Adaptation and Reinvention
Long term success hinges on adaptation and reinvention, especially in our dynamic world where nothing lasts forever. Especially with business, we routinely see the rise and fall of products and companies.
The long game mandates change, and the database ecosystem is no different. Today, megatrends of social, mobile, cloud, big data, analytics, IoT, and machine learning place us at a generational intersection.
Data drives our digital world and the systems that shepherd...
by Gary Orenstein
A customer asks potential vendors – I need a pig that can fly. Whoever can get me one, wins the deal.
Vendor 1 – The Engineer says “There is no such thing as a flying pig. Do not waste our time. We are not interested.”
Vendor 2 – The Geneticist says “I am going to create a new species of pig – one with wings.” He goes to work on a flying pig. He never comes back.
Vendor 3 – The Practical One says “Flying pig indeed! Yes, we can get you one.”
by Krishna Manoharan
For years, we have lived in a data processing world with two primary kinds of database systems, one for capturing incoming data and managing transactions, and another for answering questions and analyzing the data for insight and intelligence.
The first system is what we know as a database, a system designed so you can put data in and get data out reliably and quickly. The second system is generally referred to as a data warehouse.
There has long been a historical divide between these two...
This is a repost of an article by Ankur Goyal, VP of Engineering, published on Medium ⇒
The terms rowstore and columnstore have become household names for database users. The general consensus is that rowstores are superior for online transaction processing (OLTP) workloads and columnstores are superior for online analytical processing (OLAP) workloads. This is close but not quite right — we’ll dig into why in this article and provide a more fundamental way to reason about when...
by Lesia Myroshnichenko
In my last blog post, I investigated a Linux performance issue affecting a specific customer workload. In this post, I will introduce the tool I created to drive that investigation.
Recently, a customer was running a test where data was loaded into MemSQL via LOAD DATA. The customer’s third-party benchmarking tool found that MemSQL took twice as long to load the same amount of data as a competing database; however, the numbers reported by this tool did not make sense. Local tests had shown...
by Alex Reece
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...
by Kevin White
Photo: Martin Taylor
We often hear “How can I use MemSQL together with my Oracle database?”
As a relational database, MemSQL is similar to an Oracle database, and can serve as an alternative to Oracle in certain scenarios. Here is what sets MemSQL apart:
MemSQL is a distributed system, designed to run on multiple machines with a massively parallel processing architecture. An Oracle database, on the other hand, resides in a single, large machine, or a smaller fixed cluster size.
by Steven Camina
In the technology industry, when products or innovations last for a long period of time, they are often here to stay. SQL is a great example of this – it has been around for over 30 years and is not going away anytime soon. When Eric Frenkiel and Nikita Shamgunov founded MemSQL in 2011, they were confident in choosing the SQL relational model as the foundation for their database. But the database industry during that era was clamoring around NoSQL, lauding it as the next great innovation,...
by Carlos Bueno
The database market continues to surprise those of us who have been in it for a while. After the initial wave of consolidation in the late 1990s and early 2000s, the market has exploded with new entrants: column-stores, document databases, NoSQL, in-memory, graph databases, and more. But who will truly challenge the incumbents for a position in the Top 5 rankings? Oracle, IBM, Microsoft, SAP, and Teradata dominate the $33B database market. Will it be a NoSQL database? Will it be an open source...
by Bruce Armstrong
As adoption of in-memory databases grows at a faster and faster pace, IT leaders turn to research firms to find valuable use cases and guidance for purchasing options. We are thrilled to share that MemSQL was among the select companies that Forrester Research invited to participate in its 2015 Forrester Wave™ evaluation. In this evaluation, MemSQL was cited as a strong performer for in-memory database platforms. The report, The Forrester Wave™: In-Memory Database Platforms, Q3 2015,...
by Freja Mickos