Big data is making tremendous impacts in the world globally. With modern humans’ hunger for results from data being collected on a daily basis, big data has become of the rising fields pushing the world into the fourth industrial revolution. But even the best scientist are relying on tools in decoding volumes of massive data.
See below some of the tools that scientists are using for big data analysis according to the tech-focused website Guru99.
1. Xplenty
Xplenty is a cloud-based ETL solution providing simple visualized data pipelines for automated data flows across a wide range of sources and destinations. Xplenty’s powerful on-platform transformation tools allow you to clean, normalize, and transform data while also adhering to compliance best practices.
Features:
- Powerful, code-free, on-platform data transformation offering,
- Rest API connector – pull in data from any source that has a Rest API,
- Destination flexibility – send data to databases, data warehouses, and Salesforce,
- Security focused – field-level data encryption and masking to meet compliance requirements,
- Rest API – achieve anything possible on the Xplenty UI via the Xplenty API,
- A customer-centric company that leads with first-class support.
2. Microsoft HDInsight
Azure HDInsight is a Spark and Hadoop service in the cloud. It provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization to run their big data workloads.
Features:
- Reliable analytics with an industry-leading SLA,
- It offers enterprise-grade security and monitoring,
- Protect data assets and extend on-premises security and governance controls to the cloud,
- A high-productivity platform for developers and scientists,
- Integration with leading productivity applications,
- Deploy Hadoop in the cloud without purchasing new hardware or paying other up-front costs.
3. Skytree
Skytree is one of the best big data analytics tools that empower data scientists to build more accurate models faster. It offers accurate predictive machine learning models that are easy to use.
Features:
- Highly Scalable Algorithms,
- Artificial Intelligence for Data Scientists,
- It allows data scientists to visualize and understand the logic behind ML decisions,
- Skytree via the easy-to-adopt GUI or programmatically in Java,
- Model Interpretability,
It is designed to solve robust predictive problems with data preparation capabilities, - Programmatic and GUI Access.
4. Talend:
Talend is a big data analytics software that simplifies and automates big data integration. Its graphical wizard generates native code. It also allows big data integration, master data management, and checks data quality.
Features:
- Accelerate time to value for big data projects,
- Simplify ETL & ELT for big data,
- Talend Big Data Platform simplifies using MapReduce and Spark by generating native code,
- Smarter data quality with machine learning and natural language processing,
- Agile DevOps to speed up big data projects,
- Streamline all the DevOps processes.
5. Splice Machine:
Splice Machine is one of the best big data analytics tools. Their architecture is portable across public clouds such as AWS, Azure, and Google.
Features:
- It is a big data analytics software that can dynamically scale from a few to thousands of nodes to enable applications at every scale,
- The Splice Machine optimizer automatically evaluates every query to the distributed HBase regions,
- Reduce management, deploy faster, and reduce risk,
- Consume fast streaming data, develop, test, and deploy machine learning models.