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Databricks

11 posts
Enterprise Application Integration with Confliuent and Databricks for Oracle SAP Salesforce Servicenow et al
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  • 285 views
  • 12 minute read

Databricks and Confluent in the World of Enterprise Software (with SAP as Example)

Enterprise data lives in complex ecosystems—SAP, Oracle, Salesforce, ServiceNow, IBM Mainframes, and more. This article explores how Confluent and Databricks integrate with SAP to bridge operational and analytical workloads in real time. It outlines architectural patterns, trade-offs, and use cases like supply chain optimization, predictive maintenance, and financial reporting, showing how modern data streaming unlocks agility, reuse, and AI-readiness across even the most SAP-centric environments.
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Shift Left Architecture with Confluent Data Streaming and Databricks Lakehouse Medallion
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  • 9 minute read

Shift Left Architecture for AI and Analytics with Confluent and Databricks

Confluent and Databricks enable a modern data architecture that unifies real-time streaming and lakehouse analytics. By combining shift-left principles with the structured layers of the Medallion Architecture, teams can improve data quality, reduce pipeline complexity, and accelerate insights for both operational and analytical workloads. Technologies like Apache Kafka, Flink, and Delta Lake form the backbone of scalable, AI-ready pipelines across cloud and hybrid environments.
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Confluent and Databricks for Data Integration and Stream Processing
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  • 10 minute read

Confluent Data Streaming Platform vs. Databricks Data Intelligence Platform for Data Integration and Processing

This blog explores how Confluent and Databricks address data integration and processing in modern architectures. Confluent provides real-time, event-driven pipelines connecting operational systems, APIs, and batch sources with consistent, governed data flows. Databricks specializes in large-scale batch processing, data enrichment, and AI model development. Together, they offer a unified approach that bridges operational and analytical workloads. Key topics include ingestion patterns, the role of Tableflow, the shift-left architecture for earlier data validation, and real-world examples like Uniper’s energy trading platform powered by Confluent and Databricks.
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Data Streaming and Lakehouse - Comparison of Confluent with Apache Kafka and Flink and Databricks with Spark
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  • 9 minute read

The Past, Present, and Future of Confluent (The Kafka Company) and Databricks (The Spark Company)

Confluent and Databricks have redefined modern data architectures, growing beyond their Kafka and Spark roots. Confluent drives real-time operational workloads; Databricks powers analytical and AI-driven applications. As operational and analytical boundaries blur, native integrations like Tableflow and Delta Lake unify streaming and batch processing across hybrid and multi-cloud environments. This blog explores the platforms’ evolution and how, together, they enable enterprises to build scalable, data-driven architectures. The Michelin success story shows how combining real-time data and AI unlocks innovation and resilience.
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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 explores 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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The Shift Left Architecture
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  • 8 minute read

The Shift Left Architecture – From Batch and Lakehouse to Real-Time Data Products with Data Streaming

Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake or lakehouse. Data inconsistency, high compute cost, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: The Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink and Iceberg. 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.
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Data Streaming Landscape 2023 with Apache Kafka Flink and much more
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  • 13 minute read

The Data Streaming Landscape 2023

Data streaming is a new software category to process data in motion. Apache Kafka is the de facto standard used by over 100,000 organizations. Plenty of vendors offer Kafka platforms and cloud services. Many complementary stream processing engines like Apache Flink and SaaS offerings have emerged. And competitive technologies like Pulsar and Redpanda try to get market share. This blog post explores the data streaming landscape of 2023 to summarize existing solutions and market trends.
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Case Studies for Cloud Native Analytics with Data Warehouse Data Lake Data Streaming Lakehouse
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  • 7 minute read

Case Studies: Cloud-native Data Streaming for Data Warehouse Modernization

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 4: Case Studies for cloud-native data streaming and data warehouses.
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Data Warehouse and Data Lake Modernization with Data Streaming
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  • 9 minute read

Data Warehouse and Data Lake Modernization: From Legacy On-Premise to Cloud-Native Infrastructure

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 3: Data Warehouse Modernization: From Legacy On-Premise to Cloud-Native Infrastructure.
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