Link: http://cassandra.apache.org/download/
2. Apache Hadoop
Apache Hadoop is an open-source data analytics tool for distributed storage and large datasets processing. Additionally, Apache Hadoop provides services for data access using a collection of utilities that enables a network of multiple computers. Apache Hadoop offers a solution that is intrinsically resilient to supporting significant computing clusters. You can learn more from Apache Hadoop documentation.
Key Features of Apache Hadoop includes:
- Apache Hadoop offers a highly scalable platform and analyzes data at a petabyte level.
- Data storage can be in any format and parsed when read. (Offering structured schemas, semi-structured and unstructured formats)
- Failure of nodes in cluster rarely occurs. But if it does, the system automatically re-replicates the data and readdresses the residual data.
- Interaction with other preferred data analytics platform is possible. The seamless process with data uses batch, interactive SQL, or low-latency access alongside NoSQL.
- Open-source platform running on low-cost hardware makes it more reasonably priced solution.
Download link: https://hadoop.apache.org/releases.html
3. Elastic Search
Elastic Search is a JSON-based engine for searching and making analytics of Big data. Elastic Search offers a distributed, analytics engine, and RESTful search towards resolving numbers of use cases. Also available on Elastic Search are easy management, maximum reliability, and horizontal scalability. You can learn more from Elastic Search documentation.
Key Features of Elastic Search includes:
- Allows the building and maintenance of customers in several languages like Java, Groovy, NET, and Python.
- Intuitive APIs for management and monitoring, thus providing complete control and visibility.
- It permits the combination of several search types that includes geo, metric, structured, unstructured, etc.
- It makes use of standard RESTful APIs and JSON.
- Elastic Search offers an enhanced experience with machine learning, monitoring, reporting, and security features.
- Real-time analytics and search features to handle big data through the use of Elasticsearch-Hadoop
Download link: https://www.elastic.co/downloads/elasticsearch
4. Presto
Developed by Facebook, Presto is notable for its regular work speed on commercial data. Presto functions as a distributed SQL query engine that connects well with Hadoop, MySQL, and more. Presto uses a distributed open-source approach in executing collaborative analytical queries to varying data sources. Presto system also provides great interactive analytics as it is amongst the best open source tools for Big Data analysis. You can learn more from Presto Documentation.
Key Features of Presto includes:
- Offers an adaptive multi-tenant system with the ability of synchronously running several memory operations, input/output (I/O), and CPU-intensive queries.
- Presto offers optimization for high performance, with critical optimizations like code-generation.
- Presto is extensible with a federated design which permits the creation of several clusters.
- Presto is flexible and configurable to support a massive variety of use cases with several limitations and performance features.
- Presto single query can combine data from many sources, permitting organizational conduct of Big Data analysis.
- Presto supports ANSI SQL (in addition to ARRAY, JSON, MAP, and ROW).
Download Link: https://prestodb.github.io/download.html
5. Talend
Talend is considered to be amongst the next-gen groundbreaker in big data and cloud integration solution. Talend remains an open-source platform which offers big data tool that automates and simplifies big data integration. Additional solutions provided by Talend includes checking data quality, data management, and generation of native code through a graphical wizard. You can learn more from Talend Documentation.
Key Features of Talend includes:
- Accelerating time to value for plans involving big data
- Offers Agile DevOps to accelerate big data schemes
- Talend Big Data Platform makes the use of Spark and MapReduce simpler by generating native codes.
- Smarter data quality with machine learning and natural language processing
- Simplification of ELT (Extract, Load, and Transform) and ETL (Extract, Transform and Load) for big data
- Streamlining all processes in DevOps
Download Link: https://www.talend.com/download/
CONCLUSION
Data is essential, but keeping track and making effective use of it keeps your business on top. If you plan to stay on track with finding customer preferences, market trends, effective business model, and future predictions, you should consider the top data analytics tools.
Statics about business is essential and should not go unnoticed. Also, understanding your business data traffic is vital. The use of a top analytics tool from one of the above (or even another), would help put a number to your success rate using data provided. So, towards moving in the right direction, understand, analyze, execute, and implement better outcomes for your data.
FAQs
What is the best data analytics tool?
The best data analytics tools are tools that offer simple yet commanding functionalities that permit aggregation, analysis, and visualization of data quickly. The best data analytics tool uses a platform with a scalable architecture that lets users manage a wide variety of data sizes, making it essential for big and small businesses alike. It is hard to say what tool is best for any company. In some cases, SQL server is great in others Hadoop is superior. It all depends on the use case, end goal and size of data. Our team has consulted and helped companies in fields such as healthcare, transportation, technology and several other industries find the right technology for their needs.
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