Big Data Processing and Distribution reviews by real, verified users. Find unbiased ratings on user satisfaction, features, and price based on the most reviews available anywhere.
Big data processing and distribution systems offer a way to collect, distribute, store, and manage massive, unstructured data sets in real time. These solutions provide a simple way to process and distribute data amongst parallel computing clusters in an organized fashion. Built for scale, these products are created to run on hundreds or thousands of machines simultaneously, each providing local computation and storage capabilities. Big data processing and distribution systems provide a level of simplicity to the common business problem of data collection at a massive scale and are most often used by companies that need to organize an exorbitant amount of data. Many of these products offer a distribution that runs on top of the open-source big data clustering tool Hadoop.
Companies commonly have a dedicated administrator for managing big data clusters. The role requires in-depth knowledge of database administration, data extraction, and writing host system scripting languages. Administrator responsibilities often include implementation of data storage, performance upkeep, maintenance, security, and pulling the data sets. Businesses often use big data analytics tools to then prepare, manipulate, and model the data collected by these systems.
To qualify for inclusion in the Big Data Processing and Distribution category, a product must:
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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.
Key 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.
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.
Systems administrator — 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 make sure everything runs smoothly.
Big data architect — 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.
There are different methods or manners in which the big data processing and distribution takes place.
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 in 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.
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 semi-structured 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.
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 the 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.