Name: Dremio: The Forever-Free Lakehouse Platform
Uploaded: 2022-03-01T16:38:48.000Z
Duration: 1 min 9 s
Description: Built for SQL, with a Git-like experience, and an open source foundation – Dremio’s forever-free lakehouse platform helps you drive better decisions, by giving data consumers easier access to the freshest, most complete data, without waiting. Learn more: https://www.dremio.com/platform.

Digital Adoption

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Communications Platform as a Service (CPaaS)

Partner Relationship Management

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Payment Orchestration

Finance and Accounting

Applicant Tracking

Contractor Management

Corporate Learning Management

HR Management

Payroll

Talent Intelligence

Talent Management

Vendor Management Systems (VMS)

Workforce Analytics

Workforce Management

Human Resources

Application Server

Archiving

Blockchain Analysis

Bot Mitigation

Browser

Cloud Storage

Compliance Consulting

Composable Infrastructure

Computerized Maintenance Management Systems (CMMS)

Data Center Outsourcing (Europe)

Data Masking

Desktop Management

Desktop Outsourcing

Email Encryption

Enterprise Architecture Management

Fraud Detection

IT Operations Analytics

Integration Platform as a Service (iPaaS)

Network Performance Monitoring

SOA Application Gateways

Smart Contracts

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Content Management

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Domain Registration Services

Email Marketing

Heatmap and Session Recording

IP Geolocation

Local Listing Management

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Survey & Forms Building

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Marketing

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Prototyping

Professional Services

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IOMETE

### Query Performance and Execution
Users consistently highlight Dremio's query performance and execution as standout features, praising its ability to deliver lightning-fast query speeds and optimize data retrieval processes. The platform's efficient query engine and execution capabilities have garnered positive feedback, with users commending its ease of use and the significant performance improvements it offers. Additionally, Dremio's focus on enhancing query performance through features like reflections has been well-received, contributing to a smoother and more efficient data querying experience for users across various data sources.

### Data Access and Integration
Users generally believe that Dremio excels in data access and integration functionality. The platform is praised for its ease of configuring multiple data sources and creating virtual datasets that enable unions across them. Users appreciate the performance for data retrieval, highlighting Dremio's ability to handle terabytes of data efficiently. However, some users express concerns about the lack of certain database connectors and suggest improvements in the user interface for a more streamlined experience. Despite these minor drawbacks, Dremio is recognized for its innovative approach to scalable data access and its contribution to enhancing productivity within organizations.

### Performance Optimization
Users consistently highlight Dremio's performance optimization as a standout feature, emphasizing its ability to accelerate queries and enhance data processing speed. The platform's performance optimization capabilities have garnered praise for streamlining analytics and machine learning processes, enabling efficient data insights without the need for extensive data engineering skills. Additionally, users appreciate Dremio's responsiveness in managing high workloads and delivering fast response times for numerous management dashboards, showcasing its effectiveness in optimizing performance across diverse use cases.

### Data Lakes and Warehousing
User feedback on Dremio's data lakes and warehousing capabilities varies across reviews. While some users praise Dremio for its ability to provide direct and fast access to data in the data lake, others express concerns about limitations when dealing with large datasets and the need for better support for certain data formats like Iceberg. The consensus seems to be that Dremio offers a solid foundation for connecting applications and BI tools to data lakes, but there are areas where improvements could enhance its performance and usability in data warehousing scenarios.

### Database and Storage Technologies
User consensus seems to be that Dremio's database and storage technologies capabilities are a strong point. Reviewers appreciate the ease of accessing data from various platforms like HDFS, Oracle RDBMS, and SQL Server. The standardized semantic layer eliminates the need for data duplication and accelerates queries, enhancing overall data management efficiency. However, some users have highlighted areas for improvement, such as the lack of certain database connectors and the need for UI enhancements. Despite these minor drawbacks, Dremio's database and storage technologies receive positive feedback for their ability to streamline data access and enhance data lake utilization.

### Data Management and Retrieval
Users generally praise Dremio's efficient data management and retrieval capabilities. They appreciate the platform's ability to provide fast and direct access to data in a data lake, reducing the time required to access data and minimizing the IT footprint. While some users note the manual management required for reflections and the lack of a managed service on the Microsoft Azure platform as drawbacks, overall, Dremio's user-friendly interface and modernized approach to data management have garnered positive feedback. The platform's support for creating semantic layers without IT involvement and its adaptability to various use cases, from management reporting to AI use cases, have contributed to its favorable reception among users.

### SQL and BI Tools
Opinions on Dremio's SQL and BI tools capabilities vary among users. While some praise the ease of use and the ability to connect to multiple data sources effortlessly, others have highlighted areas for improvement, such as the learning curve associated with the interface and the need for enhanced usability features. Users appreciate the SQL-based approach and the data virtualization aspect, which simplifies access to heterogeneous sources. Additionally, the platform's ability to accelerate ad-hoc and self-service queries has been positively noted. However, there are suggestions for enhancements to make the tool more user-friendly and efficient in handling complex queries.

### Analytics and Insights
Users across various industries have unanimously praised Dremio's robust analytics and insights capabilities. The tool's intuitive interface and powerful features have enabled organizations to derive valuable insights quickly and efficiently. Clients have highlighted the tool's ability to streamline data analysis processes, leading to significant cost and time savings. Additionally, Dremio's support team has been commended for their responsiveness and assistance in leveraging the tool's analytics functionalities effectively. The consensus among users is that Dremio excels in providing advanced analytics solutions that drive informed decision-making and enhance overall business performance.

### Advanced Analytics and Machine Learning
Users consistently praise Dremio for its robust capabilities in accelerating analytics and machine learning tasks. The tool's ability to streamline data preparation for AI and ML purposes, along with providing a self-service DataHub for business users, has garnered positive feedback. Dremio's role as a foundational layer in the analytics stack, offering direct SQL access to vast amounts of data and connectivity to advanced analytic, ML, and data visualization tools, has been highlighted as a key strength by users. This emphasis on enhancing analytics and machine learning workflows aligns well with users' strategies to reduce reliance on specialized data warehouse technologies.

### Data Transformation and Processing
Users generally praise Dremio for its streamlined data transformation and processing capabilities. The consensus among reviewers is that Dremio simplifies the data transformation process, allowing for quick exploration and structuring of large datasets without the need for extensive IT involvement. The platform's ability to reduce ETL developments and provide fast query processing times has been highlighted as a significant advantage by users. Additionally, Dremio's feature set, including reflections for enhanced performance and a user-friendly interface, has garnered positive feedback for enabling efficient data processing and transformation tasks.

### Data Virtualization and Exploration
Users consistently praise Dremio's data virtualization and exploration capabilities, highlighting its ability to provide a unified query layer for exploring disparate data sources. The platform's data virtualization and democratization features have been particularly well-received, with users noting the simplicity of the UI and the low entry threshold for end users. Dremio's focus on central data virtualization and democratizing data has resonated positively with users, especially those with a SQL background. Additionally, the platform's technical edge in enabling data exploration among heterogeneous sources has been commended for streamlining data quality analysis tasks.

Users have made several recommendations based on their experiences with Dremio as a centralized data platform for organizations. The most common recommendations include utilizing ODBC or pyarrow flight for queries over the Python API to improve performance. Running a proof of concept to gain confidence in Dremio is another frequent suggestion. Lastly, users advise trying out the community AWS Edition of Dremio as it provides most, if not all, of the necessary features required by organizations. Additionally, users appreciate the thorough documentation provided by Dremio, which aids in understanding and effectively utilizing its features and functionalities.

Dremio has proved useful in solving various data-related business problems. Users have found that Dremio's ability to break down data silos is a valuable asset. Its architecture unlocks data in the data lake and warehouse, making it more accessible and usable by a larger population. Additionally, users can perform queries across multiple data sources, resulting in speedy data analytics and increased efficiency.

Moreover, Dremio's user-friendly UI has helped business users create semantic layers on top of the data lake without IT involvement, resulting in significant time-to-market advantages. The tool simplifies the process of Data Analytics and provides superior performance compared to legacy technology stacks. Furthermore, its direct SQL access to PB of data allows users who like using SQL to query their data fast, despite it being stored in different locations with different formats. Finally, Dremio helps organizations be more resilient through Black Swan events by guiding their insights and actions based on data, resulting in quicker decision-making.

Easy to use and deploy: Multiple reviewers found Dremio to be an easy-to-use tool for data analytics, with a simple deployment process. Users have been impressed with the ease of getting started with the software, especially for those without an IT background.

Great customer support: Several users praised Dremio's customer support team for their responsiveness and knowledge in addressing queries. The professional services provided by Dremio were also commended as being world-class.

Ability to bring silos of data together: Many reviewers appreciated how Dremio allowed them to bring various formats and sources of data from different departments into a single platform. This feature made it easy for both analysts and non-analysts to search, find, and query data effectively without going through the ETL/ELT process.

Steep Learning Curve: Some users have found Dremio difficult to use due to a steep learning curve. They report that while the software is not unbearable, it can take some time to get used to all of its features and functionality.

Limitations on Columns and Fields: The hard limits on the number of columns and fields in Dremio can be problematic for certain use cases, according to some users. This limitation may prevent them from using the software as effectively as they would like.

Lack of Support for Database Views and External Decryption Libraries: A significant disadvantage reported by some users is the lack of support for database views and external decryption libraries. This limitation can create issues when trying to integrate with other tools or work with specific types of data.

Dremio is presented as an intelligent lakehouse platform designed to eliminate bottlenecks by unifying data sources without ETL, simplifying the creation of datasets and delivering autonomous performance optimization to accelerate AI.

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.

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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

Current Generation

Previous Generation

Redshift Spectrum

Redshift Managed Storage

Amazon Redshift

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Data Lakehouse

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Dremio Cloud Demo: Learn How to Get Started with the Data Platform in 5 Minutes

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Dremio: The Forever-Free Lakehouse Platform

Built for SQL, with a Git-like experience, and an open source foundation – Dremio’s forever-free lakehouse platform helps you drive better decisions, by giving data consumers easier access to the freshest, most complete data, without waiting. Learn more: https://www.dremio.com/platform.

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What is Dremio?