Materialize

What happened to the operational data store? And what killed it? Our Co-Founder Arjun Narayan discusses in his latest blog. ODS was traditionally used to centralize real-time operational data across databases. But ODS has since lost its relevance with the introduction of data lakehouses, cloud data warehouses, and streaming systems. However, this has led to a broken status quo. Teams now need to manage two architectures: batch-based and stream-based systems. And yet operational use cases are still difficult to perform. This has given rise to the cloud operational data store. A cloud operational data store works natively on streams of data, provides full SQL support, easily performs complex joins, and pushes updates downstream. So, is ODS back from the dead? Read the blog for a full overview -> https://lnkd.in/e-FjPvZq

Our latest benchmark report showed that Materialize offers 100x greater throughput with 1000x lower latency vs. Aurora PostgreSQL with costs being equal. What does this mean? If you’re offloading complex queries onto an Aurora read replica, you can now do more ( 📈 ) for less (💰) with Materialize! As an operational data store, Materialize allows you to offload complex queries from your OLTP system in a fast and cost-efficient way, all while maintaining consistency. That means you’re saving money while greatly speeding up your operational workflows. Read our benchmark report for a full overview. BENCHMARK REPORT -> https://lnkd.in/egQJBi_A

Is your OLTP database straining due to complex queries? Performing queries in-place strains the database. But read replicas produce lag. What you really need is an incremental view maintenance replica. In our latest blog, Materialize CEO Nate Stewart discusses how IVMRs are the ideal method for offloading complex queries from your OLTP database. IVMRs give you a massive head start on queries, without sacrificing freshness or correctness. The combination of these two approaches enables IVMRs that can deliver 1000x performance for read-heavy workloads, without losing freshness, and do so at a fraction of the price of a traditional replica. IVMRs take the DRY – or don’t-repeat-yourself – approach to the extreme. They can determine, as updates come in, the exact amount of new work that needs to be done to update a materialized view. And then, in an also DRY-like fashion, when you query those views, the heavy computational lifting has already been done and can be reused as a starting point. Read the blog for the full overview -> https://lnkd.in/ea5wRfUb

Materialize vs. PostgreSQL: Materialize delivers 100x greater throughput with 1000x lower latency. Our latest blog examines the performance of Materialize vs. Aurora PostgreSQL read replicas for computationally intensive workloads. These benchmarks show that Materialize is a much better option for offloading complex queries from OLTP systems. And unlike other solutions that offload computation from OLTP databases, Materialize does so without sacrificing correctness or requiring external change data capture (CDC) tools to move data between systems. Click here to learn what our benchmark tests uncovered about Materialize and Aurora PostgreSQL -> https://lnkd.in/egQJBi_A

🌟Build event-driven operational workflows with webhooks + Materialize! Send events over webhooks, join and process data in SQL, and push real-time updates to connected systems. This approach unlocks a long tail of new data sources that, when joined together, create operational value that is greater than the sum of the parts. 📺 Creating a webhook source has never been easier! Check out the new guided experience - no need to write SQL, no need to parse JSON manually. https://lnkd.in/gPJXt_3d

🚀 Our Materialize Fivetran Destination is now in Private Preview! 🚀 Now you can use Fivetran to easily sync data into Materialize and drive real-time operations on fresh data from all your SaaS applications and other sources. Our very own Parker Timmerman explains how we built this powerful integration using Fivetran's Partner SDK. Read it now! https://lnkd.in/gQ3_B25W

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