Compare Actian Avalanche vs. Apache Doris

Actian Avalanche is a hybrid cloud data warehouse service that is fully managed and engineered to achieve exceptional performance and scalability across various aspects, including data volume, the number of concurrent users, and the complexity of queries, all while remaining cost-effective compared to other options. This versatile platform can be implemented on-premises or across several cloud providers like AWS, Azure, and Google Cloud, allowing organizations to transition their applications and data to the cloud at a comfortable rate. With Actian Avalanche, users experience industry-leading price-performance right from the start, eliminating the need for extensive tuning and optimization typically required by database administrators. For the same investment as other solutions, users can either enjoy significantly enhanced performance or maintain comparable performance at a much lower cost. Notably, Avalanche boasts a remarkable price-performance advantage, offering up to 6 times better efficiency than Snowflake, according to GigaOm’s TPC-H benchmark, while outperforming many traditional appliance vendors even further. This makes Actian Avalanche a compelling choice for businesses seeking to optimize their data management strategies.

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.