49,378 Companies
- United States: 10,750 Companies
- North America: 13,215 Companies
- EMEA: 19,041 Companies
- United Kingdom: 3,391 Companies
- APAC: 9,272 Companies
- Australia and New Zealand: 1,719 Companies
(MSPs, CRM Vendors, Resellers, ISVs, CRM Software Companies) in our database across the globe
What is Big Data?
"Big Data" is the huge zettabytes of data coming from our computers, mobile devices, and industrial sensors that we swim in every day. Organizations use this data to inform choices, enhance procedures and rules, and develop customer-focused goods, services, and experiences. Large data is characterised as being "big" due to its variety and complexity, as well as its sheer volume. It typically requires more storage space and processing power than standard databases can handle. Big Data can also come from anything or anywhere on the planet that we can track digitally. Internet of Things (IoT) gadgets, traffic cameras, and social media fads.
1. Spark
Spark was created specifically for using clustered computing to process huge data. Both stream and batch processing are supported. It has built-in machine learning algorithms, SQL, and data streaming modules, as well as high-level R, Java, Python, and Scala APIs (meaning you can use your preferred language when programming). Since Spark is open-source and has so many built-in features, it can be applied to practically any industry that uses data science. Spark was initially developed by Apache to overcome the shortcomings of Hadoop MapReduce, another tool. With essentially identical code,
2. Hadoop
A distributed file system and a MapReduce engine that store and analyse large amounts of data, respectively, make up the open-source framework known as Hadoop. Even though Hadoop is an older and slower technology than Spark, many businesses that have used it in the past won't just drop it because another option exists. Additionally, Hadoop has benefits. To begin with, it has been tried and tested. Although it is not the most user-friendly programme, it is dependable and strong. Hadoop does not require supercomputers and can be installed on the majority of common commodity systems.
3. Power BI
One of the greatest big data technologies for processing structured data collections is Apache Cassandra. It was developed in 2008 by the Apache Software Foundation and is regarded as the most scalable open-source big data tool. This big data tool has demonstrated fault tolerance on commodity hardware and cloud infrastructure, making it more crucial for large data applications. Additionally, it provides functionalities that neither conventional relational databases nor NoSQL databases can. This comprises, among other things, performance, cloud availability points, and continuous availability as a data source. Companies like Twitter, Cisco, and Netflix use Apache Cassandra.
4. Flink
An open-source distributed processing platform for finite and unbounded data streams, Apache Flink is a tool for data analytics. It was created using Scala and Java. Even with late-arriving data, it still produces results with excellent accuracy. Flink is a stateful and fault-tolerant system, meaning it can quickly recover from errors. It performs on thousands of nodes and offers high performance efficiency at a large scale. It offers a streaming engine with low latency and high throughput and supports event time and state management.
5. Atlas
With the aid of Atlas, you may organise, examine, and understand qualitative data. People who need to examine semi-structured or even unstructured data, such as social scientists, market researchers, and healthcare experts, use it. Atlas is a thorough tool that enables you to uncover themes and patterns in your data and generate in-depth reports. Atlas has a user-friendly design, quick data loading, and a variety of analysis options to meet all needs. No of your level of experience, you can make the most of your data management by utilising this software, which uses some of the most user-friendly tools for qualitative data analysis.
6. MongoDB
A great replacement for contemporary databases is MongoDB. For enterprises that require quick and real-time data for quick decisions, a document-oriented database is the best option. Its usage of documents and collections rather than rows and columns distinguishes it from other conventional databases. It has a lot of flexibility and is simple for businesses to adapt because of its ability to store data in documents. It may hold any type of data, including objects, texts, arrays, Booleans, and integers. MongoDB offers compatibility for numerous platforms and technologies and is simple to understand.
7. Openrefine
Openrefine is an effective tool for cleaning and transforming data. Businesses, governments, and people who need to extract more value out of their data use it. It is the right tool for you if you want to take your jumbled data and turn it into something meaningful. Additionally, Openrefine has built-in security safeguards that can help you keep your data private and secure. This implies that it can assist you in maximising the worth of whatever form of data you may have. It is the ideal tool for everyone who wants to extract more value from their data and utilise it to make their business decisions because it is available in more than 15 different languages.
8. Rapidminer
In order to extract more value from their data, more than 40,000 businesses and individuals use Rapidminer on a global scale. For the best results, combine this software with appropriate data science training. Your data can be cleaned up, trends and patterns can be found, and detailed reports can be generated with the aid of Rapidminer. Rapidminer is a fantastic tool for both companies and individuals because it is completely transparent and offers an end-to-end data science process. Rapidminer enables a variety of data processing and integration tasks, including text mining, machine learning, predictive modelling, and more.
9. Tableau
Tableau offers the adaptability needed to solve issues, empower individuals and companies, and visually assess platforms. It is based on VizQL technology (visual language for database queries), which uses an intuitive user interface to transform drag and drop operations into data queries. Salesforce purchased Tableau in 2019. Data from sources like SQL databases, spreadsheets, or cloud services like Google Analytics and Salesforce can be linked thanks to this. Due to the fact that each version has distinct features and capabilities, users can choose to purchase Creator, Explorer, or Viewer depending on their personal or professional preferences.
10. Dataddo
Dataddo is a no-coding, cloud-based ETL platform that prioritises flexibility. With a large selection of connectors and the freedom to select your own metrics and attributes, Dataddo makes it quick and easy to build stable data pipelines. You don't need to add any components to your architecture that you weren't already using or alter your fundamental workflows because Dataddo seamlessly integrates into your current data stack. Instead of wasting time learning how to use yet another platform, Dataddo's user-friendly interface and quick setup allow you to concentrate on integrating your data.
Big Data FAQs
“Big Data” is the huge zettabytes of data coming from our computers, mobile devices, and industrial sensors that we swim in every day. Organizations use this data to inform choices, enhance procedures and rules, and develop customer-focused goods, services, and experiences. Large data is characterised as being “big” due to its variety and complexity, as well as its sheer volume.
- Structured data.
- Unstructured data.
- Semi-structured data.
Governments, businesses, and people have used big data for a variety of purposes, including Utilization of Big Data by Governments for Congestion Management, Intelligent Transportation Systems, and Traffic Control