Apr 20, 2022
Dear Community,
Many thanks for yor support and help!
Teradata and Apache Hadoop are competitors in the data management and analytics space. Teradata has a competitive edge with advanced analytics and real-time processing, whereas Apache Hadoop excels in scalability and cost-effectiveness for large data sets.
Features: Teradata offers robust data warehousing, analytics, and workload management capabilities. Its automation and scalability are strong points, along with high productivity and redundancy options. Apache Hadoop supports distributed processing with Spark and various data types via HDFS. Its open-source nature provides flexibility, and it efficiently handles big data.
Room for Improvement: Teradata faces challenges with its high costs, scalability limitations on-premises, and the need for better cloud integration and support for unstructured data. Users also seek improved training resources and cloud adoption. Apache Hadoop could enhance user-friendliness, processing speed, and support for real-time data and incremental data processing.
Ease of Deployment and Customer Service: Teradata supports versatile deployment in on-premises, cloud, and hybrid environments, offering well-rated customer support and professional guidance. However, Apache Hadoop mainly relies on community support, which can be less prompt compared to Teradata's structured resources.
Pricing and ROI: Teradata is known for being expensive, justified by its feature set and ROI for complex data environments, making it suitable for larger organizations. Apache Hadoop, being open-source, offers lower upfront costs, appealing to budget-conscious enterprises needing to manage extensive data, despite setup and maintenance complexities.
At least fifteen to twenty percent of our time has been saved using Teradata, which has positively affected team productivity and business outcomes.
Independent research showed that Teradata VantageCloud users achieved an average ROI of 427% across three years with payback under a year, demonstrating the platform's ability to deliver a strong financial return.
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.
They are responsive and knowledgeable, and the documentation is very helpful.
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 flexibility allows organizations to scale according to their needs, balancing performance, cost, and compliance requirements.
This expansion can occur without incurring downtime or taking systems offline.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
Its massively parallel process architecture allows the platform to distribute workload efficiently, enabling organizations to run heavy analytic queries without compromising speed or stability.
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.
I want to highlight two features for improvement: first, storing data in various formats without requiring a tabular structure, accommodating unstructured data; and second, adding AI ML features to better integrate Gen AI, LLM concepts, and user-friendly experiences such as text-to-SQL capabilities.
Unlike SQL and Oracle, which have in-built replication capabilities, we don't have similar functionality with Teradata.
The most challenging aspect is finding Teradata resources, so we are focusing on internal training and looking for more Teradata experts.
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.
Role-based access control (RBAC), strong audit and compliance features, high availability, fault tolerance, and encrypted data at rest and in-transit are key features.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
I assess Apache Hadoop's fault tolerance during hardware failures positively since we have hardware failover, which works without problems.
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.
Its architecture allows information to be processed efficiently while maintaining stable performance, even in highly demanding environments.
It facilitates data integration, where we integrate and analyze data from various sources, making it a powerful and high-quality reliable solution for the company.
| Product | Mindshare (%) |
|---|---|
| Teradata | 8.8% |
| Apache Hadoop | 3.3% |
| Other | 87.9% |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 13 |
| Large Enterprise | 52 |
Apache Hadoop provides a scalable, cost-effective open-source platform capable of handling vast data volumes with features like HDFS, distributed processing, and high integration capabilities.
Apache Hadoop is known for its distributed file system HDFS, which supports large data volumes efficiently. Its open-source nature allows cost-effective scalability and compatibility with tools like Spark for enhanced analytics. While it offers significant processing power, areas for improvement include user-friendliness, interface design, security measures, and real-time data handling. Users benefit from data storage for structured and unstructured data, facilitated by its distributed processing architecture. Data replication ensures fault tolerance, while its capability to integrate with tools like Apache Atlas and Talend highlights its versatility.
What are the key features of Apache Hadoop?Industries leverage Apache Hadoop for Big Data analytics, data lakes, ETL tasks, and enterprise data hubs, handling unstructured and structured data from IoT, RDBMS, and real-time streams. Its applications extend to data warehousing, AI/ML projects, and data migration, employing tools like Apache Ranger, Hive, and Talend for effective data management and analysis.
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?
What benefits and ROI do users look for?
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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