Big Data Software

Any business looking for big data analytics software should not have a hard time finding a vendor. There is no shortage of vendors selling this type of software. Organizations will notice that each product does have its own functionality, there's no real way to differentiate the products based solely on functionality. This is due to the fact that the products have many of the same capabilities and features. Also, the differences in the software tools are too minor to notice. Having said this, differentiating between the various software should come down to mature analytics, the software's cost, ease of use, and the sophistication of its algorithms.

This article's goal is to help vendors understand the difference between the products. It will examine products from several vendors that provide big data analytics software. The nine vendors getting analyzed include Teradata, SAP, Oracle, Microsoft, Alteryx, IBM, KNIME.com, RapidMiner, and SAS. Again, these products while they may seem similar in functionality but they do have their differences. Some of the products mentioned have more than one tool. This article features a group of vendors that highlight the big data analytics markets' various aspects. By comparing and contrasting these products, businesses are able to understand how these products can meet the needs and goals of the organization.

Consider Analyst Expertise and Skills When Selecting a Big Data Analytics Software

Some of these tools are engineered specifically for users who are new to data analytics, while other tools are designed for those who are expert-level data analysts. There are also a variety of tools suitable for use by experts and novices.

Products like IBM's SPSS Modeler, Oracle Advanced Analytics, RapidMiner's tools, and the SAP Predictive Analytics' Automated Analytics version are designed for beginners. Its features are truly designed for the person who knows nothing or has very little knowledge, in data analysis or statistics. These users will be able to use the tool to create statistical models, analyze data, and design analytic workflows with very little, or no, knowledge of coding. Each of these vendors combines their program's core elements with an interface that is intuitive. The combination of these features facilitates the analyst's progress through data preparation, the analysis of data, and the design of the model and validation. The approach taken by each software vendor may be different. These differences become evident when comparing standalone products (like RapidMiner) to vendor products that are a part of a larger suite (products offered by Oracle).

KNIME Analytics Platform, Microsoft Revolution Analytics, IBM SPSS Statistics, Teradata's Aster Discovery Platform, and Microsoft Revolution Analytics are tools that provide the functionality that experienced users expect to see. Oracle's R Advanced Analytics for Hadoop (ORAAH), is a part of Oracle's Big Data Software Connectors software suite. This tool provides an R interface that allows the manipulation of Hadoop's Distributed Files System data. It also lets users manipulate R's writing and data mapper. The tool also allows the manipulation of reducer functions. The amount of flexibility offered by these tools is appealing to advanced data scientists.

The functionality of SAS Enterprise Miner and Alteryx adapts to meet the level of expertise of the individual user. Because of this, they are beneficial to advanced users and those who are new to using them. IBM's SPSS and SAS Enterprise Miner's tool really stand out because they support advanced analytical methods and applying data to models. These tools also provide a greater array of analysis functions like association analysis, visualization capabilities, and neural networks.

Analytical Diversity

Depending on the organization's use and how they apply these tools, users will need to support a variety of analytic capabilities that use a particular type of modeling (ex. segmentation, decision trees, clustering, regression, and behavior modeling). While there is widespread support for the different types of high-level analytical modeling. Vendors have spent decades updating their algorithms and increasing the complexity of their functionality. It's very important that businesses know which models are a relevant business solution. Organizations also need to determine which products will best serve the needs of their business.

The more established and higher-end (also, more likely to be higher-priced) tools give users the greatest analytical range. Oracle Data Miner has several reputable machine learning approaches that are designed to support predictive mining, clustering, and text mining. The two additions of the SPSS products, by IBM, offer a unique group of analytical models and techniques. SAS Enterprise Miner also supports several techniques and algorithms that include time series, decision trees, market basket analysis, neural network, logical and linear regression, link analysis, Web path and sequence analysis.

The new generation of tools are less expensive and support different types of models. However, their level of algorithmic sophistication is limited. Alteryx Analytics Gallery's model inventory has the following capabilities: time series and classification analysis, regression analysis, decision trees, and association rule analysis. KNIME's capabilities include time series analysis, image mining, and methods of text mining. KNIME also incorporates machine learning algorithms that are derived from different open source frameworks like JFreeChart and Weka R.

Analytical diversity also involves the integration of statistical tools and programming languages, like R for integrating functionality, as defined by the user, and existing libraries. Analytical diversity also integrates the libraries that currently exist as user-defined functionality. SAS Enterprise Miner, Alteryx Designer, Teradata's Aster Discovery Platform, Microsoft's Revolution Analytics, KNIME's Analytics Platform and ORAAH from Oracle all have support and interface integration with R.

The Type of Data Getting Analyzed

There are several dimensions to consider when speaking about the scope of data getting analyzed. This includes the access to on-premises data warehouses, cloud-based data sources, data managed on larger platforms like Hadoop, and unstructured vs. structured information. However, there are several levels of support for managing data within unconventional data repositories. These data lakes are managed inside Hadoop or within a different NoSQL data managing system that is designed to provide horizontal scaling. Making the distinction among products really depends on the organization's rules regarding how it wants to access and process data variety and volume.

Supporting Scalability and High-Level Performance

The data volume and need for analysis will determine an organization's needs for scalable performance. There is a good chance that smaller organizations will not have the same requirements. Small organizations that do not have large amounts of data should notice that this product performs well even without the performance features that are able to scale with the organization's resources. This includes entry-level editions of lower-end tools like Alteryx Designer, Microsoft Revolution R Open, KNIME, and RapidMiner. These tools have the ability to run on a desktop system and will not require any additional server components.

Large organizations will have a considerable amount of data sets they need to analyze, these organizations will also have a large number of users. These two facts mean that organizations will have additional requirements. Organizations will need tools that provide a high level of performance and can facilitate collaboration. Product adaptability to high-performance structures is a good sign of the tool's scalability. The majority of these products are also adaptable to Hadoop's parallelism or can use another way to achieve a quicker computation.

Each one of these products is able, to a certain extent, to provide support for Hadoop. The products that support Hadoop include the following tools: RapidMiner's Radoop, SPSS Statistics, IBM SPSS Modeler, Oracle's Big Data Discovery, Cluster Execution add-ins, and Big Data Extension by KNIME. ORRAH tools are also able to provide a degree of support for Hadoop. The Teradata Aster Discovery Platform tackles high-performance requirements using Teradata's MPP architecture. Expert Analytics' edition of SAP's Predictive Analytics product can perform in-memory data mining to handle the analysis of large-volume data. Microsoft R Enterprise uses the ScaleR module of Revolution Analytics, a repository of big data analytics algorithms that facilitates parallelization. Scoring algorithms that are put into effect using SAS Enterprise Miner may be utilized and carried out in Hadoop's environment.

Intra-Organization Collaboration

As previously stated, the bigger an organization, the more likely the organization will need to share analysis, applications, and models among various groups and analysts. Organizations with many analysts that are distributed across the company may have a greater need to find ways to share models and collaborate in regards to the interpretation of these models. RapidMiner's Server product gives users the necessary support to share and collaborate while the Gold edition of IBM's SPSS Modeler provides users with collaboration capabilities. KNIME provides commercial extensions that facilitate team collaboration. Alteryx Analytics Gallery gives organizations a means to share sophisticated analytics applications in the cloud with team members who are dispersed throughout the organization. The client-server architecture of SAS Enterprise Miner let data analysts and business users work together by allowing them to share models and different types of work products.

Product Integration and Vendor Size

Vendors are often compared by their size. It is easy to contrast what is considered mega-vendors with big data tools that are one component of a rather large tool portfolio. Larger organizations tend to negotiate a site-wide, enterprise licenses that give them access to the full suite of the vendor's tools. Organizations that seek this type of arrangement will more than likely prefer to use mega-vendors like SAS, SAP, Oracle, and IBM.

Large vendors require tools for big data analytics that are a part of a bigger tool suite. It’s safe to assume that the products offered by mega-vendors are fully, or in part, integrated and designed to work together. Also, some people may have a greater degree of comfort when working with a larger vendor. People tend to be more comfortable using large vendors because they expect a level of stability. There is also an expectation of receiving a consistent customer service experience. However, big data analytics tools may be a part of a larger software licensing arrangement.

Small vendors, like RapidMiner, Altered, and KNIME, derive their revenues primarily from the licensing and supporting a limited number of big data analytics products. Working with small vendors does have its benefits. The customers of small vendors may find that they are able to develop a closer relationship with a vendor's product management and their innovation teams. Also, organizations may have the ability to influence the products roadmap or increased functionality. Small vendors may also offer users more leeway as far as pricing and what features they want to have in the licensing arrangement. Organizations should understand that there are potential risks associated with working with small vendors. There is the possibility of dealing with stability issues, a chance that the company could be acquired by a larger one, and these vendors may have limited availability of support resources. All of these factors could affect the relationship consumers have with a vendor.

Budget for Licensing and Maintenance

Every big data analytics vendor offers different editions or versions of their products. Often the differences in these versions are evident when analyzing the price range of the different additions. The cost of products and the cost to acquire the products and the cost of operating them. Teradata, IBM, RapidMiner, Microsoft, and Oracle sell editions of the products that have different tiers. The licensing costs are affected by the tool's capabilities, features, the number of processing devices the product is able to use, or the number of limitations in regards to the amount of data that is getting analyzed. RapidMiner and KNIME do offer free and open source versions of the products. There is a charge for the versions that support enterprise-level applications or support services. The costs of RapidMiner, Alteryx, and KNIME do offer lower-priced options for organizations that do not have a large number of users. Anyone thinking about using SAP or SAS should contact the companies to find out their pricing alternatives.