RESEARCH

RESOURCES / RESEARCH

Helping Different Companies With Information Management

RADAR for Master Data Management

October 16, 2021

The economic payback of master data management starts with “build once, use often.” Master data must be accessible to each new application that is built, and these applications routinely have up to 50% effort and budget directed toward collecting master data.

This GigaOm Radar report evaluates the capabilities of notable players in the MDM space against the decision-making framework established in the Key Criteria Report for Master Data Management.

Get the report here.

Enterprise Analytic Solutions 2021

October 16, 2021

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
  • Snowflake
  • Teradata Vantage
  • Vertica
  • Yellowbrick

Get the report here.

Embedded Database Performance Report

October 16, 2021

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.

Get the report here.

Choosing the Right Data Warehouse-as-a-Service for Your Analytical Needs

October 16, 2021

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.

Get the report here.

SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING, MICROSOFT SQL SERVER EVALUATION: AZURE VIRTUAL MACHINES VS. AMAZON WEB SERVICES EC2

October 13, 2021

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:

  1. Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
  2. Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)

Get the report here.

CLOUD DATA SECURITY COMPARISON: IMMUTA AND APACHE RANGER

October 13, 2021

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.

Get the report here.

ENTERPRISE READINESS OF CLOUD MLOPS

October 13, 2021

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.

Get the report here.

CLOUD DATA WAREHOUSE PERFORMANCE TESTING: CLOUDERA DATA WAREHOUSE, AMAZON REDSHIFT, MICROSOFT AZURE SYNAPSE, GOOGLE BIGQUERY, AND SNOWFLAKE

October 13, 2021

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.

Get the report here.

KEY CRITERIA FOR EVALUATING MASTER DATA MANAGEMENT

October 13, 2021

An Evaluation Guide for Technology Decision Makers.

Get the report here (GigaOm wall).

BENCHMARK REPORT: TRILLION EDGE KNOWLEDGE GRAPH

August 10, 2021

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources.

Get the report here.