Materialize

Materialize’s Chief Scientist Frank McSherry discusses how to create live data by just using SQL in our latest blog post. In the post, Frank builds a recipe for a generic live data source using standard SQL primitives and some Materialize functionality. Then he adds in various additional flavors: distributions over keys, irregular validity, foreign key relationships. It’s all based off of Materialize’s own auction load generator, but it’s written entirely in SQL and can be customized as your needs evolve. By the end of the blog, you’ll discover that the gap between your idea for live data and making it happen is just typing some SQL. READ BLOG -> https://lnkd.in/eFJcB_un

OLTP offload is when expensive queries are moved off of an OLTP database and performed on other data systems. This improves performance and stability, cuts costs, and preserves systems of record. Many teams turn to read replicas and other bandaids to execute OLTP offload. But these stopgaps are not durable in the long-term. To perform effective OLTP offload, teams need an operational data warehouse. Some of the topics covered in the following white paper include: - Why running expensive (i.e. high compute) queries on an OLTP database is challenging - OLTP offload: what does this process look like, and how it can improve database speed and stability - Different methods for OLTP offload, including performing queries on the primary database, scaling up, and read replicas - Why you should offload your expensive queries onto an operational data warehouse Download the free white paper to learn everything about OLTP offload, and find out why an operational data warehouse is the right solution. DOWNLOAD HERE -> https://lnkd.in/etpKX8ki

In our last blog on small data teams, we discussed the challenges they face when building streaming solutions. The limitations of the modern data stack require small data teams to build their own streaming services, but they often lack the time, resources, and skills to do so. In this regard, large teams have the advantage. But with the emergence of the operational data warehouse, small data teams can now leverage a SaaS solution with streaming data and SQL support to build real-time applications. In the following blog, we’ll discuss how operational data warehouses level the playing field for small data teams. Read blog here -> https://lnkd.in/eXz3cD5H

Consumers today expect real-time experiences. But small data teams have historically lacked the resources to build streaming data architectures. In the past, small data teams lacked the funds, technology, time, and skill sets required to create real-time data architectures. Building streaming data architectures from nothing was not within reach. However, with the emergence of the operational data warehouse, small teams now have a chance to level the playing field. The operational data warehouse offers a SaaS solution with streaming data and SQL support. In our new blog series, we will examine how small data teams build real-time architectures. In our first blog in this series, you will discover: Why small data teams can't wait on real-time data How the problem starts: limitations in the modern data stack Streaming solutions: what are they and how they fall short Read the first blog below to understand the challenges small data teams have typically faced in building streaming solutions. READ NOW -> https://lnkd.in/egnsYQ5u

Our CEO, Nate Stewart, discusses the revolutionary impact of Differential Dataflow in this new piece of thought leadership. Like Mendeleev’s initial periodic table, the modern data stack has gaps. Even with the myriad of OLTP and OLAP databases, the logs, the queues, the caches, there are still missing elements. That changed in January 2013, when the missing element was discovered: Differential Dataflow. This solution allows for efficiently performing computations on massive amounts of data and incrementally updating those computations as the data changes. The blog discusses several impacts of Differential Dataflow, including: - Improving OLTP Performance and Stability with Incrementally Updated Views - Removing Data Silos by Joining Databases in Real Time with SQL - Enabling Team Autonomy and Scalability with an Operational Data Mesh Check out the blog here -> https://lnkd.in/evy4V-cm

Running your business on fresh, accurate data is crucial, but delivering it can be challenging. 🌟 Download this whitepaper to learn the essentials of building or buying the perfect solution to empower your business to operate on fast-changing data. 💡📈 https://lnkd.in/gsrxwf8p

🚀 Imagine if your business could make lightning-fast decisions and adapt instantly to changes! That's the power of fresh, real-time data, unlocking incredible potential by accessing and reacting to data changes as they happen. 🌟 In our latest whitepaper, we explore 5 game-changing benefits of a real-time data architecture and reveal how you can implement it swiftly and cost-effectively. Download it now! 📊💡 https://lnkd.in/gzPCEXRZ

Overloading your PostgreSQL database by running too many queries for fresh data? Learn how you can offload your queries to Materialize, improving the resilience and performance of their core PostgreSQL database while ensuring fast and fresh query results. This video shows you how to create a PostgreSQL source in Materialize in just a few minutes to deliver fresh results that stay up-to-date as new data arrives. https://lnkd.in/g9D6Aq5d

Rockset just announced that they were acquired by OpenAI. Congratulations to the amazing talent behind RocksDB! Unfortunately, a side effect of the acquisition is that the commercial Rockset product will be discontinued. If you are a @Rockset customer and exploring alternatives, we’d love to help! Sign up for a free trial of Materialize to learn more! https://lnkd.in/e6zu82Ce

The Materialize team built the first managed service that can securely connect to any Kafka cluster over AWS PrivateLink without requiring any broker configuration changes! We’ve already contributed the required changes back to the open source community. But in this blog post, we’ll take a deeper look at how we reconciled Kafka with private networking.  The post will examine why teams historically needed delicate network and broker configurations to connect to Kafka clusters. We’ll also detail how this method impacted the stability of network configurations. Then we’ll explain how we developed frictionless private networking for Kafka by using librdkafka. Check out the blog now -> https://lnkd.in/e6hUJd7F