However, these new serverless architectures also require customers to consider the types of queries that will be run on this new platform. For instance, the benchmark results show that Google BigQuery’s serverless architecture enables IT departments to quickly launch a Big Data service without the need to manage scalability. However, enterprise IT stores data in multiple data stores, each of which has different deployment models and delivery speeds: Hadoop on-premises, Hadoop in the Cloud, RDBMSs on-premises, and databases in the Cloud.Īs enterprise buyers weigh which technologies to deploy, it is essential to consider the ramifications their choices will have for their users. The study, which evaluated Google BigQuery performance for key Business Intelligence (BI) workloads, reveals key insights that enterprise IT leaders should consider when modernizing their business intelligence infrastructure.īig Data’s “New Normal”: Heterogeneous & HybridĮnterprise CIOs are increasingly having to accommodate the reality of a complex Big Data world: the average employee expects to connect to any enterprise data with consistent speed, security and scale from any BI tool. San Mateo, California, ApAtScale, the first company to provide enterprises with a fast and secure self-service Business Intelligence platform for Big Data, released today the results of a Business Intelligence benchmark for Google BigQuery. AtScale releases industry’s first Business Intelligence Benchmark for Google BigQuery
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