Your contribution for today 🍺: Dozer 👇 Co-founded by Matteo Pelati and Vivek G., Dozer is an open source, real-time data integration platform. With Dozer, you can unify data across silos and serve real-time data to data products, dashboards and GPTs. Here’s why you should contribute to this repo: 1️⃣ With a growing repo of ~1,500 stars, Dozer's focus on real-time data movement and leveraging Change Data Capture (from various sources into multiple sinks) presents an opportunity to deepen your data engineering skills. This includes working with databases like PostgreSQL, MySQL, and Clickhouse, as well as understanding data flows and transformations. 🛠️ 2️⃣ If you love Rust, you are in luck! 99% of the repo is built with Rust. With over 130+ issues, you have a lot to play with! You can find various labels such as "enhancement", "sql", "good first issue", "connector", etc. If you start working on this repo, you will unlock the possibility to work on more problems and have a real impact!🏆 3️⃣ Nearly 65% of contributors submit a second PR. This signals that the repo may have a supportive and friendly environment. It also means that if you enjoy working on this repo, chances are you can continue to work on it! 🫰 You can find their GitHub page here: https://lnkd.in/dCfY635f 📖
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I'm excited to share our latest blog post: "How to Create (Lots of) Sample Time-series Data with PostgreSQL." In this article, we delve into the powerful capabilities of PostgreSQL for generating extensive time-series data using the generate_series function. This resource is invaluable for developers and data analysts looking to simulate time-based datasets for testing, analysis, or development purposes. Explore the techniques and best practices we recommend to efficiently create sample time-series data, enhancing your project workflows and analytical capabilities. Read the full post here: [How to Create (Lots of) Sample Time-series Data with PostgreSQL](https://ift.tt/Xxsnfo5)
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