Azure Cognitive Search VS Apache Flink

Azure Cognitive Search Reviews

Apache Flink Reviews

Azure Cognitive Search mentions (4)

• Make your Azure OpenAI apps compliant with RBAC

  Microsoft offers an array of different AI-powered products, including Azure OpenAI Service, Azure AI Search, Azure AI Speech, and their most recent Microsoft Copilot for Office 365. - Source: dev.to / about 1 month ago

• Show HN: Dera – A platform to manage chunks and embeddings for building RAG apps

  Very cool. I wonder when it makes sense to engineer things at this level vs using something like Azure AI search. [0] Love to see version control on all the things! Wonder if the version control features would be more robust if implemented in Doltgres. [0] https://azure.microsoft.com/en-us/products/ai-services/ai-search/ [1] https://github.com/dolthub/doltgresql. - Source: Hacker News / 3 months ago

• 🎵 Do you want to build a Chatbot? 🎵

  Azure Cognitive Search may seem out of place in an article on conversational AI, but I do believe that chatbots are really often a form of conversational search. You're interacting with a virtual agent looking for some piece of information or looking to accomplish some task. - Source: dev.to / over 1 year ago

• Managing the infrastructure of a reusable ecommerce platform with Terraform

  In the ones where we need a persistence layer, we rely on the resources Azure Cosmos DB or Azure Database for PostgreSQL. Other services provide an API to search among a catalog of products with Azure Cognitive Search. As I will explain later, we work with different environments, therefore, creating and updating the resources across them becomes a harder task. - Source: dev.to / almost 3 years ago

Apache Flink mentions (27)

• Top 10 Common Data Engineers and Scientists Pain Points in 2024

  Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 26 days ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago

• Go concurrency simplified. Part 4: Post office as a data pipeline

  Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago

• Five Apache projects you probably didn't know about

  Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago

• Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog

  Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago