Top Data Analytics Tools to Unleash the Potential of Big Data

1. Cloudera

Cloudera is the perfect enterprise solution to help businesses manage their Hadoop ecosystem. The Service Monitor and Host Monitor roles in the Cloudera Management Service stockpile time-series data and health data, as well as Impala query and Yet Another Resource Negotiator (YARN) application metadata. They also deliver intricate data security, which is essential for storing any sensitive or personal data.

2. MongoDB

MongoDB is an efficient data analytics tool responsible for preserving data for mobile apps, product catalogs, real-time personalization, and content management, providing a single view across multiple systems. Here are some of the benefits of MongoDB:

1. It reduces operational overhead by up to 95 percent.
2. Its new flexible storage architecture includes the WiredTiger storage engine.
3. It has a global distribution with up to 50 replica-set members.
4. It takes up to 80 percent less storage space due to compression.

3. Vidyard

Vidyard is a business video hosting and analytics provider. Companies like Ernst & Young have entrusted their video analytics to Vidyard. With a splendid custom video player and real-time analytics, Vidyard is a boon for anyone looking for a custom solution. Some key metrics monitored by Vidyard include views, average attention span, minutes watched and most popular region.

4. KnowledgeREADER

KnowledgeREADER, from Angoss, is a portion of a wide suite of data analytics tools; it explicitly addresses text analytics related to customer-oriented and marketing applications. It specializes in visual representation, including dashboards for sentiment and text analysis, and also provides a map of the results of association mining to show words that tend to occur together.

Many of its cutting-edge features make use of the embedded Lexalytics text analytics engine, which is widely recognized for its potential. Entity, theme, and topic extraction are sustained, along with decision and strategy trees for profiling, segmenting and predictive modeling.

5. Meltwater Social (Sysomos)

Meltwater Social, formerly Sysomos, is a powerful social media and data analytics tool to provide deep insight into enterprise marketing and user analytics. The ability to use social listening, audience insight, and brand engagement is an important part of any marketing professional’s toolkit. However, Meltwater Social takes the idea of a social media management platform to an entirely new level with a robust and user-friendly analytics powerhouse on the backend. Powered by a learning type of artificial intelligence technology, the analytics are drawn from bits of social user data to provide complete insights that translate to more than marketing.

6. OpenRefine

OpenRefine is a software that cleans data to prepare it for analysis. What do we mean by that? Well, OpenRefine lets you cluster into cells any values that either spelling or capitalization of letters might differentiate but mean the exact same thing. This tool might appear basic, but it helps fight redundancy. A simple data analysis activity such as clustering customer info into one cell based on geographical location might otherwise be challenging, as each customer might spell or write the locality information a different way. OpenRefine can detect similarities to make clustering easy. It contains several clustering algorithms and makes quick work of an otherwise messy problem.

7. Qubole

Qubole simplifies, speeds up and scales big data analytics workloads against data stored in the cloud on AWS, Google or Azure. This tool takes the stress out of infrastructure wrangling. Once the IT policies are in place, any number of data analysts can collaborate and click to query with the power of Hive, Spark, Presto, and others. Qubole is an enterprise-level data processing engine, and its flexibility and accessibility set it apart from the rest.

8. Tableau

Some of Tableau's crucial benefits come from its advanced language and storage analytics database. It can help you easily translate data into meaningful business metrics. The online and server versions enable an entire team to build and work collectively with the data visualization tool. Tableau can connect to local or remote data in many different formats. Additionally, the Tableau engine can access live data for up-to-date visualizations or warehoused data for much more smoothly moving visualizations. Tableau Public's million-row limit provides a thriving platform for personal use, and the free trial is more than enough to explore the tool effectively.

Tableau 10 also has innovative technology for database connections called Query Fusion, which greatly simplifies queries by looking at all of the queries in the user's dashboard.

9. Chartio

Chartio lets you chain data sources and executes queries in your browser. You can generate potent dashboards in just a few clicks. Chartio’s visual query language lets you collect data from any source without having to know SQL or any other complicated model languages. It also lets you schedule PDF reports to be exported and emailed. The other significant feature about this tool is that in most formats it doesn’t require a data warehouse. This means that you can get up and running at a faster pace and that the cost of implementation is going to be lower and more predictable when compared to other options mentioned above.

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10. Blockspring

Blockspring is a distinctive tool due to the way it harnesses all of the capabilities of services such as If This Then That (IFTTT) and Zapier on popular platforms such as Excel and Google Sheets. You can connect to a wide array of third-party programs merely by writing a Google Sheet formula. You can post Tweets from a spreadsheet, track your followers and connect to AWS, Import.io, Tableau and more. Blockspring lets you create and share private functions, implement custom tags for enhanced search and discovery, and set API tokens for your whole organization at once.

11. Datapine

Datapine is one of the most sought-after business intelligence software with 4.8 ratings on Capterra and 4.6/5 stars on G2Crowd. It concentrates on putting basic yet powerful analytical features in the hands of beginners and expert users who want a reliable and quick online data analysis solution for all analysis phases.

Key Features of Datapine:

• Visual drag-and-drop interface for automatically building SQL queries, with the ability to switch to advanced (manual) SQL mode.
• Powerful predictive analytics capabilities, interactive charts and dashboards, and automatic reporting
• AI-enabled alarms that sound when an anomaly occurs or a goal is met

12. RapidMiner

RapidMiner, which Altair just purchased as part of its data analytics portfolio in 2022. It is a tool used by data scientists worldwide to prepare data, perform machine learning, and model functioning in over 40,000 enterprises that rely significantly on analytics in their operations. This data analytics tool is built on five leading platforms and three automated data science solutions that aid in designing and deploying analytics procedures by integrating the whole data science cycle.

Key Features of RapidMiner:

• A data science and machine learning platform with over 1,500 functions and algorithms.
• Interacting with Python and R and supporting database functions (for example, Oracle) is possible.
• Advanced and cutting-edge features for prescriptive and descriptive analytics.

13. SAS

SAS is an out-and-out data manipulation programming language and environment that is a market leader in analytics. The SAS Institute created it in 1966, and it was expanded upon in the 1980s and the 90s. This particular tool is simple to use and administer and can analyse data from any source. In 2011, SAS released a significant collection of solutions for customer intelligence and numerous other SAS modules for social media, online, and marketing analytics. These are now often used to profile clients and prospects. It can also forecast their actions and manage and improve communications.

Key Features of SAS:

• Develop reliable, accurate, and simple models to create using tried-and-true methodologies. Work with data to prepare it for self-service analysis or visualisation. Unify disparate datasets and display them in an easy-to-understand style.
• Using machine learning to display data narratives, make data linkages visible and understandable. Identify data trends using algorithms and pre-defined associated metrics.
• Discover and visualise trends with easy visuals, reports, dashboards, and geographical data shown on interactive maps, making them easier to explain, share, and comprehend.
• Make data-driven business decisions based on previous data. Use data trends and patterns to gain insights for forecasting, budgeting, and other business planning.

14. Apache Hadoop

Apache Hadoop is an open-source, free software framework for saving data and executing applications on commodity hardware clusters.

In 2005, Mike Cafarella and Doug Cutting cooperated to develop Hadoop. It was intended to be distributed for the Nutch search engine project, an open-source web crawler launched in 2002.

It's a software ecosystem that includes a framework. Hadoop's key components are the Hadoop Distributed File System (HDFS) and MapReduce. The software creates a distributed storage structure for massive data processing and employs the MapReduce programming approach.

Key Features of Apache Hadoop:

• It is free to use and provides a cost-effective storage option for organisations.
• Provides easy access through HDFS (Hadoop Distributed File System).
• Highly adaptable and straightforward to implement with MySQL and JSON.
• It is very scalable since it can divide a big quantity of data into little chunks.
• It runs on tiny commodity hardware such as JBOD or a collection of drives.

15. Xplenty

Xplenty is a cloud-powered ETL tool that enables easy data pipeline visualisation. These pipelines allow data to flow automatically from one source to another. Xplenty provides robust on-platform transformation capabilities for cleaning, normalising, and transforming data while following compliance best practices.

Key Features of Xplenty:

• Simple Data Transformations
• Simple workflow creation to define task dependencies
• REST API for accessing any data source
• Integrations from Salesforce to Salesforce
• Data security and compliance at the cutting edge
• Various data source and destination choices

16. Apache Storm

Apache Storm is a free and open-source extensive data processing system. Apache Storm is another Apache product that provides a real-time framework for data stream processing and may be used with any programming language. It provides a fault-tolerant, distributed, a real-time processing system that can compute in real-time. Storm scheduler spreads workload across several nodes based on topology and works well with The HDFS (Hadoop Distributed File System).

Key Features of Apache Storm:

• It can handle one million 100-byte messages per second per node.
• Storm guarantees that each unit of data will be processed at least once.
• Excellent horizontal scalability
• Integrated fault tolerance
• Auto-restart after an accident
• Clojure-written
• It is compatible with the Direct Acyclic Graph (DAG) topology.
• JSON files are used for output.
• It has several applications, including real-time analytics, log processing, ETL, continuous computing, distributed RPC, and machine learning.

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17. Cassandra

Apache Cassandra is a free and open-source database management solution developed by the Apache Software Foundation in 2008. Apache Cassandra is distributed and uses NoSQL methodologies. The execution of data management entails the usage of cluster forms, which are linkages to several nodes across various data centres. Apache Cassandra is a 'column-oriented database' in NoSQL.

Its principal application is in large applications requiring real-time data, such as sensor devices and social networking platforms. Cassandra also has a decentralised design, which means that function modules such as data partitioning, failure management, replication, and scalability are unique and work in a cycle. More information is available in the Apache Cassandra documentation.

Key Features of Apache Cassandra:

• Capability to operate on less powerful gear.
• Cassandra architecture is based on Amazon's Dynamo and implements a key-value database system.
• High application scalability and distributed deployment
• System fault tolerance and decentralisation
• Apache Cassandra is capable of doing quick read/write operations.
• MapReduce support and tunable consistency
• Cassandra Query Language

18. Data Pine

Data Pine is a data analytics tool that allows users to monitor and analyze their data in real-time. It offers various features such as customizable dashboards, alerts, and data visualization tools. Data Pine supports multiple data sources, including databases, APIs, and cloud services. The tool also provides advanced analytics capabilities such as predictive modeling and machine learning algorithms. Users can create custom queries and reports and share data insights with others in their organization. Data Pine is a comprehensive data analytics solution that helps businesses make data-driven decisions.

Key Features of Data Pine:

• Real-time data monitoring and analysis
• Customizable dashboards with drag-and-drop widgets
• Alert notifications for important events or anomalies
• Data visualization tools for creating charts, graphs, and tables
• Support for multiple data sources, including databases, APIs, and cloud services
• Custom queries and reports with SQL and JSON syntax
• Integration with popular business intelligence tools such as Tableau and Power BI
• Advanced analytics capabilities such as predictive modeling and machine learning algorithms
• Collaboration features for sharing data insights with others in the organization
• Role-based access controls and data security measures to protect sensitive information.

19. Rapid Miner

RapidMiner is an open-source data analytics tool that provides a comprehensive platform for data preparation, machine learning, and predictive modeling. It offers a drag-and-drop interface for building analytical workflows without the need for programming skills. RapidMiner can handle various data types, including structured, unstructured, and semi-structured data. It also supports a wide range of data sources, such as databases, cloud services, and big data platforms. The tool includes numerous built-in machine learning algorithms, statistical models, and data visualization tools to help users gain insights from their data. Overall, RapidMiner is a powerful and user-friendly tool that enables organizations to extract value from their data quickly and efficiently.

Key Features of Rapid Miner:

• Drag-and-drop interface for building analytical workflows
• Open-source platform with a large community of users and developers
• Support for various data types, including structured, unstructured, and semi-structured data
• Integration with a wide range of data sources, such as databases, cloud services, and big data platforms
• Built-in machine learning algorithms and statistical models for data analysis and predictive modeling
• Data visualization tools for creating charts, graphs, and reports
• Automated model building and evaluation with the Auto Model feature
• Collaboration features for sharing workflows and results with others in the organization
• Scalable architecture for handling large data sets and distributed computing
• Comprehensive documentation and tutorials to help users get started quickly.

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