Snowflake’s Post

🚀 Unlock near real-time IoT data insights with this powerful solution architecture! Learn how to: 1️⃣ Ingest IoT data into Snowflake using IoT Bridge 2️⃣ Transform and dynamically build machine/device views 3️⃣ Detect anomalies using Snowpark ML & LSTM autoencoder! Don’t miss out on this cutting-edge use case 👉 https://okt.to/Ax4T56 #IoT #DataAnalytics #Snowflake #ML

Interesting use of Snowflake for real-time IoT data streaming and machine learning! The combination of Snowpipe for dynamic data views and LSTM for anomaly detection is a powerful approach. Excited to see how this architecture scales as IoT data continues to grow. #snowflake #IoT

💡 Ingest IOT data in near real time into Snowflake for further analysis, machine learning and other manufacturing use cases. This solution architecture will guide you through ingesting data into Snowflake using IOT bridge, transforming the data and dynamically build various machine/device views, and using Snowpark ML to run anomaly detection on the dataset In this use-case 1️⃣ IOT bridge converts the MQTT/SparkplugB messages from protobuf format to JSON format and ingest it into Snowflake 2️⃣ Messages are flattened, rehydrated and stored as device “As Of” Views for Analytics and other downstream use 3️⃣ An LSTM autoencoder for anomaly detection is trained and stored in Snowflake internal stage. It is then inferenced via a UDF Learn more: https://okt.to/gRVFCY

Dealing with Unexpected Data Disruptions in Machine Learning Dashboards? Imagine walking into a data dashboard on Monday morning only to find it completely disrupted—when everything was running smoothly just last Friday. As #dataEngineers and data scientists, we know this nightmare all too well. It often happens when variations in data schema throw off queries, especially when dealing with sensor data or third-party systems. In my latest experience, I tackled this by using #AWSLambda to transform and standardize the incoming data. This ensures that only the relevant fields make it to the final data catalog, helping prevent errors and keep the system running smoothly. In this article, I break down how schema variations affect real-time data processing and share a practical solution to manage them effectively. Stay ahead of data disruptions with this simple yet powerful approach! #DataEngineering #MachineLearning #AWS #IoT #DataTransformation #Lambda #CloudComputing #DataScience #DevOps

#Great Learning #AI and Big Data in IOT

Ever had a massive time series dataset to analyze? #Spark is one of the main languages for big data, and can get gigabytes efficiently imported into a single dataframe using #Databricks. But for machine learning, business analysis etc. you may want to use Pandas, which implies resampling datapoints and reducing the data volume. This is a typical #IoT challenge, and today we have a simple and elegant way to deal with it.

Learn How to Handle Complex MQTT Payload Structures like SparkPlug-B and Nested JSON! At Exalens, we’re leading the way in data management with our advanced MQTT client. Learn how to handle complex payload structures seamlessly. 1️⃣ Simple Topic Format 2️⃣ Array 3️⃣ JSON 4️⃣ Array of JSON 5️⃣ Nested JSON 6️⃣ SparkPlug-B 7️⃣ SparkPlug-B JSON Format Our client is designed to efficiently ingest, parse, and store your data, no matter how complex, empowering your Industry 4.0 initiatives. #Industry40 #MQTT #IoT #DataHandling #Exalens #SparkPlugB #JSON

❓ DYK that a significant portion of engineering expertise resides within product or engineering departments, often encapsulated in simulation models? Ansys and Amazon Web Services (AWS) have joined efforts to help organizations overcome these silos, presenting scalable #digitaltwin solutions through strategic collaboration. Within this context, this application brief will explain how Ansys Twin Builder, AWS IoT TwinMaker, and advanced probabilistic methods can enable industrial autonomy for customers and the different levels of digital twins to consider: https://lnkd.in/eYkiwA3Y You can also join us for a live webinar to learn more about this collaboration. April 2 (both links are in the comment section).

Have you registered for our June 13 webinar around, "Transforming Utility Reliability with AI-Driven Situational Intelligence and IoT"? This joint webinar with SAS and Accenture is available to all members of UAI and we encourage you to learn more and register below! https://bit.ly/3wWR3RE #Utility #Analytics #utilityanalytics #data #bigdata #IoT #SAS #Accenture #datascience #AI #GenAI #GenerativeAI #ML #machinelearning #situationalintelligence