Big data analytics is a critical piece of any business workflow in this digital age. It has become much easier to collect and access data that allows you to optimize your overall business performance. With this information, you can uncover current market trends, identify customer preferences, make smarter business decisions, find additional ways to improve your efficiency, and that’s just the tip of the iceberg.
But there is a problem. The collection and processing of such massive volumes of data can chew up a large portion of your hands-on productive hours. To quicken the pace of this process, data analytics programs should be utilized. Through the use of artificial intelligence (AI) and sophisticated algorithms, these solutions can help you can transform your raw data into actionable insights in just a few mouse clicks.
There are plenty of factors involved, however, in finding the right analytics tool(s) to meet the unique needs of your particular organization. Not only do your specific needs need to be identified, but you then must evaluate those needs properly against the features of each potential solution. With so many choices and so many factors to be considered, the whole process can be overwhelming.
Let’s first explore a few general features and attributes that should be considered when assessing how well a big data analytics tool can meet your organization’s needs.
Integration Difficulty and Convenience Level
Big data analytics applications rely on structured and unstructured data received from a massive number of internal and external data sources. This underscores a need for the tool to be functional, but also to support data accessibility and systems integration. Here are a few features to think about:
- Big data accessibility:
Compare how the tool connects to big data architectures, as well as how it manages storage.
- Blending with your existing platform:
If there is an expectation that the big data analytics tool will merge with existing data management tools, practices and methodologies, you must consider how well the prospective analytics tool will operate in conjunction with them.
- Data Utilization:
Verify that the tool will be able to ingest and make sense of the emails, images, videos, social media streams and other unstructured data.
How Easy is it to Use?
Focus your evaluation on how easy the product is for your team to use for data analysis and verify the efficiency and accuracy of the models. Consider the following:
- Use Case Deployment:
Often, the same methods can be applied in many different business scenarios. If your organization is considering broader use cases, you may look at adopting tools with greater modeling flexibility.
Check to ensure the product offers visual techniques that enable effective development and analytics uses.
Ensure that the big data analytics tool and platform enables your analysts to work together to refine their applications while improving the re-usability of models to increase workflow consistency.
Other General Considerations
If a high level of execution performance is a business requirement, it’s critical to consider products that are engineered to provide the necessary performance configurations.
- Special Services:
Evaluate whether it would be necessary to get help with installation and training from the vendor or to provide specialty development services.
Prices of products influence a buying decision in almost every applicable case. Some big data analytics tools are quite costly while other tools cost very little or, in some cases, are free.
While you should keep these general features in mind, there are many others that, depending on your unique business needs, may need to be taken into consideration as you step through your evaluations of each potential analytics tool.
To help with your big data analytics journey, here is a list of some of the leading products available on the market:
Celebrated for its capabilities for large-scale data processing, Apache Hadoop is a good place to begin for distributed storage and handling of large datasets. Additionally, this open source platform offers services for data governance, security, access and operations and can run on-premises or in the cloud. It’s a highly scalable platform that stores, processes and analyzes data with low requirements for hardware.
- Hadoop Distributed File System:
Adapted to working with large-scale bandwidth.
- Access and Analysis:
Analysts are able to choose their preferred tools with the ability to interact with data using SQL or NoSQL.
Data can be kept in structured, semi-structured or un-structured formats, analyzed and applied when needed.
Apache Spark is an open-source big data analytics platform that can process both batch data and real-time data while supporting a unified analytics engine for machine learning and big data. It offers lightning-fast processing while offering support for sophisticated analytics with the ability to integrate with Hadoop and existing Hadoop data.
- Data Processing:
Analysts can fulfill streaming, machine learning and SQL assignments in development APIs requiring quick access to information and datasets.
- Ease of Use:
Offers an extensive gathering of over 100 data transformation operators and well-known data-frame APIs for semi-structured data use.
Comes with support for SQL queries, machine learning and graph processing and provides the ability to craft and unite complex workflows.
Apache Storm is a real-time framework for data-stream management that can cooperate with any programming language. Storm scheduler equalizes the workload between various nodes based on topology arrangement and works well with the Hadoop Distributed File System. This Clojure-written Apache product will auto-restart upon crashes and possesses tremendous horizontal scalability.
- Built-in fault tolerance
- Works well with Direct Acyclic Graph (DAG) topology
- JSON formatted output files
Apache Cassandra provides organizations with the ability to process structured data sets that are distributed across a massive number of connections around the world. Due to its distinctive architecture without solo points of failure, it operates comfortably under heavy workloads and features unique capabilities no other NoSQL or interactive database has. It has immense direct scalability with a high fault tolerance and built-in elevated availability.
Operations are more straightforward due to the use of a simple query language.
- Addition and Removal:
Uncomplicated process for addition and removal of nodes from a running cluster.
Offers constant replication across nodes.
An open-source stream-processing platform, Apache Kafka seeks to deliver a cohesive, high-throughput, low-latency platform for managing real-time data. It lets users subscribe and publish data to a variety of systems and real-time applications, making it highly valuable for organizational infrastructures to process streaming data. Kafka stores critical-value messages which are indexed and stockpiled with a timestamp and its architecture allows it to provide tremendous streams of messages in a fault-tolerant manner.
- High Throughput:
Manages high-velocity and high-volume data without large hardware requirements.
Able to support message throughput of thousands of messages per second, with very low latency.
- Fault Tolerant:
Capability to be resistant to node or machine failure within a cluster.
Apache SAMOA focusses on constructing dispersed streaming algorithms for effective big data mining. It provides an assortment of distributed algorithms and is built with pluggable architecture that must be used atop other Apache products. Its additional features utilized for machine learning include clustering, normalization, regression, classification and the arranging and programming of primitives for developing custom algorithms.
Existing infrastructure can be used for new projects or developments.
- No Backups:
There is no need for time-consuming updates or backups.
- Limited Downtime:
There is no reboot or deployment downtime.
R is generally used for supporting wide-scale statistical analysis and data visualization, along with big data analysis. R can effortlessly scale from a solitary test machine to vast Hadoop data lakes. It also provides a broad assortment of statistical tests, can run inside the SQL server, on both Windows and Linux servers and supports Apache Hadoop and Spark. Additional packages include big data support, connecting to external databases, mapping data geographically, performing advanced statistical functions and visualizing data.
Provides a logical, integrated group of big data tools for data analysis.
Offers a collection of operators for calculations on arrays.
- Easy Analysis:
Delivers graphical capabilities for data analysis which show either on a screen or on a hardcopy.
Talend is a big data tool that automates big data integration and makes it simpler. It allows for leading data management, examines data quality and its graphical wizard also produces native code to simplify the big data platform. It connects at big data scale, from ground to cloud and batch to streaming, data or application integration.
Streamlines all the DevOps processes and utilizes Agile DevOps to speed up big data projects.
Delivers smarter data quality with machine learning and natural language processing.
Provides accelerated time-to-value for big data projects.
Sisense is a data analytics software that provides high-level analytical tools for analysis, visualization and reporting. It makes business data analytics simpler through its set of tools and features, such as personalized dashboards, analytical capabilities and interactive visualizations. Sisense allows businesses to merge data from many sources into a single database where analysis is completed and can be deployed on-premises or hosted in the cloud.
Offers a wide variety of data visualization resources, including an option to get recommendations on how best to view data, or submit open source designs.
Sisense utilizes Natural Language Detection technology for easy trend and pattern detection.
- Irregularity Detection:
The system utilizes machine learning to instantly detect abnormalities in data and provide reports on potential issues.
Domo is a browser-accessible data analytics solution that scales from small business to large enterprise and means to deliver a digitally connected environment for your data, people and systems. It utilizes real-time data refresh and drag-and-drop data preparation capabilities to provide analysis on business activities such as product sales, marketing return, forecasting and more. Furthermore, it offers interactive visualization tools and instant access to company-wide information through customizable dashboards.
- Instant Insight:
It includes more than 300 interactive dashboards and charts that can be accessed anywhere, anytime.
Data can be correlated from any third-party source, such as cloud, on-premises and other proprietary systems.
Intuitively manage responsibilities in real-time utilizing mobile applications designed for on-the-go usage.
Qlik provides the ability to create visualizations, dashboards and applications to gain business-critical insight from an organization’s data. It catalogs every possible relationship between data and unites them from numerous sources into a centralized view to extract meaningful insights commonly overlooked by other query-based analytics tools. Because the system enables work collaboration in a secure, unified hub, insights can easily be shared regardless of organizational size.
Collaborate, explore and create analysis from any device once the analytics application is available.
Make selections through a fully interactive interface to investigate and locate information effectively.
Update analytics immediately with each click, ensuring the most up-to-date information is presented with limitless exploration and investigation.
RapidMiner is a data science platform made for analytic teams to organize data, construct predictive models and deploy them in a single environment, considerably improving efficiency and decreasing time-to-value for data science projects. It offers technology for working in many stages of sophisticated analytic projects through a collection of solutions including application integration, data transformation and machine learning. It also provides true predictive analytics as well as a unified methodology for streamlining maintenance and standardization of critical data-related processes.
- Single Pain of Glass:
A single platform, user interface, and system for complete workflow management.
- Open Source:
Established connections with structured, un-structured and big data with well-recognized open languages and technology, it takes care of changing data-science needs.
- Easy to Learn:
Delivers easy and usable navigation with drag-and-drop methodology to quicken data science processes and increase productivity.
MongoDB is an open source NoSQL database with features for cross-platform compatibility with many programming languages. MongoDB can be utilized in a variety of cloud computing and monitoring solutions and stores any type of data, from text and integer to strings, arrays, dates and Boolean.
Delivers cloud-native deployment and great flexibility of configuration.
- Cost savings:
Significant cost savings, as dynamic design enables data processing on the go.
- Easy Categorization:
Capable of subdividing data across multiple connections, data centers and sources.
Neo4j is an open-source graph database that utilizes cypher-graph query language and follows the key-value pattern in storing data, which uses an array of keys whereby each key is associated with only one value, and a unified-node relationship. It has high availability and scalability and performs well under heavy workload of network data and graph-related requests.
Integrates well with a variety of other databases.
- Support on Demand:
Offers built-in support for ACID (Atomicity, Consistency, Isolation, Durability) transactions.
Delivers a high level of flexibility due to the absence of mandated plans.
IBM Analytics provides data-driven, evidence-based insights to support better business decision-making. It simplifies how data is collected and analyzed by optimizing data management and scalability while delivering the means to collect data from a variety of sources. It also provides the ability to analyze data in a smarter way by scaling insights and integrating those evidence-based insights into decisions that were formerly unavailable.
Through the use of machine learning, the completion of data projects can be accelerated by maximizing intelligence into applications.
Identify patterns in data through predictive analytics and anticipate what’s most likely to happen next.
Uncover the best course of action through prescriptive analytics according to design, plan, schedule and configuration.
IBM Cognos is a web-based interface that provides data visualization to help make successful business decisions quickly through smart self-service means. It offers self-service analytics with security, data governance and management features and can be used in the cloud or on premise. Additionally, it presents a functionality that allows users to interact with and access reports on mobile devices online and offline. It utilizes data from various sources to create reports and delivers a plethora of analysis methods.
Efficiently supply business-critical analytics and produce insights with self-service capabilities.
Eliminates the need to transfer data while providing a consistent user experience through desktop or mobile device usage.
Utilizes automation technology in the analytics process to predict user intent and more, ultimately increasing overall productivity.
Through the use of AI, IBM Watson autonomously uncovers patterns and meaning within data through data discovery and automated predictive analytics. This cloud-based, advanced data analysis and visualization solution delivers the ability for any business user to instantly define a trend or visualize the data report in a dashboard. It provides a dependable guide to users over the course of data discovery and analysis, along with the ability to interact with data and gather easily understandable answers using the tool’s cognitive capabilities like natural language dialogue, without the help of a professional data analyst.
Ask questions that will add or connect to data for logical insights on demand.
- Forms of Analysis:
Explore, assemble and predict data outcomes utilizing a variety of forms while ensuring true insight.
Operate with confidence upon identifying trends, patterns or factors that can possibly drive business outcomes.
Looker allows anyone to ask advanced or complex questions using familiar business terms. It collects and extracts data from numerous sources and inserts it into an SQL database, where it performs its agile modeling layer for custom business logic. Once completed, it makes the information available for all users through shared insights, dashboards and studies. Looker delivers data straight to the partnering tools and applications, making it easily sharable and accessible.
- Easy Exportation:
Exporting can be easily done both locally and directly to platforms such as Google Drive and Dropbox.
Data isn’t locked in an analytics tool, but can be accessed through additional systems.
- Data Delivery Flexibility:
Any team member can schedule delivery of data to chats, emails, FTP (File Transfer Protocol) and more.
Built to help make better sense of data, Yellowfin is an end-to-end business intelligence solution that delivers actionable insights and data-driven predictions that can be used to make better, more informed business decisions. It mixes with a wide range of business systems and add-ons and offers a number of data visualization options. Functionalities can also be expanded to meet evolving business needs, including a blend with existing software solutions to optimize workflow.
Create interactive presentations while utilizing different visualization techniques and analytics with data-storytelling capability.
Get alerts when changes are made in your data flow with customizable alerts.
Easily track and address multiple analytics problems by consolidating data discovery, reporting and analytics capabilities in one user-friendly platform.
A Spark and Hadoop service in the cloud, HDInsight delivers an enterprise-wide cluster for an organization to operate their big data tasks and assignments. It’s a high-productivity platform for designers and data scientists while offering high-level organizational security and monitoring and integrating seamlessly with prominent productivity applications.
Delivers an industry-leading Service Level Agreement (SLA), along with reliable analytics.
Provides protection of data assets while extending on-premises governance and security controls to the cloud.
- Up-Front Costs:
Offers Hadoop deployment in the cloud without paying for new hardware or additional up-front costs.
Lumify assists users in the discovery of connections and the exploration of relationships within their data through a grand collection of analytic options. A big data merging, analysis and conceptual platform, Lumify is built on established, scalable big data technologies and comes with explicit data ingestion processing and interface components for textual content, videos and images.
Delivers 2D and 3D graph visualizations with a variety of automatic layouts.
- Easy Analysis:
Provides a variety of options for analyzing the links between entities on the graph.
Spaces feature allows work to be easily organized into a set of projects or workspaces.
Enabling data scientists to construct more precise models faster, Skytree offers easy-to-use and accurate predictive machine-learning models with extremely scalable algorithms. Artificial intelligence specific for data scientists, Skytree provides model understandability with systematic and graphical user interface (GUI) access.
Allows data scientists to visualize and gain a thorough understanding of the logic behind machine learning decisions.
- Easy Adoption:
Utilization through the easy-to-adopt GUI, or procedurally in Java.
- Problem Resolution:
Solve tough predictive analytics issues and problems through data preparation capabilities.
The Next Step in Your Big Data Journey
The importance of big data analytics is increasing and its reach is spreading into nearly every industry imaginable. But the process of analyzing big data to improve your business operations doesn’t stop at the purchase of data analytics software. In order to successfully achieve your business goals, you must learn how to leverage your data analytics system as a business advantage.