Top Free Big Data Processing and Distribution Software

Check out our list of free Big Data Processing and Distribution Software. Products featured on this list are the ones that offer a free trial version. As with most free versions, there are limitations, typically time or features.

If you'd like to see more products and to evaluate additional feature options, compare all Big Data Processing and Distribution Software to ensure you get the right product.

(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.

CC
I think that bigquery is a excellent database for Data Warehouse OLAP solutions. Query times: TB in seconds PB in minutes Read review
UM
The ease of use and navigating the UI is a big draw, as well as all the pre-built integrations with other Google products and services. Read review
(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

- -
Qubole's proprietary autoscaling is what really provides value - this has saved us significant cloud costs compared to other solutions, such as... Read review
AL
1. Customer service and representatives. They are very patient, friendly and knowledgeable. They always show up on time for the office hours. The... Read review
(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

AC
We are able to store semi-structured (chat messages) data at scale and able to parse them out easily as well on query. At Drift. The data team also... Read review
Mark P.
MP
Tri-Secret Secure : customer held key ensures that service representatives can not "see" our data. Dynamic Compute Sizing : we can resize our... Read review
(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

Ashish M.
AM
1) Pre-rolled up data into dimension and metrics 2) Lighting fast data/ query result retrieval Read review
(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.

EF
Fast and user-friendly query engine on top of open standard parquet files without the hassle of data loading process to a proprietary vendor... Read review
(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.

VP
HVR is real time large data replication tool from variety of sources. Best tool to move bulk data from different different data sources. efficient... Read review
(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

Pankaj S.
PS
We had multiple services running independently. It was helping us to make financial data instantly available to all our services. Read review
Tharanga H.
TH
Hazlecast is great because it's distributed data-structures are extensions of commonly used java interfaces. Due to this our team was able to... Read review
(31)4.9 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).

JF
I can automate giant process in order to execute it at any time Read review
Julián Felipe D.
JD
- The graphic modules implemented make a great UI, instead of only code based. - The flexibility to make changes in ETL's already working. - Really... Read review
(6)4.8 out of 5

InsightEdge is an always-on platform for your mission-critical applications across cloud, on-premise or hybrid. The platform operationalizes machine learning and transactional processing, at scale; analyzing data as it's born, enriching it with historical context, for instant insight to action.

SG
Its relatively new and well maintained with active support .     Based on Java     Ability to load balance , replication and failover... Read review
(3)4.3 out of 5

Ataccama delivers self-driving data management and governance with Ataccama ONE. It’s a robust, AI-powered platform integrating Data Discovery & Profiling, Metadata Management & Data Catalog, Data Quality Management, Master & Reference Data Management, and Big Data Processing & Data Integration. Ataccama ONE gives you the option to start with what you need and seamlessly extend as your business requires. The first step is free—try our one-click data profiling trusted by 55,000 us

Flora J.
FJ
Data is the most precious raw material , and if refined can lead to great results . Its is one among good tools to convert raw data into ... Read review
Ed V.
EV
The tool is all-in one with DG, DQ, RDM and MDM functionality. Read review

Datacoral offers a secure, fully-managed, serverless, ELT-based data infrastructure platform that runs in your AWS VPC and includes enterprise DataOps features like Amazon Redshift management, pipeline orchestration, operational monitoring and data publishing to support the full lifecycle of data pipelines. Datacoral ingests data from over 75 sources, builds data pipelines from SQL transformations inside of Amazon Redshift, Athena or Snowflake, and publishes data to analytic, machine learning

0 ratings
Entry Level Price:$100 month

The easiest, fastest, and most affordable way to run a production-ready Snowplow. SnowCatCloud is a hosted Snowplow solution for companies who do not have the technical resources to set up and manage Snowplow in a production environment. - Fully featured Snowplow - Cloud costs included - No data engineers necessary - Deliver event-level data to S3, Redshift, BigQuery, Snowflake, and ElasticSearch in real-time - No lock-in contracts, migrate to your own Snowplow anytime https://www.youtube.com/

Top 10 Free Big Data Processing and Distribution Software in 2021

  • Google BigQuery
  • Qubole
  • Snowflake
  • Druid
  • Dremio

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.