Data Warehouse

2018 Gartner Magic Quadrant for Data Management Solutions for Analytics

The data management solutions for analytics market is evolving. Disruption is accelerating in this market, with more demand for broad solutions that address multiple data types and offer different delivery models. We are hosting complimentary access to the full Gartner Magic Quadrant for Data Management Solutions for Analytics, so you can learn more about what’s happening in this space. Access Here → MemSQL Positioned as Challenger in Gartner Magic Quadrant MemSQL has been positioned in...


A Brief Introduction to MemSQL

A Brief Introduction to MemSQL

We know choosing or evaluating a new database technology can be challenging due to the variety of choices available. In a recent webcast, we shared various use cases businesses face with traditional database and data warehouse technologies, key differentiators and architectures of MemSQL, sample applications and customer case studies, and a quick demo of MemSQL. MemSQL provides an adaptable database for real-time applications that unite transactions and analytics in a single high-performance...


Using MemSQL within the AWS Ecosystem

The database market is large and filled with many solutions. In this post, we will take a look at what is happening within AWS, the overall data landscape, and how customers can benefit from using MemSQL within the AWS ecosystem. Understanding the AWS Juggernaut At AWS re:Invent in December 2017, AWS CEO Andy Jassy revealed that the business is at a revenue run rate of $18 billion, growing 42 percent per year. Those numbers are staggering and showcase the importance Amazon Web Services now plays...


Data Warehouses and the Flying Car Dilemma

Traditional data warehouses and databases were built for workloads that manifested 20 years ago. They are sufficient for what they were built to do, but these systems are struggling to meet the demands of modern business with the volume, velocity, and user demand of data. IT departments are being challenged from both ends. On one side, companies want to analyze the deluge of data in real time, or near real time. On the other side, on the consumption end, the need to analyze and get value out of...


Why You Need a Real-Time Data Warehouse for IoT Applications

As always-on devices and sensors proliferate, the data emitted from these devices provides meaningful insights to improve customer experiences, optimize costs, and identify new revenue opportunities. In a recent report, Taking the Pulse of Enterprise IoT from McKinsey & Company, 48 percent of respondents cited “managing data” as a critical capability gap related to their IoT initiatives.1 The data infrastructure behind IoT applications requires a high performing and easy-to-access...


Modern Data Warehousing, Meet AI

We are enchanted by the possibility of digital disruption. New computing approaches, from cloud to artificial intelligence and machine learning, promise new business models and untold efficiencies. We are closing the gap between science fiction and business operations. A Quick Look Back Let’s take a quick look back at data processing, and then come back to the industry frontier. It started with data and the place to put it, which became the database. Then came a desire to understand the data...


hybrid cloud

The Real-Time Data Warehouse for Hybrid Cloud

As companies move to the cloud, the ability to span on-premises and cloud deployments remains a critical enterprise enabler. In this post, we’ll review the basics of a hybrid cloud model for MemSQL deployments to fit conventional and future needs. MemSQL Background MemSQL is a real-time data warehouse optimized for hybrid cloud deployments, and excels at operational use cases. MemSQL users are drawn to the ability to load and persist live data at petabyte scale while simultaneously querying...


real-time data warehousing

Real-Time Data Warehousing for the Real-Time Economy

In the age of manual decision making based on predictable data formats, data feeds, and batch processing times, enterprise businesses stayed current with ad hoc analyses and periodic reports. To generate analyses and reports, businesses relied on the traditional data warehouse. Using extraction, transformation, and load batch processes, the traditional data warehouse standardized disparate data into normalized schemas and pre-computed cubes. With the data shaped into pre-configured dimensions...


Database Multi-Tenancy in the Cloud and Beyond

In today’s wave of Enterprise Cloud applications, having trust in a data store behind your software-as-a-service (SaaS) application is a must. Thus, multi-tenancy support is a critical feature for any enterprise-grade database. This blog post will cover the ways to implement multi-tenancy, and best practices for doing so in MemSQL. As customer table sizes grow, you will need to scale out your multi-tenant database across dozens of machines. To support rich analytics about your customers or...


Magic Quadrant

Gartner Magic Quadrant for Data Management Solutions for Analytics

The data warehouse as we know it has changed. Growth in data size and complexity, migration to the cloud, and the rise of real-time use cases are forces pushing enterprise organizations to expect more from their data warehouse. Evidence of this trend can be found in the latest Gartner Magic Quadrant Report, which has dropped data warehouse from its title and graduated to Data Management Solutions for Analytics. This change has resulted in an expansion of types of vendors covered, while also...