8 Best Big Data Analytics Tools. Ultimate Guide

January 11, 2021
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Big data analytics tools make it possible to transform mass amounts of data into meaningful insights for better decision-making. There are a variety of big data tools and technologies on the market, so we’ve compiled eight of the best for comparison’s sake. 

What are Big Data Tools? 

Big data tools take large data sets and digest them to spot trends, anomalies and patterns through data analytics. The tools also transform such information into visualizations, dashboards and reports. Depending on the tool, you may have the option to customise reports and graphs from data sets. 

Furthermore, many tools will leverage the power of machine learning so that anyone, even those without technical expertise, can query data simply. 

Best Big Data Analytics Tools 

Amidst the variety of data processing tools on the market, here’s a look at eight of the most popular solutions. These tools are helping to transform organisations of all sizes and move the needle for finance transformation to occur. 

1. SolveXia:

SolveXia is a system designed to enable finance teams and businesses with data analytics and a scalable way to connect all data sources to glean insights. The process automation tool eliminates disconnected data and automatically processes data in the cloud. 

SolveXia can automatically provide real-time dashboards and reports with alerts via email or SMS. The easy-to-use interface is a no/low code solution as users can leverage drag-and-drop features to design and run processes. Furthermore, audit trails document all actions taken within the system and bank-grade security mitigates risk on key person dependencies and compliance. The powerful system can process data, regardless of format, to be cleansed, transformed and reported - offering end-to-end automation abilities.   

2. Tableau:

Tableau offers a product suite that supports data management and data analytics. The core of the product is a data visualisation tool, and as such, it delivers. Some of its main advantages include: quick insights, good user experience and powerful computation. There are drag and drop features to create the exact graphs and charts you could need. 

Users have the option to incorporate their own coding, or to simply use the power of the tool without any need for coding. On the downside, there is no functionality to automatically refresh reports or schedule reports. Importantly, since the tool is aimed at data visualisation, it’s not the strongest in terms of data reprocessing. This means that you’ll need to invest time into data cleansing before moving it into the system. 

3. Microsoft Power BI:

Microsoft’s business intelligence tool pulls together various software to provide the ability for data importation, aggregation, and visualisation. It’s regarded to be one of the top tools for data analysis for non-technical business users. Some of the features that make it so competitive include: the ability to ingest data in various structures, connect to different sources, integrate with Azure data services, and provide real-time data analytics. 

It does offer the option for scheduled reporting or on an as-needed basis. On the other hand, some users feel that Microsoft Power BI has a clunky interface that is too crowded with options. Furthermore, the system can only process 2 GBs of data at a time and will require a data model if you have complex relationships between tables. 

4. Integrate.io:

Integrate.io offers a cloud-based data integration platform that is commonly used by industries like hospitality, advertising and retail. Integrate.io boasts ETL, job process tracking and data transformations. It’s known for its easy user interface, ETL capability, third-party integrations and scheduling. You can use it to schedule jobs and track their progress, as well as to gain marketing analytics insights. 

Many teams integrate the tool with MailChimp, Salesforce, Hubspot, Google AdWords and Facebook ads, to name a few. However, to use the tool well, it may require deep knowledge of big data processing and there is some complexity when it comes to integrating platforms. 

5. Zoho Analytics:

For businesses looking to stick within a budget ($25/month for the basic plan for two users), Zoho Analytics can provide simple report generation and analytics through its business intelligence tool. But, given the low price comes low functionality, as there are limits to how many data imports and query tables you can have. 

For enterprise solutions, you’ll need to scale up. Users like the tool for its value for money, good customer support and ease of use. However, there could be a steep learning curve for deployment and may require a developer to code specific reports and set up. 

6. SAS Visual:

SAS stands for Statistical Analysis Systems and is designed to provide advanced analytics and data management for businesses. SAS can be used for business intelligence, predictive analytics and multivariate analysis. It’s easy to learn for anyone without programming understanding because coding is in basic statements. It has the power to deal with big databases

If the system encounters an error, it’s generally simple to solve because the log window explains the malfunction in plain language. On the cons list sits the fact that it’s not open-source, it has a high cost and the graphic representation of SAS is not as effective as R. 

7. Apache Cassandra:

Apache Cassandra is a peer-to-peer open source database, also known as a NoQL database. It was developed at Facebook and then released as open source. Now, it works as a peer-to-peer network. Some of its benefits are that it is highly scalable and decentralised. This makes it a great solution for disaster recovery and multiple data-center deployment. However, all the flexibility has its drawbacks, like the fact there are limited querying options to retrieve data and limited ways to sort data (just predefined ways). 

8. Sisense:

Sisense offers a cloud-based or on-premise solution for business intelligence. It is equipped to manage large datasets through a columnar database approach (as opposed to rows). Users can take snapshots of data and the dashboards are said to be well-designed with various chart types and metrics views. Despite its pros, some users find it to be hard to navigate and find that it requires a fair share of technical knowledge to use properly. 

Often, users have to write their own SQL code. Its share of computing capabilities means that it is a heavy application, requiring a lot of server power and space. Plus, user adoption takes some time. Lastly, the dashboards are useful, but they only work with real-time web access, which means there is no option to schedule and automate reports for when users are offline. 

Buying Guide to Best Big Data Analytics Tools

Implementing any new technological software within a team of any size requires careful consideration. When it comes to big data and analytics requirements, you’ll want to make sure that the tool is able to provide statistical analysis and be able to help you understand how one parameter can affect data outcomes. The ability to forecast and test before making decisions can greatly improve your organisation’s ability to remain agile and adaptive. It’s also beneficial to pick a tool that automates processes to help avoid bottlenecks and key person dependencies. 

Furthermore, the best tools can analyse individual patterns and behaviors of customers so that you can tailor your products and services to customer needs. With analytics tools like SolveXia, you can not only customise executive level dashboards and schedule real-time reporting, but you will also be able to meet customers’ needs by automating business processes.

What Factors to Consider When Choosing Big Data Tools?

Besides focusing on the analytical capabilities of a big data tool, you’ll have to think about pricing, scalability and requirements. Keep in mind the following when shopping for the software that your business needs:

  • Product cost 
  • The quality of customer support 
  • How much training is required for your team 
  • The software and hardware requirements and resources to run the tool 
  • Security measures 
  • User interface and learning curve 
  • Visualisation options and customisation 
  • Automation capabilities and report scheduling 
  • Vendor reputation and reviews 

Wrap Up 

Automation and big data processing tools are saturating the market. Take your time to conduct research before signing on with any vendor. But, if you’re looking for a powerful, affordable and completely transformative option for data analytics an automation tool like SolveXia could be a great choice and opens the door to many new possibilities!


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