Browsing Category

Apache Flink

25 posts
Read More
  • 5.4K views
  • 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.
Read More
0
0
0
Airport and Airlines Digitalization with Data Streaming using Apache Kafka and Flink
Read More
  • 1.3K views
  • 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.
Read More
0
0
0
Energy Trading with Apache Kafka and Flink at Uniper ReAlto Powerledger
Read More
  • 894 views
  • 9 minute read

Energy Trading with Apache Kafka and Flink

Energy trading and data streaming are connected because real-time data helps traders make better decisions in the fast-moving energy markets. This data includes things like price changes, supply and demand, smart IoT meters and sensors, and weather, which help traders react quickly and plan effectively. As a result, data streaming with Apache Kafka and Apache Flink makes the market clearer, speeds up information sharing, and improves forecasting and risk management. This blog post explores the use cases and architectures for scalable and reliable real-time energy trading, including real-world deployments from Uniper, re.alto and Powerledger.
Read More
0
0
0
The Shift Left Architecture
Read More
  • 7.0K views
  • 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.
Read More
0
0
0
RAG and Kafka Flink to Prevent Hallucinations in GenAI
Read More
  • 1.8K views
  • 5 minute read

Real-Time GenAI with RAG using Apache Kafka and Flink to Prevent Hallucinations

How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. This blog post explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. A lightboard video shows how to build a context-specific real-time RAG architecture. Also, learn how the travel agency Expedia leverages data streaming with Generative AI using conversational chatbots to improve the customer experience and reduce the cost of service agents.
Read More
0
0
0
Data Lineage for Data Streaming with OpenLineage Apache Kafka and Flink
Read More
  • 2.9K views
  • 11 minute read

Open Standards for Data Lineage: OpenLineage for Batch AND Streaming

One of the greatest wishes of companies is end-to-end visibility in their operational and analytical workflows. Where does data come from? Where does it go? To whom am I giving access to? How can I track data quality issues? The capability to follow the data flow to answer these questions is called data lineage. This blog post explores market trends, efforts to provide an open standard with OpenLineage, and how data governance solutions from vendors such as IBM, Google, Confluent and Collibra help fulfil the enterprise-wide data governance needs of most companies, including data streaming technologies such as Apache Kafka and Flink.
Read More
0
0
0
My Data Streaming Journey with Kafka and Flink - 7 Years at Confluent
Read More
  • 2.0K views
  • 11 minute read

My Data Streaming Journey with Kafka & Flink: 7 Years at Confluent

Time flies… I joined Confluent seven years ago when Apache Kafka was mainly used by a few tech giants and the company had ~100 employees. This blog post explores my data streaming journey, including Kafka becoming a de facto standard for over 100,000 organizations, Confluent doing an IPO on the NASDAQ stock exchange, 5000+ customers adopting a data streaming platform, and emerging new design approaches and technologies like data mesh, GenAI, and Apache Flink. I look at the past, present and future of my personal data streaming journey. Both, from the evolution of technology trends and the journey as a Confluent employee that started in a Silicon Valley startup and is now part of a global software and cloud company.
Read More
0
0
0
Apache Kafka and Snowflake Cost Efficiency and Data Governance
Read More
  • 2.2K views
  • 10 minute read

Apache Kafka + Flink + Snowflake: Cost Efficient Analytics and Data Governance

Snowflake is a leading cloud data warehouse and transitions into a data cloud that enables various use cases. The major drawback of this evolution is the significantly growing cost of the data processing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables a “shift left architecture” where business teams can reduce cost, provide better data quality, and process data more efficiently. The real-time capabilities and unification of transactional and analytical workloads using Apache Iceberg’s open table format enable new use cases and a best of breed approach without a vendor lock-in and the choice of various analytical query engines like Dremio, Starburst, Databricks, Amazon Athena, Google BigQuery, or Apache Flink.
Read More
0
0
0
When NOT to use Apache Kafka
Read More
  • 1.5K views
  • 3 minute read

When NOT to Use Apache Kafka? (Lightboard Video)

Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This blog post contains a lightboard video that gives you a twenty-minute explanation of the DOs and DONTs.
Read More
0
0
1
The Past Present and Future of Stream Processing
Read More
  • 17.8K views
  • 12 minute read

The Past, Present and Future of Stream Processing

Stream processing has existed for decades. The adoption grows with open source frameworks like Apache Kafka and Flink in combination with fully managed cloud services. This blog post explores the past, present and future of stream processing, including the relation of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg.
Read More
0
0
0