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Cloud Database Performance Benchmark

Read this cloud analysis paper for a benchmark of Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

Link to report.

Data Warehouse in the Cloud Benchmark

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. This benchmark study focuses on the performance of cloud-enabled, enterprise-ready, relationally based, analytical workload solutions from Microsoft Azure SQL Data Warehouse and Amazon Redshift.

The benchmark tested the scalability of corporate-complex workloads in terms of data volume with 30TB of data. The testing was conducted using as similar a configuration as can be achieved across Azure and Amazon Web Services (AWS) offerings.

Link to report.

Cloud Database Performance Benchmark: Vertica in Eon Mode and Snowflake

Organizations rely on Big Data platforms to analyze large volumes of data from a variety of sources to derive timely insights on everything from fraud detection, to customer churn, predictive maintenance and more. As more organizations move this data to the cloud for improved economics and operational simplicity, choosing the most performant and cost-effective data analytical solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard data benchmark principles to evaluate the performance of two cloud-optimized data analytical solutions architected for the separation of compute and storage — Vertica in Eon Mode and Snowflake Computing.

Link to report.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Impala

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Impala. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Snowflake

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Snowflake. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Amazon Redshift

We conducted this benchmark study, which focuses on the performance of cloud-enabled1 , enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Amazon Redshift. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Microsoft SQL Server

We conducted this benchmark study, which focuses on the performance of cloud-enabled , enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Microsoft SQL Server. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance at scale.

Link to paper.

The Need for an Intelligent Data Platform

In this paper, I will review information’s importance to business, connect data architecture to business success, define data maturity and discuss how to architect information and improve data maturity efficiently with an Intelligent Data Platform.

The Informatica Intelligent Data Platform (IDP) is an integrated end-to-end data management platform to spur data maturity and enable business initiatives with the right data at the right time. IDP also aims to decrease complexity by providing a unified platform for enterprise data, connectivity, metadata, and operations. This brings the entire realm of data management under a single umbrella.

Link to paper.

Benchmarking Enterprise Streaming Data and Message Queuing Platforms

This category of data is known by several names: streaming, messaging, live feeds, real-time, event-driven, and so on. This type of data needs special attention, because delayed processing can and will negatively affect its value—a sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—all can be of immense value to a decision maker, but only if he or she is alerted in time to affect the outcome.

We will introduce and demonstrate a method for an organization to assess and benchmark—for their own current and future uses and workloads—the technologies currently available. We will begin by reviewing the landscape of streaming data and message queueing technology. They are alike in purpose—process massive amounts of streaming data generated from social media, logging systems, clickstreams, Internet-of-Things devices, and so forth. However, they also have a few distinctions, strengths, and weaknesses.

Link to paper (fee).

Moving the Enterprise Analytical Database – A Guide For Enterprises: Strategies And Options To Modernizing Data Architecture and the Data Warehouse

The benefits of modern data architecture are as follows:

  1. It ensures the ability of the data analysis function of the organization to actually do analysis rather than restrict it to data hunting and preparation almost exclusively.
  2. It provides the ability to maneuver as an organization in the modern era of information competition with consistent, connected data sets with every data set playing a mindful and appropriate role.
  3. It enables a company to measure and improve the business with timely key performance indicators, such as streamlining your supply chain or opening up new markets with new products and services supported by technology built for analytics.

This paper will help an organization understand the value of modernizing its data architecture and how to frame a modernization effort that delivers analysis capabilities, diverse yet connected data, and key performance measures.

Link to paper (fee)