real-time

Everything We’ve Known About Data Movement Has Been Wrong

Data movement remains a perennial obstacle in systems design. Many talented architects and engineers spend significant amounts of time working on data movement, often in the form of batch Extract, Transform, and Load (ETL). In general, batch ETL is the process everyone loves to hate, or put another way, I’ve never met an engineer happy with their batch ETL setup. In this post, we’ll look at the shift from batch to real time, the new topologies required to keep up with data flows, and the...


smart-meter

MemSQL Manages Smart Meter Data with Leading Gas and Electric Utility Enterprise

Smart gas and electric meters produce huge volumes of data. A small MemSQL cluster of 5 nodes easily handles massive quantities of data like the workloads from leading gas and electric utility enterprises. In one particular use case, over 200,000 meter readings per second load into the MemSQL database while users simultaneously process queries against that data. Millions of meters sending between 10 and 30 sensor readings every hour leading to billions of rows of data. Just an initial part of a...


real-time insights

Global Pulse of Real-Time Data: 16 Definitive Insights

Each morning, millions of individuals wake up to emails sent throughout the night, text messages, Facebook notifications, and reminders that we are only 1,000 steps away from hitting our weekly goal. And though we might bemoan the occasional alert reminding us to pay our rent or hit the gym, the truth is we live in a real-time world where technology has evolved to effortlessly guide us through every aspect of our day. So how do enterprises deliver seamless experiences to millions of consumers in...


Real-Time Roadshow Rolls into Phoenix, Arizona

We’re packing our bags and heading to the Southwest to kick off the first ever MemSQL Real-Time Roadshow! Healthcare, education, aerospace, finance, technology, and other industries play a vital role in Phoenix, home to leading corporations like Honeywell, JP Morgan, AIG, American Express, Avnet, and UnitedHealth Group. Businesses in these industries face the constant challenge of keeping up with the high expectations of users and consumers that demand personalized and immediate services. To...


Election 2016: Analyzing Real-Time Twitter Sentiment with MemSQL Pipelines

November is nearly upon us, with the spotlight on Election 2016. This election has been amplified by millions of digital touchpoints. In particular, Twitter has risen in popularity as a forum for voicing individual opinions as well as tracking statements directly from the candidates. Pew Research Center states that “In January 2016, 44% of U.S. adults reported having learned about the 2016 presidential election in the past week from social media, outpacing both local and national print...


MemSQL at OOW16

MemSQL and Oracle: Better Together

Oracle OpenWorld 2016 kicks off on September 18th in San Francisco with ten tracks, including a Data Center track highlighting innovation in databases including MemSQL and Oracle. We built MemSQL to be a flexible ecosystem technology, as exemplified by several features. First, we offer users flexible deployments – whether it’s hybrid cloud, on-premises, VMs, or containers. Second, our connector tools are open source, such as MemSQL Spark Connector and Streamliner, which lets you...


real-time predictions

Predictions 2016: the Impact of Real-Time Data

Prediction 1. The industrial internet moves to real-time data pipelines The industrial internet knits together big data, machine learning, and machine-to-machine communications to detect patterns and adjust operations in near real time. Soon the industrial internet will expand by definition to include the Internet of Things. The detection of patterns and insights often comes with a price: time. While the goal of machine learning is to develop models that will prove useful, dealing with large...


Modern Database Characteristics

Characteristics of a Modern Database

Many legacy database systems are not equipped for modern applications. Near ubiquitous connectivity drives high-velocity, high-volume data workloads – think smartphones, connected devices, sensors – and a unique set of data management requirements. As the number of connected applications grows, businesses turn to in-memory solutions built to ingest and serve data simultaneously. Bonus Material: Free O’Reilly Ebook – learn how to build real-time data pipelines with modern...


Case Study: How Tradelab Enables Real-Time Bidding with MemSQL

Tradelab, a programmatic marketing platform company based in France, uses MemSQL to process and analyze real-time bidding data for hundreds of customers. Challenge: NoSQL Data Latency The Tradelab real-time ad serving platform requires a heavy mixed read/write workload, and the NoSQL database they had in place was introducing unnecessary data latency into the ad-bidding process. The company began searching for a replacement – a true real-time data management solution with two key...


O'Reilly Webcast

Building Real-Time Data Pipelines through In-Memory Architectures [Webcast]

In the era of universal connectivity, the faster you can move data from point A to B the better. Equipping your organization with the ability to make frequent decisions in an instant offers information and intelligence advantages, such as staying one step ahead of the competition. This is especially important when incoming data is arriving at a relentless pace, in high volume, and from a variety of devices. As our customers tap into new sources of data or modify to existing data pipelines, we...