Apr 20, 2022
Dear Community,
Many thanks for yor support and help!
Teradata and Apache Hadoop compete in the big data analytics space. Teradata holds an advantage in structured data processing and parallel processing capabilities, while Apache Hadoop excels in scalability and open-source flexibility for diverse data types.
Features: Teradata offers parallel processing, advanced workload management, and extensive analytics tools for structured data. It provides scalability with its columnar and temporal data processing. Apache Hadoop supports unstructured data with its distributed storage and processing, leveraging open-source flexibility. It includes features like HDFS and MapReduce for managing vast datasets cost-effectively.
Room for Improvement: Teradata should address high costs, scalability issues, and improve integration with modern cloud solutions. Enhancements are needed in handling unstructured data and user experience. Apache Hadoop could improve user interface, integration with visualization tools, and operational management. Security measures and configuration complexity are other areas for enhancement.
Ease of Deployment and Customer Service: Teradata provides diverse deployment options, including on-premises and cloud, with highly rated technical support. Despite higher costs, customer service is well-regarded. Apache Hadoop offers versatile on-premises deployment, but support often depends on community resources, which might not match proprietary solutions.
Pricing and ROI: Teradata is expensive but offers significant ROI in performance and reliability, making it ideal for large enterprises needing advanced data processing. Apache Hadoop is cost-effective due to its open-source model, appealing to budget-conscious enterprises. Costs can arise from distributions and support services, yet both products enhance ROI through improved analytics capabilities.
The technical support from Teradata is quite advanced.
Customer support is very good, rated eight out of ten under our essential agreement.
This expansion can occur without incurring downtime or taking systems offline.
Scalability is complex as you need to purchase a license and coordinate with Teradata for additional disk space and CPU.
I find the stability to be almost a ten out of ten.
The workload management and software maturity provide a reliable system.
Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata.
Initially, it may seem expensive compared to similar cloud databases, however, it offers significant value in performance, stability, and overall output once in use.
Teradata is much more expensive than SQL, which is well-performed and cheaper.
The data mover is valuable over the last two years as it allows us to achieve data replication to our disaster recovery systems.
Teradata is a scalable data analytics platform designed to meet enterprise demands for large-scale data management and processing, focusing on performance, scalability, and security for complex query executions.
As a leading data warehousing solution, Teradata integrates advanced analytics enabling organizations to derive insights from massive datasets. It supports high-volume data workloads with its architecture optimized for analytical queries. Users benefit from its robust scalability, allowing seamless expansion as data grows. Teradata's SQL engine is compatible with a wide range of data types, ensuring flexibility in data analysis. With advanced security measures, it protects sensitive data across various environments, providing peace of mind to users handling critical information.
What are the most important features of Teradata?Teradata is widely used in industries like finance, telecommunications, and healthcare, where data-driven decisions are critical. Companies leverage its robust analytics capabilities to enhance customer experiences, streamline operations, and ensure compliance with regulatory requirements. In these sectors, quick access to data insights can significantly impact competitive advantage.
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