Apache Impala vs Dremio: What are the differences?

Introduction

Apache Impala and Dremio are both open-source projects that provide fast and interactive SQL query capabilities on big data. However, there are key differences between the two that set them apart.

1. Data Processing Engine: Apache Impala is a massively parallel processing (MPP) SQL query engine that runs directly on Hadoop distributed file systems (HDFS) and Apache HBase. It provides low-latency queries by avoiding data movement. On the other hand, Dremio is a data-as-a-service platform that runs on cloud infrastructure and provides a self-service data experience. It optimizes its own execution engine called HyperScan, which leverages vectorized processing to speed up query performance.

2. Data Source Support: While both Impala and Dremio can connect to a variety of data sources, there are some notable differences. Impala focuses on providing SQL queries over HDFS and HBase. It supports Apache Kudu for high-performance analytics on fast data, and also integrates with Hadoop ecosystem components like Hive and Hue. Dremio, on the other hand, supports a wider range of data sources including cloud storage services like Amazon S3 and Azure Data Lake Store, and also popular databases like MySQL, PostgreSQL, and Oracle.

3. Optimization and Caching: Impala uses code generation techniques and runtime query optimization to achieve high performance. It also provides a metadata caching mechanism to avoid unnecessary disk I/O. Dremio, on the other hand, employs a data reflection feature that automatically indexes data subsets and materializes query results for future use. By caching and pre-processing data, Dremio can significantly accelerate subsequent queries.

4. Data Virtualization vs Data Lake: One of the key differences between Impala and Dremio lies in their approach to data storage. Impala treats data as a part of Hadoop data lake and relies on the existing data structures. It does not provide any data virtualization capabilities. Dremio, on the other hand, virtualizes data from multiple sources into a single, semantically-consistent view. It abstracts away the complexities of the underlying data sources and enables users to query data without having to know where it is physically located.

5. Enterprise-level Features: Impala offers various enterprise-level features like role-based access control (RBAC), Kerberos authentication, LDAP integration, and encryption at rest. It is well-integrated with the Hadoop ecosystem and provides easy integration with other tools like Apache Spark and Apache Ranger. Dremio, while still maturing as a platform, also provides enterprise-grade security features like authentication and authorization, as well as integration with existing identity providers. But additional features like backup and recovery are still being developed.

6. Community Support and Adoption: Apache Impala has been around for a longer time and has gained significant market adoption, especially within the Hadoop ecosystem. It has a large community of contributors and users, and its open nature allows for contributions from various organizations. Dremio, being a relatively younger project, is rapidly gaining popularity but has a smaller community. However, Dremio provides a more user-friendly interface and focuses on empowering data consumers, which has attracted interest from organizations looking for self-service data access.

In summary, Apache Impala and Dremio differ in their data processing engines, data source support, optimization and caching techniques, approach to data storage, enterprise-level features, and community support. These differences make both tools suitable for different use cases, depending on the specific requirements of the organization.