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Best Big Data Processing and Distribution Software for Enterprise Businesses

    Products classified in the overall Big Data Processing and Distribution category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Big Data Processing and Distribution to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Big Data Processing and Distribution category.

    In addition to qualifying for inclusion in the Big Data Processing and Distribution Software category, to qualify for inclusion in the Enterprise Business Big Data Processing and Distribution Software category, a product must have at least 10 reviews left by a reviewer from an enterprise business.

    Top 9 Big Data Processing and Distribution Software for Enterprise Businesses

    • BigQuery
    • Snowflake
    • Qubole
    • Amazon EMR
    • Hadoop HDFS
    • Apache Ambari
    • Cloudera
    • Pepperdata
    • Druid

    Compare Enterprise Business Big Data Processing and Distribution Software

    G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.
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    (282)4.4 out of 5
    Optimized for quick response
    Entry Level Price:$0.02 per GB, per month.

    BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.

    (280)4.6 out of 5
    Entry Level Price:$2 Compute/Hour

    Snowflake delivers the Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflake’s platform is the engine that powers and provides access to the Data

    (257)4.0 out of 5
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    Entry Level Price:30 day free trial

    Qubole is the open data lake company that provides a simple and secure data lake platform for machine learning, streaming, and ad-hoc analytics. No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent. Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytic

    (47)4.0 out of 5

    Amazon EMR is a web-based service that simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective to distribute and process vast amounts of data across dynamically scalable Amazon EC2 instances.

    (95)4.3 out of 5

    Hadoop HDFS is a distributed, scalable, and portable filesystem written in Java.

    (21)4.2 out of 5

    Apache Ambari is a software project designed to enable system administrators to provision, manage and monitor a Hadoop cluster, and also to integrate Hadoop with the existing enterprise infrastructure.

    (25)4.1 out of 5

    At Cloudera, we believe data can make what is impossible today, possible tomorrow. We deliver an enterprise data cloud for any data, anywhere, from the Edge to AI. We enable people to transform vast amounts of complex data into clear and actionable insights to enhance their businesses and exceed their expectations. Cloudera is leading hospitals to better cancer cures, securing financial institutions against fraud and cyber-crime, and helping humans arrive on Mars — and beyond. Powered by the rel

    (15)4.4 out of 5
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    Pepperdata is a big data performance optimization product within the broader category of Application Performance Monitoring (APM). Pepperdata provides recommendations and optimizations for the big data stack, both in the cloud and on-premises. With Pepperdata, you get the granular insight and automation necessary and the assurance you will meet SLAs and increase the number of applications you run, all at a cost that meets business expectations.

    (29)4.2 out of 5

    Apache Druid is an open source real-time analytics database. Druid combines ideas from OLAP/analytic databases, timeseries databases, and search systems to create a complete real-time analytics solution for real-time data. It includes stream and batch ingestion, column-oriented storage, time-optimized partitioning, native OLAP and search indexing, SQL and REST support, flexible schemas; all with true horizontal scalability on a shared nothing, cloud native architecture that makes it easy to depl

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    Check out the G2 Grid® for the top Big Data Processing and Distribution Software products. G2 scores products and sellers based on reviews gathered from our user community, as well as data aggregated from online sources and social networks. Together, these scores are mapped on our proprietary G2 Grid®, which you can use to compare products, streamline the buying process, and quickly identify the best products based on the experiences of your peers.
    Leaders
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    Hadoop HDFS
    Cloudera
    BigQuery
    Qubole
    Snowflake
    Amazon EMR
    Apache Ambari
    Pepperdata
    Druid
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    Learn More About Big Data Processing and Distribution Software

    What is Big Data Processing and Distribution Software?

    Companies are seeking to extract more value from their data but they struggle to capture, store, and analyze all the data generated. With various types of business data being produced at a rapid rate, it is important for companies to have the proper tools in place for processing and distributing this data. These tools are critical for the management, storage, and distribution of this data, utilizing the latest technology such as parallel computing clusters. Unlike older tools which are unable to handle big data, this software is purpose built for large scale deployments and helps companies organize vast amounts of data.

    The amount of data businesses produce is too much for a single database to handle. As a result, tools are invented to chop up computations into smaller chunks, which can be mapped to many computers to perform computations and processing. Businesses that have large volumes of data (upwards of 10 terabytes) and high calculation complexity reap the benefits of big data processing and distribution software. However, it should be noted that other types of data solutions, such as relational databases are still useful for businesses for specific use cases, such as line of business (LOB) data, which is typically transactional.

    What Types of Big Data Processing and Distribution Software Exist?

    There are different methods or manners in which big data processing and distribution takes place. The chief difference lies in the type of data that is being processed.

    Stream processing

    With stream processing, data is fed into analytics tools in real time, as soon as it is generated. This method is particularly useful in cases like fraud detection where results are critical at the moment.

    Batch processing

    Batch processing refers to a technique in which data is collected over time and is subsequently sent for processing. This technique works well for large quantities of data that are not time sensitive. It is often used when data is stored in legacy systems, such as mainframes, that cannot deliver data in streams. Cases such as payroll and billing may be adequately handled with batch processing. 

    What are the Common Features of Big Data Processing and Distribution Software?

    Big data processing and distribution software, with processing at its core, provides users with the capabilities they need to integrate their data for purposes such as analytics and application development. The following features help to facilitate these tasks:

    Machine learning: This software helps accelerate data science projects for data experts, such as data analysts and data scientists, helping them operationalize machine learning models on structured or semistructured data using query languages such as SQL. Some advanced tools also work with unstructured data, although these products are few and far between.

    Serverless: Users can get up and running quickly with serverless data warehousing, with the software provider focusing on the resource provisioning behind the scenes. Upgrading, securing, and managing infrastructure is handled by the provider, thus giving businesses more time to focus on their data and how to derive insights from it.

    Storage and compute: With hosted options, users are enabled to customize the amount of storage and compute they want, tailored to their particular data needs and use case.

    Data backup: Many products give the option to track and view historical data and allows them to restore and compare data over time.

    Data transfer: Especially in the current data climate, data is frequently distributed across data lakes, data warehouses, legacy systems, and more. Many big data processing and distribution software products allow users to transfer data from external data sources on a scheduled and fully managed basis.

    Integration: Most of these products allow integrations with other big data tools and frameworks such as the Apache big data ecosystem.

    What are the Benefits of Big Data Processing and Distribution Software?

    Analysis of big data allows business users, analysts, and researchers to make more informed and quicker decisions using data that was previously inaccessible or unusable. Businesses use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.

    Using big data processing and distribution software, companies accelerate processes in big data environments. With open-source tools such as Apache Hadoop (along with commercial offerings, or otherwise), they are able to address the challenges they face around big data security, integration, analysis, and more.

    Scalability: In contradistinction, with traditional data processing software, big data processing and distribution software is able to handle vast amounts of data in an effective and efficient manner and has the ability to scale as the data output increases.

    Speed: With these products, businesses are able to achieve lightning-fast speeds, giving users the ability to process data in real time.

    Sophisticated processing: Users have the ability to perform complex queries and are able to unlock the power of their data for tasks such as analytics and machine learning.

    Who Uses Big Data Processing and Distribution Software?

    In a data-driven organization, various departments and job types need to work together to deploy these tools successfully. While systems administrators and big data architects are the most common users of big data analytics software, self-service tools allow for a wider range of end users and can be leveraged by sales, marketing, and operations teams.

    Developers: Users looking to develop big data solutions, including spinning up clusters and building and designing applications, use big data processing and distribution software.

    System administrators: It may be necessary for businesses to employ specialists to make sure that data is being processed and distributed properly. Administrators, who are responsible for the upkeep, operation, and configuration of computer systems fulfill this task and ensure everything runs smoothly.

    Big data architects: Translating business needs into data solutions is challenging. Architects bridge this gap, connecting with business leaders and data engineers alike to manage and maintain the data lifecycle.

    What are the Alternatives to Big Data Processing and Distribution Software?

    Alternatives to big data processing and distribution software can replace this type of software, either partially or completely:

    Data warehouse software: Most companies have a large number of disparate data sources. To best integrate all their data, they implement data warehouse software. Data warehouses house data from multiple databases and business applications that allow business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.

    NoSQL databases: While relational databases solutions excel with structured data, NoSQL databases more effectively store loosely structured and unstructured data. NoSQL databases pair well with relational databases if a company deals with diverse data that is collected by both structured and unstructured means.

    Software Related to Big Data Processing and Distribution Software

    Related solutions that can be used together with big data processing and distribution software include:

    Data preparation software: Data preparation software helps companies with their data management. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Although big data processing and distribution software typically offer some data preparation features, businesses might opt for a dedicated preparation tool.

    Big data analytics software: Businesses with a robust big data processing and distribution solution in place may begin to dig into their data and analyze it. They may adopt tools that are geared toward big data, called big data analytics software, which provides insights into large data sets that are collected from big data clusters.

    Stream analytics software: When users are looking for tools specifically geared toward analyzing data in real time, stream analytics software can be helpful. These real-time processing tools help users analyze data in transfer through APIs, between applications, and more. This software is helpful with internet of things (IoT) data that may require frequent analysis in real time.

    Log analysis software: Log analysis software is a tool that gives users the ability to analyze log files. This type of software typically includes visualizations and is particularly useful for monitoring and alerting purposes.

    Challenges with Big Data Processing and Distribution Software

    Software solutions can come with their own set of challenges. 

    Need for skilled employees: Handling big data is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts who are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data.

    Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring a skilled professional in house.

    Data organization: Big data solutions are only as good as the data that they consume. To get the most of the tool, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which stores data from a variety of applications and databases in a central location. Businesses may need to purchase a dedicated data preparation software as well to ensure that data is joined and clean for the analytics solution to consume in the right way. This often requires a skilled data analyst, IT employee, or an external consultant to help ensure data quality is at its finest for easy analysis.

    User adoption: It is not always easy to transform a business into a data-driven company. Particularly at older companies that have done things the same way for years, it is not simple to force new tools upon employees, especially if there are ways for them to avoid it. If there are other options, they will most likely go that route. However, if managers and leaders ensure that these tools are a necessity in an employee’s routine tasks, then adoption rates will increase.

    Which Companies Should Buy Big Data Processing and Distribution Software?

    The implementation of data processing solutions can have a positive impact on businesses across a host of different industries.

    Financial services: The use of big data processing and distribution in financial services can yield significant gains, such as for banks, which can use it for everything from processing credit score related data to distributing identification data. With big data processing and distribution software, data teams can process company data and deploy it to both internal and external applications.

    Health care: Within healthcare, a large amount of data is produced, such as patient records, clinical trial data, and more. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using this software to speed up the process, using data from past trials, research papers, and more.

    Retail: In retail, especially e-commerce, personalization is important. The top retailers are recognizing the importance of big data processing and distribution software to provide customers with highly personalized experiences, based on factors such as previous behavior and location. With the proper software in place, these businesses can begin to get their data in order.

    How to Buy Big Data Processing and Distribution Software

    Requirements Gathering (RFI/RFP) for Big Data Processing and Distribution Software

    If a company is just starting out and looking to purchase its first big data processing and distribution software, wherever a business is in its buying process, g2.com can help select the best big data processing and distribution software for the business.

    The first step in the buying process must involve a careful look at how the data is stored, both on premises or in the cloud. If the company has amassed a lot of data, the need is to look for a solution that can grow with the organization. Although cloud solutions are on the rise, each business must evaluate their own data needs to make the right decision. 

    Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for a number of reasons, including data security and issues related to latency. In cases such as health care, strict regulations such as HIPAA, require that data be secure. Therefore, on-premises solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is particularly strict and sometimes vital.

    Users should think about the pain points, such as getting their data consolidated and collecting their data from disparate sources, and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

    Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a big data processing and distribution software.

    Compare Big Data Processing and Distribution Software Products

    Create a long list

    From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

    Create a short list

    From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

    Conduct demos

    To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

    Selection of Big Data Processing and Distribution Software

    Choose a selection team

    Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.

    Negotiation

    Just because something is written on a company’s pricing page, does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

    Final decision

    After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

    What Does Big Data Processing and Distribution Software Cost?

    As mentioned above, big data processing and distribution software come as both on-premises and cloud solutions. Pricing between the two might differ, with the former often coming with more upfront costs related to setting up the infrastructure. 

    As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.

    Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software. Before evaluating the total cost of the solution, a business must carefully consider the full offering which they are purchasing, keeping in mind the cost of each component. It is not infrequent for businesses to sign a contract thinking they will only use a small portion of a given offering, only to realize after-the-fact that they benefited from and paid for a lot more.

    Return on Investment (ROI)

    Businesses decide to deploy big data processing and distribution software with the goal of deriving some degree of an ROI. As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

    Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

    Implementation of Big Data Processing and Distribution Software

    How is Big Data Processing and Distribution Software Implemented?

    Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

    Who is Responsible for Big Data Processing and Distribution Software Implementation?

    It may require a lot of people, such as the chief technology officer (CTO) and chief information officer (CIO), as well as many teams, to properly deploy, including data engineers, database administrators, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together data and begin the journey of data science, starting with proper data preparation and management.

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