Somrita Ganchoudhuri

Marketing Communications Manager @ RisingWave Labs

Rayees Pasha

Head of Product @ RisingWave Labs

Attending Kafka Summit can be a game-changer for professionals in the data industry. It is a unique opportunity to learn from leading experts and stay updated with the latest trends in Kafka technology.

Unlocking the Power of Kafka Summit: Top 4 Reasons for Attending it

Fortune Adekogbe

Community Contributor

This article discusses how event-driven computation works and the various tools that can be used to implement different stages of the event-driven computation process.

Getting Started with Event-Driven Computation

Runji Wang

Software Engineer

In our previous article, we introduced the concept of deterministic simulation and discussed the development of Madsim, a deterministic testing framework based on Rust's asynchronous programming ecosystem. In this article, we will explore the application of deterministic simulation in RisingWave, a cloud-native distributed database.

Applying Deterministic Simulation: The RisingWave Story (Part 2)

In this article, we will discuss the background and principles of deterministic simulation, introduce our deterministic testing framework Madsim, and share our experience applying deterministic testing to RisingWave.

Deterministic Simulation: A New Era of Distributed System Testing (Part 1) 

Yanghao Wang

Product Manager @ RisingWave Labs

RisingWave announces the release of RisingWave Cloud beta, a fully managed service of RisingWave Database. Let's review what RisingWave Cloud offers in this beta release. 

Announcing RisingWave Cloud Beta Release

Yingjun Wu

CEO @ RisingWave Labs

Ease of use, cost-efficiency, and correctness — in this article, we provide a comprehensive explanation from the users' point of view focusing on three crucial factors.

RisingWave and Apache Flink SQL: A Comparison

As a distributed SQL streaming database, RisingWave is well-optimized for stream processing. Let's take a look at whether it is meant for your stream processing application.

Is RisingWave the Right Streaming Database for Your Stream Processing Application?

Jovana

Community Manager @ RisingWave Labs

We are excited to announce the launch of Streaming Stories. This platform brings together stories from users around the world; all centered around their experiences with stream processing.

RisingWave Labs Launching a Webinar Series: Streaming Stories

Jon Chuang

Zhidong Guo

Storage Intern

Siyuan Wang

In this blog, we'll look at three techniques in RisingWave that leverage the power of S3-compatible object stores to achieve better performance.

Object Store Optimizations in RisingWave 

In this blog, our CEO and Founder, Yingjun Wu, will combine his software development and customer engagement experiences to discuss the past, present, and future of stream processing and streaming databases from a practitioner’s perspective.

Rethinking Stream Processing and Streaming Databases

Sooter Saalu

Materialized views are database objects that contain the results of your operations, calculations, or queries. They exist as a locally saved representation of your data that you can use and reuse without recalculating or computing data values. Using materialized views saves processing time and cost and allows you to front-load your frequently used or most time-consuming queries.  This article explores materialized views in modern-day databases, their use cases, why they are important, how they are connected with data analytics, and their limitations.  

How to Update Materialized Views in Real Time

Since its official open-source release on GitHub under the Apache 2.0 license on April 8, 2022, RisingWave has grown into a popular project with over 100 contributors and 3,700 stars. Let's take a step back and reflect on our open-source community.

RisingWave Community Retrospective and a Look Forward 

Let's take a look back at 2022! In this blog, we focus on five key updates from 2022.

A Look Back at 2022: 5 Key Highlights

Chi Zhang

Community Contributor @ RisingWave Labs

In database systems, we can speed up join computations by creating indexes. Here we examine RisingWave's data persistence process on its cloud-native storage, the implementation of shared state, and delta join.

Shared Indexes and Joins in Streaming Databases 

Tushar Malik

RisingWave Community Contributor

In a nutshell, RisingWave is a distributed, cloud-native streaming database. It is built from the ground up to ingest and process data from streaming messaging queues in a horizontally scalable way. I discovered the RisingWave Code Camp, a mentorship program for junior developers to advance their skills in software and database systems, through a friend who'd met the team at Kubecon EU. Unfamiliar with Rust and admittedly daunted at the prospect of working on a database, I was hesitant to apply but hey, I needed an excuse to learn Rust, and I was intrigued by the idea of working with the amazing people building RisingWave! 

Building a Source Connector: A RisingWave Code Camp Experience 

Tianyi Zhuang

Software Engineer @ RisingWave Labs

Seven months ago, we open-sourced RisingWave, a cloud-native streaming database written in Rust. In this blog, I will introduce how RisingWave manages the streaming state in our Hummock cloud storage engine.

State Management for Cloud Native Streaming: Getting to the Core

What’s next for real-time data systems? Let’s take a look at what startups are working on!

Future of Real-time Data Systems: A Recap of Current’22

The Cloud Native Computing Foundation brings Kubecon, its flagship conference, to promote education and advancement of cloud native computing. Kubernetes, Envoy, and Prometheus are just a few of the rapidly expanding cloud-native projects that the Cloud Native Computing Foundation supports, oversees, and directs as a member of the Linux Foundation. This time it's happening from October 24 – 28, 2022, in Detroit, Michigan.

Kubecon + CloudNativeCon: Top 3 reasons for attending it

We’re excited to announce that we have secured $36M in Series A funding to meet surging demand for streaming databases that enable users to make real-time decisions. This brings the total amount raised to more than $40 million. We plan to use this funding to accelerate our mission of democratizing stream processing technology that until now has been available to only a select few organizations.

RisingWave Labs Raises $36M in Series A Funding for its Stream Processing Platform 

Recently, RisingWave Labs' team attended Current 2022, where over 2,000 people from around the globe gathered to see what is new and exciting in the world of data streaming technologies. There was a wide range of sessions and events available — attendees could get a taste of what is happening in the world of streaming.

Current 2022: 4 themes you should not miss

The Next Generation of Kafka Summit by Confluent is around the corner. Join us at this event to learn about stream processing.

Current 2022: The Next Generation of Kafka Summit

We believe focusing on messaging is just as important as the focus on product differentiation. Keeping this in mind, we are changing our company name to RisingWave Labs. After all, RisingWave is at the heart of our innovation. And this change will allow us to stay close to our product and the RisingWave community.

We are RisingWave Labs now!

Shunjie Ding

RisingWave is a cloud-native streaming database. It is designed to fully leverage the elasticity provided by the cloud. However, deploying and maintaining RisingWave on the cloud is still a challenging technical problem. To tackle the problem, we developed the RisingWave operator, an extension that features the operations of managing RisingWave on Kubernetes. It supports out-of-the-box restarting, scaling, and upgrading operations on a RisingWave cluster. The RisingWave operator is now open-sourced and it is one step further toward the cloud era for RisingWave.

One Step Towards Cloud: The RisingWave Operator

Everyone is talking about the modern data stack (MDS) nowadays. I am a data system person. I started building core database systems in the big data era, and have witnessed the birth and prosperity of cloud computing over the last decade. But the first time I came across the term “modern data stack,” I felt confused - is it just yet another buzzword that the cloud service vendors created to attract people’s eyeballs? There are so many articles online, but most of them are quite markety and salesy. After running a startup building core systems in the modern data stack domain for a while, I would like to share my thoughts. In this article, I will explain “modern data stack” to you in simple terms, and discuss why modern data stack can really matter in companies.

Everything You Wanted to Know but were Afraid to Ask about Modern Data Stack (part I)

Emily Le

Technical Writer @ RisingWave Labs

As companies collect increasing amounts of data daily, it is crucial to query and analyze the data to make timely decisions quickly. To support such business scenarios, OLAP and stream processing are two popular techniques that promise to deliver analytical results with low latency. However, since both techniques claim to target real-time analysis, it can be quite confusing when trying to understand the differences between them and when to use them. What is OLAP? What is stream processing? How are they different from each other? This blog answers these questions by comprehensively comparing OLAP and stream processing.

Real-time analytics: how is OLAP different from stream processing?

Heng Ma

Tao Wu

Social media platforms like Twitter process a high volume of messages every second. To gain insights from social media platform data in real-time, companies need to adopt stream processing in their tech stack. However, this can be a struggle as many stream processing frameworks are complicated to design and implement. This blog presents how RisingWave can seamlessly work with Apache Pulsar to provide a real-time data streaming solution, making real-time applications much easier to build and maintain.

Tutorial: Pulsar + RisingWave for Fast Twitter Event Processing

Open-source mascots have been widely considered synonyms for their projects/products. So, it should come as no surprise that we thought RisingWave too, deserves a mascot that will add an element of fun to the often-intimidating world of stream processing. Meet Wavy, a squid that won our hearts and will help us keep RisingWave a welcoming and friendly community.

Hello World, Meet Wavy

Building the development environment manually for RisingWave is painful. We have developed RiseDev, the developer tool, to drastically simplify the development process. It automates the process of starting and deploying a full RisingWave cluster and makes the development process much smoother and more efficient.

Developer Experience Counts: Behind the Scenes of RiseDev

The amount of streaming data has grown explosively over the past few years. A lot of businesses realize that they need to move to stream processing, but they have a hard time figuring out the route to take. 

Build and Maintain Real-time Applications Faster and Easier with Redpanda and RisingWave

Two months ago, we open-sourced RisingWave, a cloud-native streaming database. RisingWave is developed on the mission to democratize stream processing — to make stream processing simple, affordable, and accessible.

On the Way to Democratized Stream Processing: RisingWave’s Roadmap

As an early-stage database startup, we entirely deleted our C++ codebase after 7 months of development and rewrote everything from scratch in Rust programming language. Here's how we arrived at a decision and why we think this was one of the best decisions ever.

Building a Cloud Database from Scratch: Why We Moved from C++ to Rust

Two weeks ago, we open-sourced RisingWave, a cloud-native streaming database. And in no time, the project gained lots of attention from the open-source community and became the #1 trending project written in Rust!

Is RisingWave the Next Apache Flink?

Stream processing is a well-studied topic. Over the last few decades, researchers and practitioners have devoted substantial efforts to developing fast, scalable, and reliable streaming systems tailored for real-time analytics over streaming data.