In this paper, we focus on the higher-volume, critical-app compute and storage that is the analytic database. We have undertaken the ambitious goal of evaluating the current vendor landscape and assessing the analytic platforms that have made, or are in the process of making, the leap to a new generation of capabilities in order to support the AI-based enterprise.
For this Roadmap Report, we chose technologies powered for an enterprise-class application in a midsize to large enterprise. We considered popularity and interest. The vendors/products chosen were:
- Actian Avalanche
- Amazon Redshift
- Cloudera Data Platform
- Google BigQuery
- IBM Db2 Warehouse on Cloud and Cloud Pak for Data
- Microsoft Azure Synapse Analytics
- Oracle Autonomous Data Warehouse
- Teradata Vantage
This benchmark compared Actian Zen Enterprise Server and MySQL Enterprise, both running on the same Ubuntu Linux in 8- and 16-core VMs as AWS EC2 instances, each using its ODBC driver. The benchmark used is derived from the TPC-C industry standard benchmark.
This paper examines the different flavors of DWaaS to ensure you get into the right one. Then it looks at some of the key criteria that should be considered when reviewing the cloud database.
Behind the covers of the DWaaS term, there are three distinct approaches. While all include most of the benefits for DWaaS, the differences mean that the benefits will accrue quite differently according to the fit of the model to the enterprise. These are vast enough differences to actually be the deciding factor in the DWaaS selection.
This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings:
- Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
- Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)
Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.
To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta.
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report.
This report outlines the results from an analytic performance test derived from the industry-standard TPC Benchmark™ DS (TPC-DS) to compare Cloudera Data Warehouse service (CDW)—part of the broader Cloudera Data Platform (CDP)—with four prominent competitors: Amazon Redshift, Azure Synapse Analytics, Google BigQuery, and Snowflake. Overall, the test results were insightful in revealing query execution performance of these platforms.
An Evaluation Guide for Technology Decision Makers.
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a selection that allows a worry-less experience with the architecture and its components.
We decided to take four leading platforms – Azure, AWS, GCP and Snowflake – for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise, and for an enterprise project, matters to cost.