Apr 20, 2022
Dear Community,
Many thanks for yor support and help!
Teradata and Apache Hadoop compete in the data warehousing and big data analytics category. Teradata appears to have the upper hand in handling structured data and performance-intensive analytics, while Hadoop is recognized for its cost-effectiveness and flexibility in handling diverse data types.
Features: Teradata offers advanced parallel processing capabilities, scaling efficiently with features like Teradata Optimizer and Grid. It supports complex queries through its shared-nothing architecture and enables efficient data warehousing with "Parallel Everything" design. Apache Hadoop is known for its open-source nature and cost-effectiveness, providing scalability with HDFS and ecosystem components like Hive and Spark. It handles various data types, making it suitable for large dataset processing.
Room for Improvement: Teradata users seek better cloud flexibility and integration with big data platforms, alongside more affordable pricing and improved data visualization. Apache Hadoop users note the complexity of setup and maintenance, expressing a desire for better user interfaces and real-time processing capabilities. Its reliance on community support may challenge enterprises needing strong technical assistance.
Ease of Deployment and Customer Service: Teradata offers deployment across on-premises, private, and public clouds with strong technical support, though some note response times as a concern. Apache Hadoop provides similar deployment flexibility but may lack Teradata's structured support. While the Hadoop community is active, Teradata’s support is perceived as more comprehensive.
Pricing and ROI: Teradata is a high-cost solution justified by its performance and advanced features, though pricing adjustments could enhance its competitiveness. Apache Hadoop's open-source model offers lower upfront costs, suiting enterprises managing large data volumes without heavy commercial support. Both solutions offer significant ROI, though organizational needs and scale influence cost structure and value perception.
We have realized a return on investment, with a reduction of staff from 27 to eight, and our current return on investment is approximately 14%.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
The customer support for Teradata has been great.
Customer support is very good, rated eight out of ten under our essential agreement.
The technical support from Teradata is quite advanced.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
This expansion can occur without incurring downtime or taking systems offline.
Teradata's scalability is great; it's been awesome.
Scalability is complex as you need to purchase a license and coordinate with Teradata for additional disk space and CPU.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
I find the stability to be almost a ten out of ten.
The workload management and software maturity provide a reliable system.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata.
If Teradata could provide a list of certified experts, that would be fantastic.
Teradata is much more expensive than SQL, which is well-performed and cheaper.
Initially, it may seem expensive compared to similar cloud databases, however, it offers significant value in performance, stability, and overall output once in use.
We spent roughly $295,000 on setup costs.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
Teradata's security helps our organization meet compliance requirements such as GDPR and IFRS, and it is particularly essential for revenue contracting or revenue recognition.
The data mover is valuable over the last two years as it allows us to achieve data replication to our disaster recovery systems.
| Product | Market Share (%) |
|---|---|
| Teradata | 12.1% |
| Apache Hadoop | 4.3% |
| Other | 83.6% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 12 |
| Large Enterprise | 49 |
Teradata is a powerful tool for handling substantial data volumes with its parallel processing architecture, supporting both cloud and on-premise environments efficiently. It offers impressive capabilities for fast query processing, data integration, and real-time reporting, making it suitable for diverse industrial applications.
Known for its robust parallel processing capabilities, Teradata effectively manages large datasets and provides adaptable deployment across cloud and on-premise setups. It enhances performance and scalability with features like advanced query tuning, workload management, and strong security. Users appreciate its ease of use and automation features which support real-time data reporting. The optimizer and intelligent partitioning help improve query speed and efficiency, while multi-temperature data management optimizes data handling.
What are the key features of Teradata?In the finance, retail, and government sectors, Teradata is employed for data warehousing, business intelligence, and analytical processing. It handles vast datasets for activities like customer behavior modeling and enterprise data integration. Supporting efficient reporting and analytics, Teradata enhances data storage and processing, whether deployed on-premise or on cloud platforms.
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