Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.
Spark is an open-source solution, so there are no licensing costs.
Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.
Spark is an open-source solution, so there are no licensing costs.
When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive.
The price could be better for the product.
When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive.
The price could be better for the product.
Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.
The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs.
There is a need for my company to pay for the licensing costs of the solution.
The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs.
There is a need for my company to pay for the licensing costs of the solution.
The solution is open-sourced and free.
There is no license or subscription for this solution.
The solution is open-sourced and free.
There is no license or subscription for this solution.
The Hortonworks Data Platform (HDP) is widely used for managing and analyzing big data across various organizations. Key applications include optimizing data storage through its scalable structure, essential for handling significant data volumes effectively. It also enables advanced analytics by facilitating complex data processing, thereby enhancing decision-making with deeper insights. HDP excels in processing both streaming and batch data and supports the creation and management of robust data lakes, which are crucial for data governance and compliance.
Key features appreciated by users include its efficient large-volume data handling and robust data processing capabilities, powered by the Hadoop ecosystem. Users also value its scalability, advanced security features ensuring data protection, and compatibility with various analytics and management tools, allowing seamless integration with existing systems.
It is priced well and it is affordable
Currently, we are using the product in a sandbox environment, and there is no licensing. We might choose a licensing option once we get the results.
It is priced well and it is affordable
Currently, we are using the product in a sandbox environment, and there is no licensing. We might choose a licensing option once we get the results.
IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of compute notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms.
