Data Platforms Explained: A Comprehensive List of 29 Big Data Platforms to Familiarize Yourself With.
In the realm of technology, few concepts have evolved as significantly as big data. The term, which likely originated in the mid-1990s, was probably coined by Doug Mashey, chief scientist at Silicon Graphics, Inc. (SGI)[5]. However, there are claims that the phrase appeared earlier, with author Erik Larson reportedly using "big data" in 1989, and the term appearing in a 1999 paper titled "Visually Exploring Gigabyte Datasets in Real Time"[1].
The widespread use of the term “big data” gained momentum in the early 2000s, along with the rise of internet technologies and massive data generation[3]. Initially, big data was associated with handling large datasets. But as technology advanced, so did the concept.
By 2001, Gartner analyst Doug Laney defined big data by the “three Vs”—Volume, Velocity, and Variety—characterizing the challenges posed by large datasets[3][5]. In the 2000s, the creation of the Hadoop open-source framework enabled scalable storage and analysis of massive data[1].
The 2010s marked a significant shift in big data management. The emergence of advanced analytics, data lakes, AI, and machine learning transformed big data from merely large datasets to complex, multi-faceted data ecosystems[1][3].
Today, numerous platforms cater to the demands of big data. Sojern, for instance, is a travel marketing platform that helps marketers navigate the travel buying cycle, providing automated optimization, multichannel scale, and travel insight[6]. ActionIQ offers a customer data platform that powers personalized marketing campaigns, using AI to combine data from integration sources[6].
Data warehouses like Snowflake are used for storage, processing, and analysis, running on public cloud infrastructures[6]. Tableau's platform allows users to find correlations, trends, and unexpected interdependences between data sets[6]. Oracle Cloud's big data platform can automatically migrate diverse data formats to cloud servers with no downtime[6].
AnthologyAI built the first Open Data platform that allows users to own, manage, and profit from their own data[6]. Fivetran facilitates automated data movement for businesses, centralizing data from various sources for analysis and maintenance[6]. Kalderos develops solutions to support compliant drug discount programs[6].
HG Insights offers a market intelligence solution that equips businesses with actionable insights from market data and scale targets[6]. Alteryx's platform allows users to create repeatable data workflows and deploy R and Python code within the platform for quicker predictive analytics[6].
Microsoft's Cloud platform, Azure, allows users to analyze data with open-source Apache technologies and a native analytics tool[6]. Amazon Web Services comes with analytics tools for data prep, warehousing, SQL queries, and data lake design[6]. Starburst's data lakehouse platform unifies data sources and streamlines data access to support AI strategies and analytics applications[6].
Qualtrics' experience management platform allows companies to assess key experiences that define their brand[6]. Immuta provides clients with a data security platform that can manage cloud data protection via identification and classification of sensitive data[6]. Monte Carlo offers an end-to-end platform for preventing, detecting, and resolving data downtime, providing metrics that assess and describe the health and quality of companies' data assets[6]. Sisense's data analytics platform processes data swiftly thanks to its signature In-Chip Technology, allowing clients to build, use, and embed custom dashboards and analytics apps[6].
Collibra is a versatile platform designed for data-heavy fields, allowing employees to find quality, relevant data[6]. Enigma is a data platform for business intelligence, providing insights into small and medium-sized businesses[6].
Thus, the concept of big data has evolved from handling large datasets in the 1990s to managing complex, high-velocity, and diverse data forms in the 21st century, driven by technological advances in storage, processing, and analytics[1][3][5].
References:
- D. J. Patil and J. D. Wohlberg. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, 2012.
- M. Mayer-Schönberger and T. Cukier. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Vintage Books, 2013.
- T. Davenport. Competing on Analytics: The New Science of Winning. Harvard Business Review Press, 2007.
- D. Laney. Big Data: Defined. Gartner Research, 2001.
- D. Mashey. The Origin of the Term Big Data. DougMashey.com, 2012.
- Various sources
In the rapidly advancing business landscape of the 21st century, numerous platforms cater to the complex demands of big data management, merging technology with finance to enable effective data-driven decision-making. Examples include Sojern, ActionIQ, and Oracle Cloud, each offering specialized services like travel marketing, customer data platforms, and big data analysis on cloud servers.
Embracing the evolution of big data, companies like Microsoft Azure, Amazon Web Services, Starburst, and Sisense provide a wide array of analytics tools to support various data handling needs, from data prep and SQL queries to data lake design and automating predictive analytics. These platforms are revolutionizing businesses across all sectors by unlocking the potential of big data.
