Amazon Redshift and Dremio compete in the data warehousing and analytics category. Amazon Redshift seems to have the upper hand due to its robustness in handling large-scale data and ease of integration with the AWS ecosystem.
Features: Amazon Redshift offers impressive scalability for handling petabytes of data, a robust performance engine, and tight integration with AWS services like S3 and Redshift Spectrum. It also supports column-store data, enhancing compression and read speeds. Dremio's notable features include federated querying which simplifies accessing multiple data sources, intuitive data lineage tracking for better auditability, and seamless integration with various databases and data lakes like Amazon S3 and Azure Data Factory.
Room for Improvement: Amazon Redshift users face challenges with snapshot restoring, AWS IAM integration, and handling complex data types. Real-time integration and query optimization tools require enhancement to simplify data loading processes. Dremio needs to improve its Delta connector support, make dynamic scaling more intuitive, and enhance performance handling for large query operations. Improvements in SQL query support and memory management are also crucial.
Ease of Deployment and Customer Service: Amazon Redshift is extensively deployed in the Public Cloud, offering comprehensive but sometimes costly customer support. Dremio stands out with its flexibility across Public and Hybrid Clouds and On-premises environments, maintaining robust technical support and satisfactory customer service. Both products need to enhance interaction efficiencies with customers.
Pricing and ROI: Amazon Redshift is known for its competitive pricing model, beneficial for extensive data needs, though possibly expensive for smaller businesses. Its pay-as-you-go framework aids cost management, with initial setup investments necessary. Dremio offers a more attractive cost proposition compared to competitors like Snowflake and potentially high ROI, with broad impact from efficient data management practices.
Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.
Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.
It's costly when you enable support.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
The scalability part needs improvement as the sizing requires trial and error.
Dremio's scalability can handle growing data and user demands easily.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
I rate Dremio a nine in terms of stability.
They should bring the entire ETL data management process into Amazon Redshift.
Integration with AI could be a good improvement.
Starburst comes with around 50 connectors now.
It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.
The cost of technical support is high.
It's a pretty good price and reasonable for the product quality.
The pricing of Amazon Redshift is expensive.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
Scalability is the best feature of Amazon Redshift. Amazon Redshift handles scalability automatically, so we do not need to scale up or down; it is all managed by Redshift.
Scalability is also a strong point; I can scale it however I want without any limitations.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
| Product | Market Share (%) |
|---|---|
| Dremio | 7.1% |
| Amazon Redshift | 7.6% |
| Other | 85.3% |
| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 21 |
| Large Enterprise | 28 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.
Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.
The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Amazon Redshift Functionalities
Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:
Reviews from Real Users
“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS
“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini
Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.
Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.
What are Dremio's most valuable features?Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.
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