Insights generated from data is the foundation for smart business decisions. And Business Intelligence tools such as Tableau, MicroStrategy, Looker, or Qlik, which are staples in the business decision process, favor SQL as the language of choice for data analysts and data mining. The Aerospike Connect for Presto enables business and data analysts to use ANSI SQL to query data stored in Aerospike via Presto, a highly parallel and distributed SQL query engine.

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How it works

Aerospike is a highly scalable NoSQL data platform, and its Hybrid Memory Architecture™ makes it an ideal database for analytics workloads. It is typically deployed into real-time environments managing terabyte-to-petabyte data volumes and supports millisecond read and write latencies. Aerospike Connect for Presto enables business and data analysts to use ANSI SQL, which they are very comfortable with, to query data stored in Aerospike via Presto. Presto is a highly parallel and distributed SQL query engine. Further, it is multi-tenant and capable of concurrently running hundreds of memory, I/O, and CPU-intensive queries, and scaling to thousands of workers.

Presto: High-level architectural deployment

Figure 1. Aerospike Connect for Presto

Why Aerospike Connect for Presto

Run ANSI SQL queries in-place on massive amounts of data stored in Aerospike without complex and error-prone processes of copying data over other analytics systems.

Easy Integration with BI Tools
Analyze Aerospike data through your favorite Business Intelligence tools like Tableau, Qlik, Looker, and many more for visualization and reporting
Accelerate Time to Insight
Drastically reduce time to insight by combining Aerospike’s massively parallelism for speeding up interactive and ad-hoc Presto queries.
Build AI/ML Models
Move quickly from proof-of-concept to production of AI/ML models by pre-processing data stored in Aerospike using Python to create powerful models using popular libraries like Pandas and NumPy Scitkit-learn, etc.
Lower TCO
Lower the TCO of your analytics by enabling the analysis of massive, larger datasets with a smaller storage cluster footprint.

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