Teradata Competitors and Alternatives

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Read reviews of Teradata competitors and alternatives

Anirban Bhattacharya
Practice Head, Data & Analytics at Tech Mahindra Limited
Real User
Top 5
Exceptionally good technology that addresses data warehousing challenges and is built and designed in a good way

What is our primary use case?

It is used in my company as well as in my client's company. We are a system integrator, so naturally, we need to have the centers of excellence and competencies in Snowflake.

Pros and Cons

  • "The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."
  • "There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."

What other advice do I have?

This could be something that might be debated upon, but Snowflake has two parts to it. One is the data warehouse itself, and the other one is the cloud. It is important to know about the cloud in terms of: * How a cloud functions? * How a cloud orchestrates through its services, domains, invocation of services, and other things? * How a cloud is laid out? For example, let's take AWS. If AWS is invoking Lambda or something else, how will S3 come into the picture? Is there a role of DynamoDB? If you're using DynamoDB, how would you use it in the Snowflake landscape? So, cloud nuances are…
Radu Biristeica
IT Consultant at Trend Import-Export
Consultant
Top 20
Excellent storage cell capabilities and intelligence with great speed

Pros and Cons

  • "We can use virtualization on Exadata."
  • "The improvement could be made on the hardware level as the habit in the industry is to go better and faster and larger with every iteration."

What other advice do I have?

We've been Oracle partners for around ten years or so. I'm a project manager, and not overly technical. We don't have Exadata in our company, however, we have Exadata via a client. The current company where I work is the first company in Romania to sell Exadata in Romania. There are a number of Exadata solutions sold in Romania - which is why my colleague has achieved past competencies and certification in Exadata machines. They are very good, and they are delivering the present services on Exadata. I manage the projects where they deliver services on Exadata only for the customer, not in our…
SM
Program Director - Multi-service Integration and Large programs at a computer software company with 10,001+ employees
Real User
Has good base product functionality of data storage and analytics but there should be an option to use it on the cloud

What is our primary use case?

Aster Teradata is mostly being used for doing mapping and I use it for analytics. Usually, there is a support vendor who will use Teradata to do the database analytics. But sometimes with Teradata, the DB administrators use it to get access to the libraries or the data processes. I mostly recommend Teradata for data storage because it is easy to deploy and implement.

Pros and Cons

  • "The ease of deployment is useful so clients are up and running quickly in comparison to other products."
  • "There are some ways that the handling of unstructured data could be improved."

What other advice do I have?

Teradata has all the best features currently, but I think right now the market is really flooding with multiple options. Many customers have a need for rapid on-premise deployment. I think in that case I would recommend Teradata. But for smaller or medium-sized companies, there are probably other options out there that they might use instead which will fit better for their budget. On a scale from one to ten where one is the worst and ten is the best, I would rate Aster Data MapReduce (Teradata) overall as maybe six or seven probably. Seven-out-of-ten. To improve that rating, I think we see…
DB
Sr Tech Business Analyst, Group Data Projects & Ventures at a financial services firm with 10,001+ employees
Real User
Top 20
Stable, flexible, and scalable

What is our primary use case?

Currently, I'm moving to another set of projects. One is for a small company that supports a client and is building on a different surface on the SQL server. The cloud that is used is essentially Amazon AWS.

Pros and Cons

  • "The solution seems to be pretty flexible."
  • "Due to the fact that I'm dealing with the product more as a data analyst, the SQL Server management studio is really relatively primitive compared to other more advanced tools."

What other advice do I have?

I'm using the 2016 or 2017 version of the solution. There are many SQL options. I'd only recommend this one if it made sense to the individual company and their requirements. In general, I'd rate the solution at an eight out of ten.
Mike Walker
Director - Big Data, IoT and Analytics at Benchmark Corp
Reseller
Top 5
Low cost, high performance with large scale queries, and integrates well in an enterprise setting

What is our primary use case?

We are resellers and we provide products for our customers. Our clients are using this solution in two ways; one is for a data warehouse, and the second is for analytics in the database.

Pros and Cons

  • "For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments."
  • "Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint."

What other advice do I have?

The customers love them. They absolutely love them. Before implementing this solution, make sure that it is on the list and that you evaluate it. I would rate Vertica a ten out of ten.
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