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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.

Best Big Data Processing and Distribution Software

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:

Collect and process big data sets in real-time
Distribute data across parallel computing clusters
Organize the data in such a manner that it can be managed by system administrators and pulled for analysis
Allow businesses to scale machines to the number necessary to store its data

Top 10 Big Data Processing and Distribution Software

  • BigQuery
  • Snowflake
  • Azure Data Lake Store
  • Qubole
  • Amazon EMR
  • MS SQL
  • Hadoop HDFS
  • Azure HDInsight
  • Google Cloud Dataflow
  • Apache Ambari

Compare 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
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
Optimized for quick response
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

(20)4.6 out of 5

Azure Data Lake Store is secured, massively scalable, and built to the open HDFS standard, allowing you to run massively-parallel analytics.

(256)4.0 out of 5
Optimized for quick response
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.

(1,984)4.4 out of 5

SQL Server 2017 brings the power of SQL Server to Windows, Linux and Docker containers for the first time ever, enabling developers to build intelligent applications using their preferred language and environment. Experience industry-leading performance, rest assured with innovative security features, transform your business with AI built-in, and deliver insights wherever your users are with mobile BI.

(94)4.3 out of 5

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

(15)3.9 out of 5

HDInsight is a fully-managed cloud Hadoop offering that provides optimized open source analytic clusters for Spark, Hive, MapReduce, HBase, Storm, Kafka, and R Server backed by a 99.9% SLA.

(29)4.1 out of 5

Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed. And with its serverless approach to resource provisioning and management, you have access to virtually limitless capacity to solve your biggest data processing challenges, while paying only for what you use.

(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.

Apache Spark for Azure HDInsight is an open source processing framework that runs large-scale data analytics applications.

(25)4.6 out of 5

Snowplow is a data delivery platform that collects and operationalizes behavioral data at scale. We empower you and your team to rise above the difficulties of data delivery and organization, enabling you to focus on your data journey.

(12)4.3 out of 5

Google Cloud Dataprep is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. Cloud Dataprep is serverless and works at any scale.

(20)4.2 out of 5

Making big data simple

(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

Oracle Big Data Cloud Service offers an integrated portfolio of products to help organize and analyze diverse data sources alongside existing data.

(15)4.0 out of 5

Apache Beam is an open source unified programming model designed to define and execute data processing pipelines, including ETL, batch and stream processing.

(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

(11)4.0 out of 5

Hadoop Distribution

(14)4.3 out of 5

Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead, and you pay only for the resources you use (with per-second billing). Cloud Dataproc also easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning.

(11)4.2 out of 5

Web based mysql client

(12)3.7 out of 5

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing.

(21)4.8 out of 5

Maximize the power of your data with Dremio—the data lake engine. Dremio operationalizes your cloud data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts via a governed self-service layer. The result is fast, easy data analytics for data consumers at the lowest cost per query for IT and data lake owners.

(9)4.3 out of 5
Optimized for quick response

HVR is a real-time data replication solution designed to move large volumes of data FAST and efficiently in hybrid environments for real-time analytics. With HVR, discover the benefits of using log-based change data capture for replicating data from common DBMS such as SQL Server, Oracle, SAP Hana, and more to sources such as AWS, Azure, Teradata and more.

(9)4.1 out of 5

Hazelcast IMDG (In-Memory Data Grid) is a distributed, in-memory data structure store that enables high-speed processing for building the fastest applications. It creates a shared pool of RAM from across multiple computers, and scales out by adding more computers to the cluster. It can be deployed anywhere (on-premises, cloud, multi-cloud, edge) due to its lightweight packaging that also makes it easy to maintain, since there are no required external dependencies. It provides a processing engine

(5)4.2 out of 5

Build and deploy clusters within minutes with simplified user experience, scalability, and reliability. Custom configure the environment. Administer through multiple interfaces. Scale on demand.

(32)4.8 out of 5

Since 2007, we are creating the most powerful framework to push the barriers of analytics, predictive analytics, AI and Big Data, while offering a helpful, fast and friendly environment. The TIMi Suite consists of four tools: 1. Anatella (Analytical ETL & Big Data), 2. Modeler (Auto-ML / Automated Predictive Modelling / Automated-AI), 3. StarDust (3D Segmentation) 4. Kibella (BI Dashboarding solution).

(2)4.0 out of 5

Apache Apex is an enterprise grade native YARN big data-in-motion platform designed to unify stream processing as well as batch processing.

ASG Technologies’ Enterprise Data Intelligence Solution delivers a tool-agnostic solution that supports the creation of custom metadata interfaces for your enterprise sources, providing a complete data lineage knowledge base. The range and flexibility offered by ASG includes discovery of mainframe, distributed and other ETL code, analyzing to ensure there are no gaps in your end-to-end lineage.

(2)5.0 out of 5
Entry Level Price:50000 USD / EUR

GI Big Data Analytics is a complete Big Data platform for companies that want to really benefit from the best technologies on the market as well as the consulting & services in one package. GI Big Data offer Analytics comprises all what you need: - Cloud Data Warehouse based on the best technologies Google Big Query, AWS Redshift, Pivotal Greenplum, Snowflake - Sandbox management included; - Reporting tool such as Tableau Software; - Analytics tool; - Connectors to databases and trackers -

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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.
High Performers
SQL Buddy
Hadoop HDFS
Hortonworks Data Platform
Snowplow Analytics
Amazon EMR
Google Cloud Dataproc
Azure HDInsight
Oracle Big Data Cloud Service
Google Cloud Dataflow
Apache Storm
Apache Ambari
Apache Beam
Azure Data Lake Store
Apache Spark for Azure HDInsight
Google Cloud Dataprep
Market Presence

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.

Key Benefits of Big Data Processing and Distribution Software

  • Decrease costs by using software which was built for big data
  • Increase efficiency and effectiveness through software utilities
  • Improve processing speed with the use of parallel computing clusters

Why Use 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.

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.

Kinds of Big Data Processing and Distribution Software

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 Features

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.

Potential Issues with Big Data Processing and Distribution Software

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.