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Business Intelligence

7 posts
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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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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Difference between a Data Warehouse and a Live Datamart?

Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.
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Comparison of Stream Processing and Streaming Analytics Alternatives (Apache Storm, Spark, IBM InfoSphere Streams, TIBCO StreamBase, Software AG Apama)

The article discusses what stream processing is, how it fits into a big data architecture with Hadoop and a data warehouse (DWH), when stream processing makes sense, and what technologies and products you can choose from. Comparison of open source and proprietary stream processing / streaming analytics alternatives: Apache Storm, Spark, IBM InfoSphere Streams, TIBCO StreamBase, Software AG’s Apama, etc.
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