Lakehouse and Data Streaming - Competitor or Complementary
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  • 12 minute read

How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)

In today’s data-driven world, understanding data at rest versus data in motion is crucial for businesses. Data streaming frameworks like Apache Kafka and Apache Flink enable real-time data processing. Meanwhile, lakehouses like Snowflake, Databricks, and Microsoft Fabric excel in long-term data storage and detailed analysis, perfect for reports and AI training. This blog post delves into how these technologies complement each other in enterprise architecture.
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Microsoft Fabric and OneLake Azure Lakehouse vs Databricks and Snowflake Cloud
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  • 8 minute read

What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Competes with Snowflake and Databricks

If you ask your favorite large language model, Microsoft Fabric appears to be the ultimate solution for any data challenge you can imagine. That’s also the impression many people get from Microsoft’s sales teams. But is it really the silver bullet it’s made out to be? This article takes a closer look exploring the glossy marketing and sales definition of the platform and then deconstructing it from a more practical perspective. Learn what Microsoft Fabric is truly built for, and how it fits into the wider data landscape, especially in comparison to other major players in the data analytics market like Databricks and Snowflake.
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Real-Time AI ML Model Inference Predictive AI and Generative AI with Data Streaming using Apache Kafka and Flink
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  • 14 minute read

Real-Time Model Inference with Apache Kafka and Flink for Predictive AI and GenAI

Artificial Intelligence (AI) and Machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. This blog post explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of model predictions. Whether for real-time fraud detection, smart customer service applications or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.
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Industrial IoT Middleware OT IT Bridge between Edge and Cloud with Apache Kafka and Flink
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  • 11 minute read

Industrial IoT Middleware for Edge and Cloud OT/IT Bridge powered by Apache Kafka and Flink

As industries continue to adopt digital transformation, the convergence of Operational Technology (OT) and Information Technology (IT) has become essential. The OT/IT Bridge is a key concept in industrial automation to connect real-time operational processes with business-oriented IT systems ensuring seamless data flow and coordination. By leveraging Industrial IoT middleware and data streaming technologies like Apache Kafka and Flink, businesses can achieve a unified approach to managing both production processes and higher-level business operations to drive greater efficiency, predictive maintenance, and streamlined decision-making.
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Apache Kafka Deployment Options - Serverless vs Self-Managed vs BYOC Bring Your Own Cloud
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  • 9 minute read

Deployment Options for Apache Kafka: Self-Managed, Fully-Managed / Serverless and BYOC (Bring Your Own Cloud)

BYOC (Bring Your Own Cloud) is an emerging deployment model for organizations looking to maintain greater control over their cloud environments. Unlike traditional SaaS models, BYOC allows businesses to host applications within their own VPCs to provide enhanced data privacy, security, and compliance. This approach leverages existing cloud infrastructure. It offers more flexibility for custom configurations, particularly for companies with stringent security needs. In the data streaming sector around Apache Kafka, BYOC is changing how platforms are deployed. Organizations get more control and adaptability for various use cases. But it is clearly NOT the right choice for everyone!
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Unified Commerce with Data Streaming using Apache Kafka and Flink at the Edge and in the Cloud
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  • 8 minute read

Unified Commerce in Retail and eCommerce with Apache Kafka and Flink for Real-Time Customer 360

Delivering a seamless and personalized customer experience across all touchpoints is essential for staying competitive in today’s rapidly evolving retail and eCommerce landscape. Unified commerce integrates all sales channels and backend systems into a single platform to ensure real-time consistency in customer interactions, inventory management, and order fulfillment. This blog post explores how Apache Kafka and Flink can be pivotal in achieving real-time Customer 360 in the unified commerce ecosystem and how it differs from traditional omnichannel approaches.
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Multi-Cloud Replication in Real-Time with Apache Kafka and Cluster Linking
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  • 5 minute read

Multi-Cloud Replication in Real-Time with Apache Kafka and Cluster Linking

Multiple Apache Kafka clusters are the norm; not an exception anymore. Hybrid integration and multi-cloud replication for migration or disaster recovery are common use cases. This blog post explores a real-world success story from financial services around the transition of a large traditional bank from on-premise data centers into the public cloud for multi-cloud data sharing between AWS and Azure.
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One Apache Kafka Cluster Type Does NOT Fit All Use Cases
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  • 13 minute read

Apache Kafka Cluster Type Deployment Strategies

Organizations start their data streaming adoption with a single Apache Kafka cluster to deploy the first use cases. The need for group-wide data governance and security but different SLAs, latency, and infrastructure requirements introduce new Kafka clusters. Multiple Kafka clusters are the norm, not an exception. Use cases include hybrid integration, aggregation, migration, and disaster recovery. This blog post explores real-world success stories and cluster strategies for different Kafka deployments across industries.
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Apache Iceberg Open Table Format for Data Lake Lakehouse Streaming wtih Kafka Flink Databricks Snowflake AWS GCP Azure
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  • 11 minute read

Apache Iceberg – The Open Table Format for Lakehouse AND Data Streaming

An open table format framework like Apache Iceberg is essential in the enterprise architecture to ensure reliable data management and sharing, seamless schema evolution, efficient handling of large-scale datasets and cost-efficient storage. This blog post explores market trends, adoption of table format frameworks like Iceberg, Hudi, Paimon, Delta Lake and XTable, and the product strategy of leading vendors of data platforms such as Snowflake, Databricks (Apache Spark), Confluent (Apache Kafka / Flink), Amazon Athena and Google BigQuery.
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Airport and Airlines Digitalization with Data Streaming using Apache Kafka and Flink
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  • 10 minute read

The Digitalization of Airport and Airlines with IoT and Data Streaming using Kafka and Flink

The vision for a digitalized airport includes seamless passenger experiences, optimized operations, consistent integration with airlines and retail stores, and enhanced security through the use of advanced technologies like IoT, AI, and real-time data analytics. This blog post shows the relevance of data streaming with Apache Kafka and Flink in the aviation industry to enable data-driven business process automation and innovation while modernizing the IT infrastructure with cloud-native hybrid cloud architecture.
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