White Papers Plus

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)

Transitioning from PostgreSQL to an Analytical Database for Higher Performance and Massive Scale

In today’s data driven world, where effective decisions are based on a company’s ability to access information in seconds or minutes rather than hours or days, selecting the right analytical database platform is critical.

Read this McKnight white paper to learn:

  • Which criteria to consider for an analytical database
  • The process for transitioning away from PostgreSQL
  • Transition success stories from Etsy, TravelBird and Nimble Storage

Link to paper.

Sector Roadmap: Unstructured Data Management 2017

This Sector Roadmap is focused on unstructured data management tool selection for multiple uses across the enterprise. We eliminated any products that may have been well-positioned and viable for limited or non-analytical uses, such as log file management, but deficient in other areas. Our selected use cases are designed for high relevance for years to come and so the products we chose needed to match all these uses. In general, we recommend that an enterprise only pursue an unstructured data management tool capable of addressing a majority or all of that enterprises’ use cases.

In this Sector Roadmap, vendor solutions are evaluated over five Disruption Vectors: query operations, search capabilities, deployment options, data management features, and schema requirements.

Link to report (fee).

Sector Roadmap: Modern Enterprise Grade Data Integration 2017

This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise.

Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.

di

Link to report (fee).