Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Teradata Vantage
Score 8.3 out of 10
N/A
Teradata Vantage is presented as a modern analytics cloud platform that unifies everything—data lakes, data warehouses, analytics, and new data sources and types. Supports hybrid multi-cloud environments and priced for flexibility, Vantage delivers unlimited intelligence to build the future of business. Users can deploy Vantage on public clouds (such as AWS, Azure, and GCP), hybrid multi-cloud environments, on-premises with Teradata IntelliFlex, or on commodity hardware with VMware.
$0
Pricing
Apache SparkTeradata Vantage
Editions & Modules
No answers on this topic
Teradata VantageCore
$0
Teradata VantageCloud Lake
$0
Offerings
Pricing Offerings
Apache SparkTeradata Vantage
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Community Pulse
Apache SparkTeradata Vantage
Top Pros
Top Cons
Best Alternatives
Apache SparkTeradata Vantage
Small Businesses

No answers on this topic

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Score 8.6 out of 10
Medium-sized Companies
Cloudera Manager
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Score 9.7 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
Snowflake
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Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkTeradata Vantage
Likelihood to Recommend
9.9
(24 ratings)
8.9
(36 ratings)
Likelihood to Renew
10.0
(1 ratings)
8.2
(3 ratings)
Usability
10.0
(3 ratings)
7.3
(3 ratings)
Support Rating
8.7
(4 ratings)
8.0
(1 ratings)
User Testimonials
Apache SparkTeradata Vantage
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Teradata
TV is well suited for high speed, which is a great for large tables. The workload functionality is very good when in Viewpoint. The BAR functionality could use a little work. QueryGrid is very useful as well. The client handlers are still a work in progress, as I keep hearing that they continue to fail. There are also many restarts on the systems as well.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Teradata
  • ETL (Extract - Transfor - Load)
  • NOS to send data from Teradata Vantage to S3 and from S3 to Teradata Vantage
  • Teradata GeoSpacial feature
  • Bulk reading and writing in huge tables
  • MPP capacity already mature
  • Temporal Capacity more mature that other solutions
  • TASM
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Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Teradata
  • Teradata is an excellent option but only for a massive amount of data warehousing or analysis. If your data is not that big then it could be a misfit for your company and cost you a lot. The cost associated is quite extensive as compared to some other alternative RDBMS systems available in the market.
  • Migration of data from Teradata to some other RDBMS systems is quite painful as the transition is not that smooth and you need to follow many steps and even if one of them fails. You need to start from the beginning almost.
  • Last but not least the UI is pretty outdated and needs a revamp. Though it is simple, it needs to be presented in a much better way and more advanced options need to bee presented on the front page itself.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Teradata
Teradata is a mature RDBMS system that expands its functionality towards the current cloud capabilities like object storage and flexible compute scale.
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Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Teradata
It has helped our project to save time, effort and give accurate results in the quickest possible time. The diagnostics feature is also outstanding giving us a detailed report of the error which occurred, when it occurred and the likely cause and resolution of the same as well. The support of SQL is inherited which makes it easy to work on it because you don't need to learn any new language for this.
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Support Rating
Apache
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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Teradata
We have meetings at the beginning with the technical team to explain our requirements to them and they were really putting in a lot of effort to come up with a solution which will address all our needs. They implemented the software and also trained a few of our resources on the same too. We can get in touch with them now as well whenever we run into a roadblock but it's very less now.
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Teradata
Teradata is way ahead of its competitor because of its unique features of ensuring data privacy and data never gets corrupted even in worst case scenario. In most cases, the data corruption is a major issue if left unused and it leads to important data being wiped off which in ideal case should be stored for 3 years
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Teradata
  • Saving time and cost through its stable integration tools which helps import and export a huge amount of different data.
  • Providing analytics for the different data is easy and quick.
  • Securing all the data processes across the entire company.
  • Great Cloud solution for effective data quality management.
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ScreenShots

Teradata Vantage Screenshots

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