Their focus on expanded support for cloud services should accelerate AI-driven analytics.
They’ve also created a blueprint for cloud-native analytics and BI. Arcadia Data works in distributed and decoupled architectures and enables low-cost scalability, elasticity and performance with usage-based pricing of cloud environments.
Arcadia Enterprise has the native ability for direct analyzation of data in storage like Amazon S3 and Azure Data Lake Storage Gen 2 (ADLS) that has no data movement or duplication. It allows customers to expand and reduce the resources they dedicate to its BI workloads depending on the need to optimize spending. This approach produces a cloud-native BIs service with direct access to object stores with built-in flexibility.
“With data management increasingly moving to object storage and cloud data warehouses, organizations naturally expect that BI applications can also benefit from the scale of data and real-time analytics,” says Priyank Patel, Arcadia Data co-founder and CPO. “Yet, traditional BI architectures cannot meet those expectations because they are a mismatch for cloud-native environments. For instance, traditional BI can only run on subsets of data. Although data ingestion can happen in real time, analytics lags while waiting on ETL batch cycles. Arcadia Data provides a cloud-native approach that frees companies from relying on those outdated analytic patterns, delivering an agile architecture that meets the new level of analytics scalability and performance companies demand.”
Arcadia Enterprise runs on all Apache Hadoop distributions including Amazon EMR and is integrated with services like Amazon Glue, Amazon Athena, Redshift, RDS, Aurora, Confluent, Databricks, Google BigQuery, and Snowflake. The update lets users make queries using like natural language query (NLQ) and high-scale visual mapping. It also features AI-driven dashboard acceleration.