Here’s the best data archiving, data extraction, analytics, and visualizations tools
It’s a world based on the data that we’re living in and that data is increasing by the number of personnel. So much so that it rapidly changes our lives and organizations around the world are adjusting and adapting to this enormous amount of information.
From innovative storage technologies to IoT deployments and new EU GDPR laws, big data is driving industry change. Big data is a challenge for even the largest organizations, who can no longer ignore the tremendous potential that it has to improve business decisions, reach customers with greater accuracy, and simplify business processes.
To use big data with its full potential, companies need the right tools to process, analyze, and store important information they create and collect every day for real-time results.
The four key elements of any big data project are data storage, data mining, data analytics, and data visualizations, and each has a number of innovative tools and technologies offered to businesses.
Here we have listed the best tools for your big data projects.
Data storageFor big data projects, cloud-based storage tools are important for maximizing the amount of information you can store. Cloud storage options allow you to store data in a safe and easy-to-access manner, for ease of use.
Here are our three heads:
HadoopHadoop is an open-source platform, specially designed to store very large datasets using clusters. It supports both structured and non-structured data and is easy to scale, so it’s good for organizations that are likely to need more capacity without much notice. It can also handle a large number of tasks without any lag. This is a great option for organizations that have developer resources to deploy Java, but it requires some effort to launch and run.
MongoDBMongoDB is useful for organizations that use a combination of semi-structured and non-structured data. For example, these can be organizations that develop mobile applications, which organizations need to store data related to the product catalog or data used for real-time personalization.
RainStorRather than just storing big data, Rainstor compresses and copying data, providing up to 40:1 storage savings. It does not take any data sets in the process, making it a great option if an organization wants to take advantage of storage savings. Rainstorm inherently available for Hadoop and uses SQL for data management.
Data miningOnce you’ve stored your data, you’ll need to add some tools to find the information you want to analyze or visualize. Our three top tools will help you extract the data you need without the hassle of manually tracking all (a task that humans can’t do if you hold thousands or more records).
IBM SPSS Stylist
The IBM SPSS Model Builder can be used to construct predictive models using its intuitive interface rather than through programming. It includes text analytics, entity analysis, decision management, and optimization, and enables the extraction of both structured and non-structured data across the entire dataset.
KNIME is an extensible open-source solution with over 1,000 modules to help data scientists harness new insights, make predictions, and discover key points from data. Text files, databases, documents, images, networks, and even Hadoop-based data can be read, making it a perfect solution if the types of data are mixed. It has a huge array of algorithms and huge community contributions to provide a full set of data extraction and analysis tools.
RapidMiner is an open-source data mining tool that allows customers to use templates instead of having to write code. This makes it an attractive option for organizations without specific resources or if they are only looking for a tool to start data mining. The free version is also available, although it is limited to 1 logical processor and 10,000 rows of data. The tool also provides environments for machine learning, text extraction, predictive analytics, and business analytics to help the whole process.
Data analysisHave the data you need? Now it’s time to find the strongest tools to help you analyze it in order to gather key insights about your business, your customers, or the wider world. Here, we fulfill our favorite data analysis tools.
Apache Spark is probably one of the most well-known big data analysis tools, built with big data at the top of information transmission. It’s open-source, fast, efficient, and works with all major data languages including Java, Scala, Python, R, and SQL.
This is also one of the most widely used data analysis tools and is used by companies of scale, from small businesses to public sector organizations and technology-related technologies such as Apple, Facebook, IBM, and Microsoft.
Apache Spark conducts a one-step analysis, allowing developers to use large-scale SQL, batch processing, stream processing, and machine learning in one place, in addition to the chart processing.
Apache Spark is also super-versatile, running on Hadoop (because it was originally developed), Apache Mesos, Kubernetes, itself an independent platform or in the cloud, making it suitable for businesses of all scales and in all areas.
Like Apache Spark, Presto is an open-source tool, using distributed SQL queries, designed to run data-driven queries as a powerful interactive analytics tool. It supports both non-related sources, such as Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase, plus relational data sources such as MySQL, PostgreQuery, Amazon Redshift, Microsoft SQL Server, and Teradata, make it a useful tool for businesses to operate both types of databases.
It is also used by large corporations like Facebook. In fact, the Social network is the main contributor to its development, although Netflix, Airbnb, and Groupon are also involved in its development to turn it into one of the most powerful data analysis tools around.
Analyzing data is only one aspect of the SAP HANA platform, but this is a very good feature. Supporting text, space, graph, and string data from one place, SAP HANA integrates with Hadoop, R and SAS to help businesses make quick decisions based on invaluable data insights.
Tableau combines analytics and data visualizations and can be used on desktops, through servers or online. The online version is a huge focus on collaboration, which means you can easily share your discoveries with anyone else in your organization. Interactive visualization makes it easy for anyone to understand the information and with the full hosted option of Tableau Cloud, you won’t need any resources to configure the server, manage software upgrades, or expand hardware storage.
Designed to run on the Apaches Hadoop framework, Splunk’s Hunk is a fully equipped data analysis tool that can create visual charts and visualizations of the data provided, all of which can be managed via the dashboard. Queries can be made against raw data through the look of Hunk, while charts, charts, and dashboards can quickly be created and shared through the look of Hunk. It also works across databases and other stores, including Amazon EMR, Cloudera CDH, and Hotronworks Data Platform among others.
Data visualizationsNot everyone has experience in getting key insights from the list of data points or understanding their meanings. The best way to present your data is by turning it into data visualizations so that everyone can understand its meaning. Here are our top data visualization tools.
DataHero is a simple visual tool to use, which can suck data from various cloud services and put them into charts and dashboards to help the whole business understand deeper insights. Because there is no coding required, it is suitable for use by organizations that do not have resident data scientists.
With the ability set provisioned, QlikView allows users to visualize data from all data sources using self-service tools that eliminate the need for complex data models. Simple visualizations served by QlikView run on the company’s own analysis platform, which can be shared with others to decide whether to follow data trends disclosed that can cooperate. More advanced capabilities allow the visual analysis of QlikView to be embedded in applications, while the console can guide people through the creation of analysis reports without the need for understanding of data science.