How to Optimize Data Streaming Performance

Snowflake Data Loading: Part 3 - Streaming Data 🌊 After batch fundamentals (Part 1) and advanced techniques (Part 2), we now focus on Streaming Data Loading 🌊 for real-time analytics. Streaming Data Loading Patterns (Do's ✅): ✅ Snowpipe Streaming: Real-Time Ingestion (⚡🚀): Lowest latency, highest efficiency. Direct row-by-row insertion from clients/platforms, bypassing intermediate files. ✅ Snowflake Kafka Connector (Streaming Mode) (📬➡️❄️): Robust for Kafka users. Pushes data reliably from Kafka topics with auto schema detection, evolution, high throughput, and data integrity. ✅ Streams & Tasks for Change Data Capture (CDC) (🔄👁️🗨️): For propagating DML changes (inserts, updates, deletes) from internal/external sources. Streams record changes, Tasks execute scheduled logic. ✅ Robust Error Handling/Dead-Letter Queues (🚨📦): Crucial for continuous streams. Implement queues for failed records, allowing analysis and reprocessing. ✅ Monitor/Alert on Latency & Throughput (📊🔔): Track end-to-end latency, throughput, error rates. Set alerts for deviations to ensure data freshness and reliability. Streaming Data Loading Anti-Patterns (Don'ts 🚫): 🚫 Ignoring Latency Requirements (⏰): Don't use batch solutions for true real-time needs. Misalignment leads to stale data and dissatisfied customers. 🚫 Over-Reliance on Complex UDFs during Ingestion (🧩): Avoid resource-intensive transformations with UDFs during direct ingestion. Better done in a subsequent Snowflake transformation layer. 🚫 Failing to Manage Schema Evolution (💥): Streaming sources can have unexpected schema changes. Without a strategy (e.g., VARIANT type, schema registry with Kafka Connector), pipelines break, causing data loss. 🚫 Lack of Proper Resource Management (💸): Snowpipe/Snowpipe Streaming consume credits. Failing to monitor high-volume streams leads to unexpected cost. Regularly review consumption. Stay tuned for Part 4: Hybrid Approaches & Common Architectures! #Snowflake #StreamingData #SnowpipeStreaming #Kafka #DataStreams #CDC #DataEngineering

Got too many data streams and processing tasks? 🤹♂️ I've got a solution to share with you - Elastic's integration filter plugin for Logstash. 1. Offload Processing: Move data processing operations from your Elastic deployment to Logstash. This flexibility is crucial for optimizing performance and resource allocation. 2. Simplify Network Configuration: By using Logstash as the final route for your data, you can potentially reduce the number of open ports and firewall rules. This is a win for both security and simplicity. 3. Leverage Elastic Integrations: Process data from Elastic integrations by executing ingest pipelines within Logstash before forwarding to Elastic. It's like having the best of both worlds! I recently set this up in our lab, and the process was straightforward: 1. Install Logstash 2. Generate custom certificates for secure communication 3. Configure Fleet to add a Logstash output 4. Set up a custom Logstash pipeline 5. Update the agent policy to use the new Logstash output The beauty of this setup is its versatility. Whether you're using a hosted cloud deployment or a serverless project, you can tailor the configuration to fit your needs. For those of you managing large-scale observability pipelines, it allows for more efficient data processing and gives you greater control over where and how your data is handled. Have you tried using the integration filter plugin yet? I'd love to hear about your experiences! #Observability #SiteReliabilityEngineering #DataProcessing https://lnkd.in/gfNDa-Ac

Software Engineering and Data Loading Optimization from Cloud Object Storage: Cloud-based object storage is often considered as a high-latency alternative for data stores as opposed to something like NVMe. This is primarily for reasons like data backup and redundancy, scalability, distribution, flexibility, and cost. In many scenarios, this is a cost effective trade-off. For example, for non-critical batch-processing workloads or cloud native applications, pulling data from a bucket will definitely be more flexible than deploying a parallel filesystem, and the latency may be acceptable as performance continues to improve. When working with cloud-based object storage, or any network attached storage really, we're always trying to strike the perfect balance between the network packet size, and the number of I/O requests. In general, object storage systems are designed to handle larger, self-contained data units efficiently: - Many object storage systems use block sizes in range of 64KB to 100MB. - Object storage provides rich metadata, but rich metadata comes at a cost. Using smaller files incurs a higher metadata cost than larger files because metadata is stored for each file. Since we know that cloud-based object storage is going to be high latency (because it travels over a far-away network), we optimize for throughput. Since I/O requests come at a cost, we target fewer requests by asking for larger packets of data. Each single request has a higher latency than a smaller packet size, but the reduction in number of requests increases throughput significantly. This also aligns very well with GPU workflows such as AI and ML, as GPUs are throughput monsters, but are also high latency. So how do we optimize for throughput in these environments? Recently, I've been studying several PyTorch data loaders designed for streaming data from object storage and they take an interesting approach. First, they all provide an "optimizer". This optimizer ingests data, and shards it back out in a manner that is optimized for reading from cloud-based object stores. Second, they provide mechanisms to stream data efficiently, using "load-ahead" and caching techniques to process data more efficiently. They can utilize these techniques to perform an efficient approach to load data directly as a PyTorch IterableDataset, which bypass any unnecessary data transformation on host to do a "some temporary data structure into PyTorch Dataset" conversion. Additionally, they have all aligned on 64MB as the optimal "shard-size". 64MB strikes the perfect balance between optimized object storage, I/O requests, throughput, and cache efficiency. Taking a deep dive into these technologies and their approaches to dealing with high-latency storage has been fascinating. If you like my content, feel free to follow or connect! #softwareengineering #objectstorage

AWS has just released Kinesis Client Library (KCL) 3.0, a game-changer for those working with Amazon Kinesis Data Streams. This update can slash your stream processing costs by up to 33%! Key highlights:
 • New load balancing algorithm for even resource utilization
 • Reduced compute costs through optimized worker distribution
 • Minimized data reprocessing with graceful shard handoffs
 • Removal of AWS SDK for Java 1.x dependency KCL 3.0 introduces a smart load balancing system that monitors CPU utilization across workers and redistributes the workload dynamically. This means you can process the same amount of data with fewer compute resources, leading to significant cost savings. But that’s not all! The update also brings:
 • Reduced Amazon DynamoDB read capacity unit (RCU) usage
 • Improved performance and security with AWS SDK for Java 2.x
 • Easy migration path from KCL 2.x versions Whether you’re processing real-time data for IoT, analytics, or any high-throughput scenario, KCL 3.0 offers a more efficient and cost-effective solution. Ready to optimize your stream processing? Check out our blog post for a detailed walkthrough and migration guide. It’s time to supercharge your Kinesis applications while keeping costs in check! #AWS #KinesisDataStreams #StreamProcessing #CloudComputing #CostOptimization https://lnkd.in/gWcyRd8z