> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Integrated Solutions > This article covers integrated solutions for ETL/ELT, data warehousing, and analytics using Azure Synapse Analytics and related technologies. Welcome to the **Azure Data Fundamentals (DP-900)** course. In this lesson, we’ll cover end-to-end integrated solutions for ETL/ELT, data warehousing, and analytics. Instead of piecing together best-of-breed components, you can leverage platforms that manage every step—from ingestion to real-time insights—within a unified service. ## Azure Synapse Analytics Azure Synapse Analytics is Microsoft’s flagship integrated analytics platform. It combines the power of Azure Data Factory, a data warehouse, Apache Spark, and Azure Data Explorer into a seamless experience. By default, Synapse runs continuously, enabling real-time data processing and analytics. ![The image is a flowchart illustrating the integration process of Azure Synapse Analytics, showing data flow from various DBMS and files through a data factory to Azure Synapse, and then to Apache Spark and Azure Data Explorer. It includes a tip about reducing costs by pausing the service.](https://kodekloud.com/kk-media/image/upload/v1752872882/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Integrated-Solutions/azure-synapse-integration-flowchart.jpg) Continuous mode in Synapse ensures up-to-the-minute insights but may lead to high compute costs. Schedule or pause pipelines to run only when you need batch reports (daily, weekly, or monthly). ### Key Components * **Azure Data Factory**: Orchestrates ETL/ELT workflows * **Synapse SQL Pool**: Dedicated or serverless warehousing * **Apache Spark**: In-memory big data processing * **Azure Data Explorer**: Interactive data exploration Learn more: [Azure Synapse Analytics Documentation](https://docs.microsoft.com/azure/synapse-analytics/) *** ## Data Lake Architecture and PolyBase Under the hood, Synapse’s storage relies on a **Data Lake** built on Azure Storage. Unlike traditional data warehouses, you can ingest raw files—CSV, JSON, XML, Parquet—without upfront transformation. ![The image illustrates a data lake architecture using PolyBase to integrate various file formats like CSV, JSON, XML, and Parquet, with a focus on extract, load, and transform processes.](https://kodekloud.com/kk-media/image/upload/v1752872883/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Integrated-Solutions/data-lake-architecture-polybase-integration.jpg) When you run a SQL query against your lake, Synapse uses **PolyBase** to: 1. Extract raw data into the lake 2. Load it dynamically during query execution 3. Transform it on-the-fly Parquet is a column-oriented storage format optimized for analytics. It delivers high compression and performance, similar to columnar databases. *** ## Azure Storage and Hierarchical Namespace Your data lake files are stored as blobs in an Azure Storage account. To enable folder-like organization, activate **hierarchical namespace** (Data Lake Storage Gen2). This provides: * Filesystem semantics (folders/subfolders) * Fine-grained ACLs on directories and files * Improved performance for large-scale analytics ![The image illustrates the relationship between a Data Lake and Azure Storage blobs, highlighting that it is built on top of Azure Storage with features like hierarchical storage and access control.](https://kodekloud.com/kk-media/image/upload/v1752872884/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Integrated-Solutions/data-lake-azure-storage-relationship.jpg) For more information, see [Azure Data Lake Storage Gen2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-introduction). *** ## Delta Lake: ACID and Unified Workloads A raw data lake is flexible but lacks transactions, indexing, and ACID guarantees. **Delta Lake** extends cloud object storage with a transactional layer, bringing warehouse capabilities—schema enforcement, time travel, and unified batch/streaming workloads. ![The image illustrates the concept of Delta Lake, showing it as a structure that adds warehousing functionality to a data lake.](https://kodekloud.com/kk-media/image/upload/v1752872885/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Integrated-Solutions/delta-lake-warehousing-functionality-structure.jpg) **Delta Lake Benefits:** * ACID transactions on Parquet data * Schema evolution and enforcement * Switch from batch to streaming without code changes Read the open-source project: [Delta Lake](https://delta.io/) *** ## Apache Databricks Beyond Microsoft’s stack, **Apache Databricks** is the most popular managed platform for Spark and Delta Lake. It offers a unified analytics environment for ETL, data warehousing, machine learning, and BI. ![The image describes Apache Databricks as a unified toolset built on Delta Lake and Apache Spark, supporting tasks like extracting, transforming, loading, warehousing, and analyzing data. It facilitates deploying and sharing analytics solutions.](https://kodekloud.com/kk-media/image/upload/v1752872887/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Integrated-Solutions/apache-databricks-delta-lake-spark.jpg) **Why Choose Databricks?** * Fully managed Spark clusters * Built-in support for Delta Lake transactions * Collaborative notebooks and job scheduling * Scales on demand, with pay-as-you-go pricing Explore: [Databricks Documentation](https://docs.databricks.com/) *** ## Integrated Platforms Comparison | Platform | Core Components | Ideal Use Case | Documentation | | ----------------------- | --------------------------------------------- | -------------------------------------------------- | ------------------------------------------------------------------- | | Azure Synapse Analytics | Data Factory, SQL Pools, Spark, Data Explorer | End-to-end analytics with flexible compute options | [Synapse Docs](https://docs.microsoft.com/azure/synapse-analytics/) | | Apache Databricks | Apache Spark, Delta Lake, MLflow | Unified analytics & AI workspaces on managed Spark | [Databricks Docs](https://docs.databricks.com/) | *** ### When to Use Each Platform * **Azure Synapse Analytics**: Best for organizations needing integrated SQL and Spark with hybrid provisioning (serverless + dedicated). * **Apache Databricks**: Ideal for data science, machine learning, and collaborative analytics teams using Spark and Delta Lake.