Data Warehouse

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...


design blog

Designing for a Database: What’s Beyond the Query?

Even the most technically-minded companies need to think about design. Working on a database product at a startup is no different. But this comes with challenges, such as figuring out how to implement the human-centered design methodology at a technical company, but also contribute to building a design process that everyone agrees with across the organization. This blog will detail how product design is done at MemSQL as well as highlight how to design enterprise products at a startup.   How do...


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...


1.3 Billion NYC Taxi Rows into MemSQL Cloud

Experience teaches us that when loading data into a database, in whatever form ― normalized, denormalized, schema-less, hierarchical, key-value, document, etc ― the devil is always in the data load. For enterprise companies in the real-time economy, every second saved means improved efficiency, productivity, and profitability. Thankfully, MemSQL Cloud makes your enterprise data fast to load and easy to access. You can spin up a cluster in MemSQL Cloud in minutes and load data very quickly...


Key Considerations for a Cloud Data Warehouse

Data growth and diversity has put new pressures on traditional data warehouses, resulting in a slew of new technology evaluations. The data warehouse landscape offers a variety of options, including popular cloud solutions that offer pay-as-you-go pricing in an easy-to-use and scale package. Here are some considerations to help you select the best cloud data warehouse. First, Identify Your Use Case A cloud data warehouse supports numerous use cases for a variety of business needs. Here are some...


A Deeper Look at MemSQL Cloud

What’s new with MemSQL? First, we announced the general availability of MemSQL Cloud, our managed service offering. Second, we are fulfilling our plans to be the world’s leading real-time data warehouse. Now Chief Information, Data, and Analytics officers can make a strategic choice for the future: Freedom to choose a managed service for fast execution and convenience Deploy on any public or private cloud Or mitigate expensive appliances with a scale-out on-premises solution Where did the...


Data Warehouse Rescue

Seeking a Rescue from a Traditional RDBMS

In the Beginning Years ago, organizations used transactional databases to run analytics. Database administrators struggled to set up and maintain OLAP cubes or tune report queries. Monthly reporting cycles would slow or impact application performance because all the data was in one system. The introduction of custom hardware, appliance-based solutions helped mitigate these issues, and the resulting solutions were transactional databases with column store engines that were fast. Stemming from...


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...


big data changing

From Big to Now: The Changing Face of Data

Data is changing. You knew that. But the dialog over the past 10 years around big data and Hadoop is rapidly moving to data and real-time. We have tackled how to capture big data at scale. We can thank the Hadoop Distributed File System for that, as well as cloud object stores like AWS S3. But we have not yet tackled the instant results part of big data. For that we need more. But first, some history. Turning Point for the Traditional Data Warehouse Internet scale workloads that emerged in the...


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...