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1010data vs Hadoop
May 26, 2023 | Author: Michael Stromann
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1010data provides a cloud-based platform for big data discovery and data sharing that delivers actionable, data-driven insights quickly and easily. 1010data offers a complete suite of products for big data discovery and data sharing for both business and technical users. Companies look to 1010data to help them become data-driven enterprises.
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The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
1010data and Hadoop are both powerful platforms for big data processing and analytics, but they differ in their architecture, data processing approach, and target use cases.
1010data is a cloud-based analytics and data management platform that offers an integrated solution for storing, querying, and analyzing large datasets. It provides a high-performance, columnar-based database that is optimized for interactive analytics and complex queries. 1010data simplifies data integration and provides a user-friendly interface, making it accessible to business users and data analysts. It is suitable for real-time analysis, ad-hoc querying, and collaborative data exploration.
Hadoop, on the other hand, is an open-source framework that enables distributed storage and processing of large datasets across clusters of commodity hardware. It consists of the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for distributed processing. Hadoop is designed to handle massive volumes of data and provides fault tolerance, scalability, and flexibility. It is particularly well-suited for batch processing, large-scale data transformations, and complex analytics tasks.
The key differences between 1010data and Hadoop lie in their architecture, data processing models, and target use cases. 1010data offers a fully managed cloud platform with a focus on interactive analytics, ease of use, and real-time analysis. It is suitable for business users and data analysts who require quick and intuitive access to data. Hadoop, on the other hand, provides a scalable, distributed framework for processing massive volumes of data, particularly in batch processing scenarios. It is widely used for data transformation, complex analytics, and processing unstructured data.
See also: Top 10 Big Data platforms
1010data is a cloud-based analytics and data management platform that offers an integrated solution for storing, querying, and analyzing large datasets. It provides a high-performance, columnar-based database that is optimized for interactive analytics and complex queries. 1010data simplifies data integration and provides a user-friendly interface, making it accessible to business users and data analysts. It is suitable for real-time analysis, ad-hoc querying, and collaborative data exploration.
Hadoop, on the other hand, is an open-source framework that enables distributed storage and processing of large datasets across clusters of commodity hardware. It consists of the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for distributed processing. Hadoop is designed to handle massive volumes of data and provides fault tolerance, scalability, and flexibility. It is particularly well-suited for batch processing, large-scale data transformations, and complex analytics tasks.
The key differences between 1010data and Hadoop lie in their architecture, data processing models, and target use cases. 1010data offers a fully managed cloud platform with a focus on interactive analytics, ease of use, and real-time analysis. It is suitable for business users and data analysts who require quick and intuitive access to data. Hadoop, on the other hand, provides a scalable, distributed framework for processing massive volumes of data, particularly in batch processing scenarios. It is widely used for data transformation, complex analytics, and processing unstructured data.
See also: Top 10 Big Data platforms
1010data vs Hadoop in our news:
2014. MapR partners with Teradata to reach enterprise customers
The last remaining independent Hadoop provider, MapR, and the prominent big data analytics provider, Teradata, have joined forces to collaborate on integrating their respective products and developing a unified go-to-market strategy. As part of this partnership, Teradata gains the ability to resell MapR software, professional services, and provide customer support. Essentially, Teradata will act as the primary interface for enterprises that utilize or aspire to use both technologies, serving as the representative for MapR. Previously, Teradata had established a close partnership with Hortonworks, but it now extends its collaboration and analytic market leadership to all three major Hadoop providers. Similarly, earlier this week, HP unveiled Vertica for SQL on Hadoop, enabling users to access and analyze data stored in any of the three primary Hadoop distributions—Hortonworks, MapR, and Cloudera.
2014. HP plugs the Vertica analytics platform into Hadoop
HP has unveiled the introduction of Vertica for SQL on Hadoop, a significant announcement in the world of analytics. With Vertica, customers gain the ability to access and analyze data stored in any of the three primary Hadoop distributions: Hortonworks, MapR, and Cloudera, as well as any combination thereof. Given the uncertainty surrounding the dominance of a particular Hadoop flavor, many large companies opt to utilize all three. HP stands out as one of the pioneering vendors by asserting that "any flavor of Hadoop will do," a sentiment further reinforced by its $50 million investment in Hortonworks, which currently represents the favored Hadoop flavor within HAVEn, HP's analytics stack. HP's announcement not only emphasizes the platform's interoperability but also highlights its capabilities in dealing with data stored in diverse environments such as data lakes or enterprise data hubs. With HP Vertica, organizations gain a seamless solution for exploring and harnessing the value of data stored in the Hadoop Distributed File System (HDFS). The combination of Vertica's power, speed, and scalability with Hadoop's prowess in handling extensive data sets serves as an enticing proposition, potentially motivating hesitant managers to embrace big data initiatives confidently. HP's comprehensive offering provides a compelling avenue for organizations to unlock the potential of their data, urging them to venture beyond their reservations and embrace the world of big data.
2014. Cloudera helps to manage Hadoop on Amazon cloud
Hadoop vendor Cloudera has unveiled a new offering named Director, aimed at simplifying the management of Hadoop clusters on the Amazon Web Services (AWS) cloud. Clarke Patterson, Senior Director of Product Marketing, acknowledged the challenges faced by customers in managing Hadoop clusters while maintaining extensive capabilities. He emphasized that there is no difference between the cloud version and the on-premises version of the software. However, the Director interface has been specifically designed to be self-service, incorporating cloud-specific features like instance-tracking. This enables administrators to monitor the cost associated with each cloud instance, ensuring better cost management.
