Today, data is the bread and butter of the IT world. We feed the huge amounts of data to the analytics tool and expect information and insights in return. These insights and information help us take business decisions and predict future outcomes of several new processes, products, and procedures.
Here, we are talking of megabytes of data that the analytics tool processes and then gives us valuable information.
Hence, looking at the importance of data, this article will discuss the best big data analytics tools that you can use. Without these tools, the raw data is meaningless. So, let’s dive in and explore.
- Apache Hadoop is the most popular tool for clustered file system and big data analytics. It is a java-based open-source tool that uses MapReduce programming model for data analytics. You can use the Hadoop Distributed File System (HDFS) for holding different data types such as images, XML, videos, JSON, etc.
- Apache Cassandra is another great product that is developed to manage and interpret high volumes of data across several servers. The tool uses Cassandra Structure Language to communicate with the database. Cassandra has no point of failure and it uses a log structure for storage.
- CHD or Cloudera Distribution for Hadoop is an open-source deployment that offers platform distribution of Hadoop, Impala, Spark, etc.
- If you are looking for simple ETL operations, KNIME is your tool. Using this tool, you can integrate, report, research, mine, and analyze data. You can also use the tool for CRM and business intelligence.
- Datawrapper is a platform for simple visualization. Users who wish to generate quick and precise charts, Datawrapper is the best tool for it.
- For startups just stepping foot in data analytics, Lumify is the tool. Using this free tool, you can analyze, visualize, and integrate data. Its primary features are 2D visualizations, 3D visualizations, full-text search, geographical layouts, automatic layouts, multimedia analysis, etc.
- Apache Storm is utilized for fault-tolerant, distributed stream processing. This framework offers reliability, real-time analytics, ETL processes, log processing, continuous computation, and machine learning analytics.
- If your goal is to predict the future outcomes, then utilize Rapidminer. This tool helps with machine learning, data science, and predictive analysis. You can use it for advanced level insights of the future.
- We have all heard of Tableau as this tool is widely utilized for business analytics and big data. The tool specifically has razor-sharp speed, no-code queries, and advanced visualization processing.
- Just like Python and C, R is for statistical data miners. Using this tool, you can analyze, manipulate, and graphically display data. You can also perform an advanced statistical calculation with this language.
It is likely that you may get confused with so many options. Simply analyze the features of each tool, assess your requirements, and utilize the one that suits your needs. However, don’t forget to wisely implement your big data tool as it can help you improve the execution of processes and overall productivity.
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