Apache Cassandra vs Hadoop

May 26, 2023 | Author: Michael Stromann
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Apache Cassandra
Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients.
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Hadoop
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.
Apache Cassandra and Hadoop are both open-source distributed data processing platforms, but they have distinct features and use cases.

Apache Cassandra is a highly scalable and fault-tolerant distributed database system designed for handling large amounts of data across multiple nodes. It excels in providing high availability, linear scalability, and fault tolerance, making it suitable for use cases where data needs to be distributed across multiple data centers and nodes. Cassandra is optimized for write-heavy workloads and offers fast and efficient write performance, making it well-suited for use cases such as time-series data, real-time analytics, and high-velocity data ingestion.

Hadoop, on the other hand, is an ecosystem of open-source software frameworks that enables distributed storage and processing of big data. At the core of Hadoop is the Hadoop Distributed File System (HDFS), which provides scalable and reliable storage for large datasets across commodity hardware. Hadoop also includes the MapReduce framework for parallel processing of data, allowing for efficient batch processing and large-scale data analytics. Hadoop is commonly used for offline batch processing, data warehousing, and large-scale data analytics where data can be processed in a distributed manner.

The key difference between Apache Cassandra and Hadoop lies in their data models and processing paradigms. Cassandra is a distributed NoSQL database that offers high scalability, availability, and low-latency performance for real-time data needs. Hadoop, on the other hand, is a distributed data processing platform that focuses on batch processing and large-scale analytics. It is well-suited for offline processing and analytical workloads where data can be processed in parallel across a cluster of nodes.

See also: Top 10 Big Data platforms
Apache Cassandra 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.

Author: Michael Stromann
Michael is an expert in IT Service Management, IT Security and software development. With his extensive experience as a software developer and active involvement in multiple ERP implementation projects, Michael brings a wealth of practical knowledge to his writings. Having previously worked at SAP, he has honed his expertise and gained a deep understanding of software development and implementation processes. Currently, as a freelance developer, Michael continues to contribute to the IT community by sharing his insights through guest articles published on several IT portals. You can contact Michael by email stromann@liventerprise.com