Solid information architecture is critical to enable organizations to meet data requirements including governance, storage, protection, retention, and more. This course explores identifying and satisfying information architecture requirements.

The Audience should have familiarity with notebooks that use different languages and a Spark engine, such as Databricks, Jupyter Notebooks, Zeppelin notebooks and more. They should also have some experience with SQL, Python, and Azure tools, such as Data Factory.

Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

In this learning path, you'll learn how Azure Cosmos DB uses partitioning to scale containers and how spending some time thinking about your data model helps to meet the performance needs of your applications.

Azure Synapse Link for Azure Cosmos DB enables HTAP integration between operational data in Azure Cosmos DB and Azure Synapse Analytics runtimes for Spark and SQL.

Learn how to implement CI/CD workflows in Azure Databricks to automate the integration and delivery of code changes.

Beyond indexes for keyword searches, Couchbase also offers full-text indexes to search within document text using natural language capabilities. This course gives you a conceptual and hands-on understanding of full-text searches in Couchbase.

Streamline the implementation of Large Language Models (LLMs) with LLMOps (LLM Operations) in Azure Databricks. Learn how to deploy and manage LLMs throughout their lifecycle using Azure Databricks.

Multi-stage reasoning systems break down complex problems into multiple stages or steps, with each stage focusing on a specific reasoning task. The output of one stage serves as the input for the next, allowing for a more structured and systematic approach to problem-solving.

Retrieval Augmented Generation (RAG) is an advanced technique in natural language processing that enhances the capabilities of generative models by integrating external information retrieval mechanisms. When you use both generative models and retrieval systems, RAG dynamically fetches relevant information from external data sources to augment the generation process, leading to more accurate and contextually relevant outputs.

You explore different features and tools to help you develop architecture patterns with Azure Databricks Delta Live Tables.