> ## 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. # Storing Data > This lesson explores the storing phase of the data pipeline in Azure, focusing on optimal storage solutions for analytics and batch processing. In this lesson from the **Azure Data Fundamentals (DP-900)** course, we explore the **Storing** phase of the data pipeline. After extracting and transforming data with **Azure Data Factory**, the next step is to load your cleansed data into the optimal storage solution for **batch processing**, reporting, and analytical workloads. ## Data Stores for Batch Processing Whether you’re aggregating sales metrics or preparing large datasets for machine learning, choosing the right batch storage is crucial. ![The image is a flowchart illustrating the process of storing data, starting from various data sources, transforming the data, and then storing it, before analyzing and reporting on it.](https://kodekloud.com/kk-media/image/upload/v1752872911/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Storing-Data/data-storage-flowchart-process-analysis.jpg) ### Key Batch Storage Options | Data Store | Type | Key Features | Common Use Case | | -------------------- | --------------------- | -------------------------------------------------------------- | --------------------------------------- | | Azure Synapse & ADLS | Cloud analytics lake | PolyBase for direct querying, supports Parquet/CSV/JSON | Large-scale data lake for SQL analytics | | Apache HBase | NoSQL wide-column | Horizontally scalable, HDFS-native, low I/O bottlenecks | Real-time reads/writes on big tables | | Apache Hive | Hadoop data warehouse | ORC columnar storage, fast `SUM`/`AVG`/`COUNT`, fault-tolerant | Ad-hoc aggregations on Hadoop clusters | ### Azure Synapse Analytics and Azure Data Lake Storage Azure Synapse Analytics integrates seamlessly with Azure Data Lake Storage (ADLS). You can store data in multiple formats—**CSV, Parquet, JSON**, and more—and use **PolyBase** to run T-SQL queries directly against your files. No ETL is required before querying. PolyBase pushes computation down to ADLS, boosting performance and reducing costs. ### Apache HBase Apache HBase is a Java-based, NoSQL wide-column store built on Hadoop Distributed File System (HDFS). It offers: * **Horizontal scalability**: Distributes data across many nodes for parallel reads and writes. * **HDFS ecosystem**: Leverages existing Hadoop clusters, minimizing I/O bottlenecks. * **Easy migration**: On-premises HBase schemas lift-and-shift to Azure effortlessly. ### Apache Hive Apache Hive transforms Hadoop into a fault-tolerant data warehouse. Hive uses the **Optimized Row Columnar (ORC)** format to: * Accelerate aggregations like `SUM`, `AVG`, and `COUNT`. * Read only relevant columns, reducing I/O overhead. * Maintain schema-on-read for flexible data ingestion. ![The image is a comparison of data storage technologies: Azure Synapse and Data Lake, Apache HBase, and Apache Hive, highlighting their features and purposes.](https://kodekloud.com/kk-media/image/upload/v1752872912/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Storing-Data/data-storage-comparison-azure-hbase-hive.jpg) ## Data Warehouse Schemas for Analytics When designing for analytics, your table schemas often deviate from OLTP patterns. Two popular modeling techniques are the **Star Schema** and the **Cube Schema**. | Schema Type | Structure | Best For | | ----------- | ----------------------------------------------- | ----------------------------------------- | | Star Schema | Central fact table + surrounding dimensions | Multi-dimensional filters, ad-hoc queries | | Cube Schema | Multiple “slices” of the same fact by dimension | High-speed aggregations across axes | ### Star Schema A **star schema** centers around a **fact table** containing numeric measures (e.g., sales amounts), linked to multiple **dimension tables** (e.g., Customer, Product, Date). * **Fact table**: Holds transactional metrics over time. * **Dimension tables**: Store descriptive attributes for filtering and grouping. This model simplifies SQL joins and enhances query performance, especially in **Azure Synapse dedicated SQL pools**. ![The image depicts a star schema for data analysis, showing a central fact table "Quantity Sold" connected to four dimension tables: Countries, Customers, Date Sold, and Products. It also explains the difference between fact and dimension tables.](https://kodekloud.com/kk-media/image/upload/v1752872913/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Storing-Data/star-schema-data-analysis-fact-dimension.jpg) ### Cube Schema A **cube schema** extends beyond two dimensions by stacking multiple slices of data—each slice represents the fact table at a specific dimension value (e.g., year). * Fast aggregations along time, geography, and product axes. * Pre-aggregated views reduce query runtime. * Scales to 4+ dimensions in many OLAP systems. Cubes optimize dashboards and BI tools that require instant drill-downs and rollups. ![The image illustrates an analytical schema using a cube to represent multi-dimensional data, with axes labeled for columns (quantity sold), entities (products), and time (2010 to next year). It explains that cubes speed up data aggregation compared to two-dimensional tables.](https://kodekloud.com/kk-media/image/upload/v1752872915/notes-assets/images/DP-900-Microsoft-Azure-Data-Fundamentals-Storing-Data/multi-dimensional-data-cube-schema.jpg) Some OLAP solutions support even more dimensions—each tailored for specialized analytical queries. ## Links and References * [Azure Data Factory Overview](https://docs.microsoft.com/azure/data-factory/overview) * [Azure Synapse Analytics Documentation](https://docs.microsoft.com/azure/synapse-analytics/) * [Apache HBase Reference Guide](https://hbase.apache.org/book.html) * [Apache Hive ORC Format](https://orc.apache.org/) * [DP-900 Microsoft Certified: Azure Data Fundamentals](https://docs.microsoft.com/certifications/exams/dp-900/)