Albert Wong’s Post

Great blog post from our Field CTO Kai Waehner on how modernizing with Confluent can help you save on costs using our fully managed, future-proof data streaming platform, built on Apache Kafka & Apache Flink! Let me know if you're interested to learn more!

The "Shift Left Architecture" for Real-Time Data, Better Quality and Reduced Cost => Technology-independent approach! The below diagram is just an example... Replace the logos with your favorites. You still get the same value out of it! #DataIntegration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a #datawarehouse, #datalake or #lakehouse. Data inconsistency, high compute cost, and stale information are the consequences. The Shift Left Architecture enables a data mesh with real-time data products to unify operational and analytical workloads with #ApacheKafka, #ApacheFlink and #ApacheIceberg. Consistent information is handled with streaming processing or ingested into Snowflake, Databricks, Google BigQuery, or any other analytics / AI platform to increase flexibility, reduce cost and enable a data-driven company culture with faster time-to-market building innovative software applications.

Insightful article from Kai, worth a read.

The "Shift Left Architecture" Is helping our customers improve both their operational and Analytical data needs, read all about it in Kai's blog! #datainmotion #apacheFlink #apacheKafka #shiftleft

Shift left architecture for data and AI platforms refers to the practice of moving tasks and processes earlier in the development lifecycle. In the context of data and AI platforms, this approach emphasizes the integration of data quality, governance, and security measures into the early stages of data processing and model development. Here are some key aspects of shift left architecture: 1. Early Integration of Data Quality Checks - Implementing data validation, cleansing, and transformation processes at the beginning of the data pipeline. - Ensuring that data entering the platform meets quality standards to reduce downstream issues. 2.Proactive Governance - Embedding data governance practices, such as data lineage tracking and metadata management, early in the data lifecycle. - Facilitating compliance with regulatory requirements and ensuring data transparency. 3. Security by Design - Incorporating security measures, such as encryption and access controls, from the outset. - Reducing the risk of data breaches and ensuring data privacy. 4. Early Testing and Validation - Shifting testing and validation of data and AI models to the beginning of the development process. - Using automated testing frameworks to detect issues early, thus reducing the cost and effort of fixing problems later. 5. Collaboration and Continuous Integration - Encouraging collaboration between data engineers, data scientists, and other stakeholders early in the project. - Adopting continuous integration and continuous deployment (CI/CD) practices to streamline development and deployment processes. 6. Monitoring and Feedback Loops - Implementing monitoring and feedback mechanisms early in the lifecycle to continuously assess the performance and quality of data and models. - Using feedback to make iterative improvements. By adopting a shift left approach, organizations can enhance the efficiency, reliability, and security of their data and AI platforms, ultimately leading to better outcomes and faster time-to-market for AI solutions.

Check out this great blog post by our Field CTO, Kai Waehner, on how modernizing with Confluent's cutting-edge, fully managed data streaming platform, grounded in Apache Kafka and enhanced with Apache Flink, can drive significant cost savings. A must-read for those looking to future-proof their data infrastructure with efficiency and scalability. Interested in discovering more? Reach out for further details! 📘✨ #DataStreaming #ApacheKafka #Flink #Confluent #ShiftLeftArchitecture 

Join us for our upcoming hands-on virtual lab session. Learn how to leverage the Azure Databricks Data Intelligence Platform to implement a complete data lifecycle and use Databricks SQL to query & visualize data in your lakehouse architecture. Click below for link to registration page!

🚀 Apache Hudi: The Power of Incremental Data Lakes 🚀 Architecture: Hudi sits on top of a data lake and enables streaming ingestion with support for incremental upserts, deletes, and schema evolution. Its unique Merge-on-Read and Copy-on-Write tables allow users to balance between real-time queries and batch workloads. Real-time Use Case: Hudi is great for log analysis. A streaming pipeline can capture user activities on a website and perform upserts into a Hudi table. Real-time dashboards or queries can then be run on the data with consistent snapshot views, perfect for fraud detection. Example: Imagine an e-commerce company that needs real-time updates for order status changes. Hudi’s real-time incremental data processing ensures fresh data is available for users instantly without reprocessing entire datasets. Future Trends: The future for Hudi includes deeper integration with lakehouse architectures, especially with tools like dbt for better data modeling and multi-cloud compatibility for hybrid deployments. This will drive wider adoption for real-time analytics in enterprises. #ApacheHudi #DataLakehouse #RealTimeData Pavan K.

James Gabriel I agree. And Apache Druid is a powerful choice in a shift-left architecture due to its capabilities that align well with the principles of this approach. Here’s why: 1. Real-Time Data Ingestion and Querying. Shift-left emphasizes early and continuous testing and feedback. Druid supports real-time data ingestion and querying, allowing teams to quickly detect and respond to issues as they arise. This capability ensures that data is available for analysis almost immediately after it is generated, supporting rapid feedback loops. 2. High Performance. Druid is designed for high-performance, low-latency queries on large datasets. This performance is critical in a shift-left architecture where timely insights and decision-making are essential. Developers and testers can run complex queries and get instant results, facilitating faster debugging and optimization. 3. Scalability. As development teams push more tasks to the left, the amount of data generated and analyzed increases. Druid’s architecture supports horizontal scaling, allowing it to handle increasing data volumes efficiently. This scalability ensures that the system remains responsive even as data loads grow. 4. Flexible Data Model. Druid’s flexible data model allows it to handle a wide variety of data types and sources, making it adaptable to the diverse needs of a development environment

Lakehouse formats, how to choose: Iceberg vs. DeltaLake vs. Hudi vs. Paimon All of these table formats have one objective in common: Make object stores look and feel like a database. This translates into 5 common capabilities all formats support: 1. Make data on object stores easily mutable (update/delete individual rows) 2. Enable ACID transactions when reading/writing data to object stores 3. Not be tied to the physical layout/grouping of files on object stores 4. Continuously and automatically optimize data files for best performance 5. Multi reader/writer concurrency controls and conflict resolution So what differentiates them? #ApacheIceberg - Large ecosystem, OSS & commercial, adoption with full read/write support - Numerous large scale deployments - Great for batch use cases - Quickly improving for streaming use cases - Simple to use and get started - Includes a new REST catalog to simplify integration #ApacheHudi - Nominal ecosystem adoption - Few large scale deployments - Performance oriented with indexes and lots of levers to tune - Slightly more complex to use with the many config options - Great for streaming and batch use cases #DeltaLake - Small'ish ecosystem mostly developed by Databricks; commercial tools have only partial support - Numerous large scale deployments mostly by Databricks customers - Performance oriented with open and proprietary optimizations - Simple to use and well integrated into Spark - Great for batch use cases - Improving for streaming use cases #ApachePaimon - Newest format on the block with minimal ecosystem adoption - Few large scale deployments - Performance oriented tuned for streaming data - Still very new but quickly maturing - Built-in CDC with Flink - Great for streaming use cases - Improving for batch use cases Overall, these formats are very similar and at different stages of maturity. For me when choosing a format that will lock-in my precious data, it's important to consider (in this order): 1. Ecosystem adoption 2. Maturity 3. Pace of innovation through community effort 4. Performance This is my opinion and it's based on personal experience with each of these formats and working with my customers that implement these in production. Always do your own testing and come to your own conclusions. #lakehouse #datalake #tableformat #dataengineering