Estuary

If you’ve worked in the data world for even a year, someone has asked you to build a real-time dashboard. I had this happen nearly day one of my first job. And of course I said...no problem! One possible choice is a method called change data capture, also known as CDC. I have seen companies employ multiple ways to use CDC or CDC-like approaches to pull data from databases like Postgres or MongoDB.  This can range from using triggers to reading logs. Of course, this focuses on the analytical component as many companies use CDC to replace or supplement traditional ETL/ELT. But CDC can also be a great way to understand your database and its structure. Databases abstract much of what they do to manage and process large volumes of data quickly. Before going down that rabbit hole, let’s dive into what CDC is and why companies use it. https://lnkd.in/gUYWABxq

Looking to evaluate or replace an ETL vendor? Read through the latest Data Engineer’s Guide to ETL Alternatives. It evaluates Estuary, Informatica, Matillion, Talend, and Rivery based on nearly 40 criteria including the main data integration use cases, key features, performance, scalability, reliability, security, cost, and support. Considering other vendors? Don’t worry. There’s more information about ELT, CDC, and streaming analytics vendors as well. #ETL #dataengineering Estuary Informatica Matillion Talend Rivery

In data engineering, finding ways to manage costs while scaling data operations is crucial. #ApacheIceberg tables offer a powerful solution for this by serving as a flexible data ingestion layer for data warehouses such as Snowflake and Databricks. Here's how it works: Traditional streaming ingestion methods typically require constant compute resources provided by the destination, which can quickly become expensive as data volumes increase. However, Iceberg tables change the game by allowing data to be ingested using compute provided by the ingestion framework, which is almost always more cost-effective. With Iceberg tables, organizations can maintain real-time data processing capabilities without the hefty price tag often associated with streaming. This not only democratizes data ingestion—making advanced data strategies accessible to companies of all sizes—but also optimizes cost efficiency, allowing businesses to allocate resources more effectively. Check out our article for a comparison of every way of getting data into Iceberg: https://lnkd.in/d8KGP5yK ps. we have an amazing Iceberg connector that you can get started with today, for free 😎

💪 No more trying to synchronize two schedules and worrying about new data being missed in your transformation step. With our new dbt Cloud integration, you can trigger dbt Cloud jobs straight from Estuary Flow, right when your ingestion pipeline finishes, making sure all data lands in your data warehouse before your models get to work. Available for: Snowflake Databricks BigQuery Redshift Postgres MySQL SQLServer You can go and configure the trigger in your Materialization configuration! dbt Labs

Super cool new features for our SQL Server Capture connector! 💪 Lower impact on the source DB We’ve restructured the connector to only query tables with new changes which results in fewer overall queries. ⏩ Lower latency Lower latency in extracting data when many tables are being captured 🧹 Automatic CDC table clean-up As soon as we’ve read data, we have the option to delete it from your source, resulting in lower disk usage. 👨💼 Automatic CDC instance management We can automatically create new change tables and remove old ones as your schema changes so you no longer have to do anything in that case. Check it out: https://lnkd.in/dyqzBPia

See how Launchmetrics revolutionized their data pipeline with Estuary Flow: ✅ Implemented within days ✅ Lowered costs, including up to 40% reduction in Databricks expenses ✅ Enhanced data engineering productivity ✅ Eliminated constant monitoring and troubleshooting Launchmetrics, a leader in Fashion, Lifestyle, and Beauty analytics, chose Estuary Flow to stream 1 TB of data monthly from Aurora (MySQL & Postgres) to Databricks. "Estuary stood out for its comprehensive source coverage, reliability and lowest total cost. However, what truly won us over was their flexibility and outstanding support." - Pau Montero Parés, CTO at Launchmetrics Ready to transform your data pipeline? Learn how Estuary Flow can help your business achieve similar results! Check out the full Case Study here: https://lnkd.in/d2BmCWzE

Stale Data Means Hallucinations 👻 With our Pinecone connector, your AI applications can now operate on the freshest data, ensuring enhanced accuracy and relevance. No more stale inputs, no more complex configurations. Just efficient, incremental updates and exactly-once delivery guarantees, all while mapping your data fields directly to Pinecone’s index structures. Why is this so amazing? ✨ Instantaneous Data Ingestion: Stream data into Pinecone in real-time, ensuring your AI models are always operating on the freshest data. ✨ Automatic Vectorization: Effortlessly convert incoming data into vector representations, perfectly fitting Pinecone. ✨ Incremental Updates: Manage partial updates with upserts, keeping your data relevant and accurate. This means enhanced accuracy, reduced data staleness, and exactly-once delivery guarantees for AI applications. Imagine real-time recommendation systems, fraud detection, dynamic pricing, and content moderation—all powered by the latest data, with sub-100ms latency! Try it out for free (no cc required!): https://lnkd.in/dYYWGpe4

Change Data Capture (CDC) isn't just for database replication. Here are 8 powerful use cases: 1. Operational Data Stores (ODS): Create real-time, read-only stores for data sharing and read offloading 2. Historical Analytics: Load data warehouses with minimal latency 3. Operational Data Integration: Synchronize data across applications in real-time 4. Data Migration: Efficiently move data during system upgrades or replacements 5. Stream Processing: Capture and respond to events in real-time 6. Operational Analytics: Enable real-time decision making with up-to-date data 7. Data Science and ML: Feed data lakes with fresh data for analysis and model training 8. AI Pipelines: Support real-time data flows for generative AI and other AI applications Which of these use cases could benefit your organization? #dataengineering #changedatacapture

🌟 Exciting news from Estuary Flow! We've just launched our Google #PubSub source connector, allowing you to stream real-time data directly into Estuary Flow. This integration makes it simpler than ever to capture and process events as they happen, empowering your data-driven applications with the speed and efficiency they need. Get started for free: https://lnkd.in/eegZhCPR

Check out our guide for Change Data Capture (CDC) for DynamoDB: https://lnkd.in/d2bBcF_7