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.
At least fifteen to twenty percent of our time has been saved using Teradata, which has positively affected team productivity and business outcomes.
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.
The technical support from Teradata is quite advanced.
Customer support is very good, rated eight out of ten under our essential agreement.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Whenever we need more resources, we can add that in Teradata, and when not needed, we can scale it down as well.
This expansion can occur without incurring downtime or taking systems offline.
Teradata's scalability is great; it's been awesome.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
The workload management and software maturity provide a reliable system.
I find the stability to be almost a ten out of ten.
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.
If the same thing were available in a web interface, that would be really helpful.
If Teradata could provide a list of certified experts, that would be fantastic.
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.
We spent roughly $295,000 on setup costs.
Hadoop is a distributed file system, and it scales reasonably well provided you give it sufficient resources.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
It has resulted in better performance improvement within our team as we now cover nine business units instead of 18, thanks to the data performance, which has increased data visibility and helped the enterprise achieve a higher rate of internal return on financials.
The first thing that I appreciate about Teradata is its multi-parallel processing. Whatever queries we execute on Teradata, they are blazingly fast, so it offers really fast connectivity.
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 | 11.1% |
| Apache Hadoop | 3.9% |
| Other | 85.0% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 12 |
| Large Enterprise | 50 |
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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