Estuary

Thinking about modernizing your infrastructure and migrating from #Oracle to #MongoDB? Check out our latest guide for three recommended ways: https://hubs.ly/Q02ZHbcR0

When building data pipelines, achieving exactly-once processing is often a holy grail for data engineers. But what does "exactly-once" truly mean, and why is it so challenging in data movement? Let’s break it down. What is Exactly-Once? In simple terms, it ensures that each event in your pipeline is processed one and only one time—no duplicates, no missing data. This precision is critical for downstream systems, especially when handling financial transactions, inventory updates, or analytics dashboards where correctness matters. Why is it Difficult? Data pipelines span multiple systems—message brokers, storage layers, databases—all of which have their own guarantees. For example: Message Brokers like Kafka handle at-least-once or at-most-once delivery, but introducing exactly-once semantics requires additional configurations. Stateful Systems (like your transformations) must handle partial failures gracefully, ensuring a retry doesn’t lead to duplication. Idempotency at the destination is vital. Without it, duplicate events can corrupt your data. How Does Estuary Flow Help? With Estuary Flow’s real-time connectors, we ensure exactly-once semantics from source to destination—whether you’re ingesting events from Kafka or writing to Iceberg tables. This is achieved through: 1. Transactional Guarantees: Flow checkpoints data during movement, ensuring retries are safe. 2. Idempotent Writes: Our platform generates consistent, deduplicated outputs even in complex scenarios like change data capture (CDC). 3. Unified Batch & Streaming Support: Flow allows you to move data without worrying about semantics breaking between real-time and batch processes. For data engineers, this means you can trust your pipelines—reducing the complexity of building error-prone retry mechanisms or cleaning up duplicates. ?? Curious about how exactly-once semantics works in Flow? Check out our page for more information! https://hubs.ly/Q02Z9q5z0

#Airflow is not your only choice! We've curated a list of the top 9 Python ETL tools for Data Engineers. If you're interested in learning more about the likes of #polars, Bytewax and dltHub - read on. Check it out: https://hubs.ly/Q02Z1f1q0

Looking to integrate your #PostgreSQL database with your #ApacheIceberg Data Lakehouse? Look no further! In this guide, we’ll show you how you can connect these two systems, and we’ll go into some detail on how to actually set up the integration! Learn more: https://hubs.ly/Q02YXKDf0

When I first started in the data world, the first tool I used to build a data pipeline was SSIS. Since then it feels like I have come across every possible tool and custom data pipeline set-up possible(of course thats far from the truth). There seem to be hundreds of tools and methods for data teams use to get data from point A to point B. So I wanted to share some of those experiences as well as hear from Daniel Palma's experiences building data pipelines. What has changed? What has stayed the same? What challenges do data engineers still face today? Feel free to share some of your questions below!

For many organizations, Retrieval-Augmented Generation (RAG) have?become the go-to approach for making AI applications work seamlessly with proprietary company data. And let’s face it, no one wants their AI applications to rely on outdated information. Building real-time RAG pipelines just got a bit simpler. With Estuary you can connect to almost any data source and ingest updates in real time, seamlessly streaming them into Bytewax. Bytewax is purpose-built for creating robust, real-time embedding pipelines while harnessing the magic of Python. It integrates seamlessly with leading Python libraries like unstructured.io, Haystack, LangChain, and many others for document cleansing and chunking. For embedding generation, Hugging Face Transformers is a popular choice that offers countless pre-trained models, empowering your AI applications with powerful and flexible embeddings. With real-time RAG applications, you can be confident your AI outputs are not just free of hallucinations, but grounded in the most current, accurate data available. How important is up-to-date data for your AI application?

Struggling to balance data integration with strict security and compliance? Estuary Flow’s private deployments let you process data within your own cloud environment—securely, scalably, and in real-time. ?? Key Benefits: - Data Sovereignty: Keep sensitive data in your VPC. - Compliance Made Easy: Meet GDPR, HIPAA, or SOC 2 standards. - Unified Pipelines: Stream and batch data in one seamless platform. - Blazing Performance: High throughput, low latency, total control. Perfect for industries like #finance, #healthcare, and #supplychain, private deployments ensure you stay compliant while unlocking the power of real-time decision-making. Want to see how it works? Learn more: ?? https://lnkd.in/dYjSYaZx

Check out how quickly you can connect your Amazon #RDS instance to Snowflake using Estuary Flow's fully managed change data capture (CDC) connectors.

Combining real-time data ingestion with the flexibility of a data lakehouse architecture is more important than ever. That’s why we’ve put together a step-by-step guide on how to set up a streaming lakehouse using Estuary Flow, #ApacheIceberg, and #PyIceberg! ???? In this article, you’ll learn how to: 1?? Ingest data in real-time using Estuary Flow’s Change Data Capture (CDC) from your source system. 2?? Store and manage your data in Apache Iceberg, enabling scalable and reliable storage. 3?? Perform powerful queries with PyIceberg & pandas for near-instant insights. Whether you’re building real-time analytics pipelines or looking to leverage the full potential of a streaming lakehouse, this guide will help you get started and scale your architecture. Ready to dive into the world of streaming lakehouses? Check out the full guide and start building your own robust data architecture today! ?? https://lnkd.in/d-yitUv7

?? Ready to elevate your Retrieval-Augmented Generation (RAG) workflows? In our latest blog, Real-Time RAG with Estuary and Pinecone, Shruti Mantri walks you through the essentials of integrating real-time data into your AI applications. From setup to seamless data flow, this guide shows you how to build responsive, up-to-date RAG models with Estuary and Pinecone. Curious about the setup and impact? Dive into Shruti’s full guide here: ?? https://lnkd.in/eFv4fxru