DP-900: Microsoft Azure Data Fundamentals in a Weekend

Explore how data fuels the digital economy, from structured to unstructured formats, and learn Azure storage options across relational, NoSQL, and analytical databases, regions, zones, and IaaS, PaaS, SaaS.

Explore cloud fundamentals, learn why enterprises move from data centers to the cloud, and understand servers, data centers, and the advantages of cloud adoption through real enterprise examples.

Discover why enterprises need thousands of servers by exploring how web, file, and email servers serve services, scale beyond single machines, and rely on data centers to host infrastructure.

Explore what data centers require—uninterrupted power supply, cooling, networking, and security—and examine challenges like high upfront cost, peak load provisioning, over-provisioning, and latency, before introducing cloud as a faster alternative.

Discover how the cloud turns infrastructure into a rentable, on-demand resource with elasticity and pay-as-you-go, using AWS, Azure, and Google Cloud as examples.

Discover why cloud enables pay for use OpEx, economies of scale, elastic capacity, faster innovation, global reach, and avoid undifferentiated heavy lifting for startups and enterprises.

Explore elasticity, agility, geo-distribution, and managed services to deploy globally and respond quickly. Understand capex to opex, pay-as-you-go, economies of scale, and availability and latency.

Explore elasticity and pay-as-you-go OpEx through scalable resources. Learn agility, high availability, geo-distribution, low latency, economies of scale, and avoiding undifferentiated heavy lifting as cloud providers manage infrastructure.

Discover why Azure is Linux friendly and open source oriented, with more than half workloads on Linux, plus Kubernetes, containers, regions, and services like compute, storage, databases, networking, and AI.

Explore a structured, step-by-step path to learning the cloud, using a cloud account gateway to deploy resources globally, with free beginner credits.

Create your Azure account through the free trial and verify email, phone, and card details. Access the Azure portal to explore $200 credit and 12 months of free services.

Explore regions and zones, including availability zones, and learn how cloud providers enable global infrastructure with low latency and high availability by distributing data centers across multiple regions.

Identify factors to consider when choosing a region for deploying your application, including compliance, data locality, latency, service availability, and pricing.

Deploy your applications across multiple regions to reduce latency, expand globally, and meet data residency, availability, and disaster recovery goals.

Explore zones, or availability zones, within a region to achieve high availability by deploying across Zone A, Zone B, and Zone C in London, with independent power, network, and connectivity.

Explore the difference between regions and zones and how multi-region and multi-zone deployments enable disaster recovery, high availability, and data residency.

Explore Azure regions and availability zones across global locations like East US, West Europe, Southeast Asia, Brazil South, and West Central US to understand high availability and low latency.

Explore how to run SQL Server on Azure via IaaS, PaaS, and SaaS, including provisioning an Azure virtual machine, SQL Server installation, and high availability.

Learn to provision an Azure virtual machine with SQL Server 2019 on Red Hat, configure the image and size, enable SSH access, and deploy via the portal.

Connect to an Azure VM via SSH, configure SQL Server password with mssql-conf, start SQL Server, and create a database and users table using sqlcmd.

Using a VM for databases requires you to manage OS and database upgrades, availability, durability, backups, and scaling. Azure offers simpler database solutions, which we explore next to reduce responsibilities.

Explore IaaS alongside PaaS and SaaS, as you run SQL server on a cloud VM, managing OS, database, and scaling while the provider handles virtualization.

Discover platform as a service on Azure, where the provider handles virtualization, OS, database software, and scaling, with Azure SQL Database and Cosmos DB as examples.

Learn to create an Azure SQL Database as a platform as a service, connect with connection strings, and use query editor to build tables while enabling zone redundancy and encryption.

Explore software as a service (SaaS): centrally hosted cloud apps with subscription models, where providers manage OS, updates, runtime, and scaling, and users only configure the software (Gmail, Office 365).

Explore IaaS, PaaS, and SaaS through scenarios like deploying databases on virtual machines and Azure SQL Database, clarifying who manages OS updates and availability.

Stay relevant by maximizing time at work and asking why decisions and process changes occur. Surround yourself with curious colleagues, attend conferences, and follow industry trends such as Thoughtworks Radar.

Explore structured, semi-structured, and unstructured data formats and their storage options, from relational and NoSQL databases to object, block, and file storage.

Explore how structured data resides in relational databases with tables, rows, and columns, featuring predefined schemas, primary keys, constraints, indexes, and OLTP and OLAP use cases in Azure managed services.

Explore OLAP relational databases for analyzing petabytes of data with Azure Synapse Analytics, focusing on columnar storage, compression, and massively parallel processing across nodes.

Explore semi-structured data and document databases like JSON and key-value formats, and learn how Azure Cosmos DB stores document, key-value, graph, and column family data with flexible schemas.

Explore key-value, graph, and column-family semi-structured data and their use cases in Azure. Learn how Cosmos DB table API, Gremlin API, and Cassandra API support these data models.

Explore unstructured data and Azure storage options, including block storage with Azure disks, file storage via Azure files, and object storage with Azure blob storage, using rest APIs.

Compare structured relational data and non-relational data storage in Azure. Learn how OLAP and OLTP workloads align with Azure Synapse Analytics and Azure SQL options.

Explore Azure database: Cosmos DB NoSQL with SQL API or MongoDB API, relational OLTP on Azure SQL, and Cosmos DB Gremlin API, Azure Synapse Analytics, Azure Files, and Blob Storage.

Explore databases in Azure, including tables, rows, columns, and SQL queries. Learn how schema and strong transactions enable banking apps, and review Azure SQL Database and Azure SQL Managed Instance.

Explore Azure SQL Database, a fully managed service with serverless compute, hyperscale storage, encryption at rest, and SQL Server authentication or Azure Active Directory with optional MFA.

Learn to connect to a MySQL database in Azure Data Studio, run create table and insert scripts, and query a simple students table with a primary key and five rows.

Explore how relational databases use tables and a primary key to identify rows and a foreign key to link courses to instructors.

Explore structured query language basics and commands across DDL, DQL, DML, and DCL, plus transactions with commit and rollback, including create, drop, alter, select, insert, update, delete, grant.

Explore how indexes speed data retrieval by examining clustered and non-clustered indexes, including the primary key’s automatic unique clustered index, and multi-column indexes for querying by id, first_name, and last_name.

Explore views as virtual tables mapped to queries, and learn to join course, student, and enrollment data to display enrollments and create the all_courses_with_students view.

Learn how normalization reduces data redundancy and enhances integrity by splitting data into related tables (students, instructors, courses, enrollments) and using foreign keys.

Explore transactions and acid properties: atomicity, consistency, isolation, and durability, in relational databases, using a two-step transfer that either completes together or rolls back.

Explore Azure SQL Database purchase models, compare vCore-based and DTU-based options, and learn how compute and storage scale in general-purpose, hyperscale, and serverless configurations.

Explore Azure SQL Database features: single database, elastic pool, and the database server. Learn how a single server can host multiple databases and share compute and storage across databases.

Learn how to connect to Azure SQL Database by configuring firewall rules, IP access, and private endpoints; use the Azure Hybrid Benefit to re-use licenses and enable read scale-out with read-only replicas.

Explore Azure SQL managed instance, offering near 100% SQL Server feature compatibility, cross-database queries, database email, SQL Server Agent, and native virtual network support for on-premises migrations.

Compare SQL Server on Azure Virtual Machines, which offers full admin control, with Azure SQL Database and Azure SQL Managed Instance, both fully managed and feature compatible.

Explore azure managed relational databases: MySQL, MariaDB, and PostgreSQL, each offering automatic updates, backups, and scalable, fully managed services with high availability.

Explore relational data scenarios in Azure: choose sql server on vm for os access, or managed instance for database mail and agent, or sql database for installs; enable read scale-out.

Build a growth mindset by embracing challenges, learning from feedback, celebrating effort, and staying curious to unlock your full potential.

Explore Azure Cosmos DB, a fully managed, globally distributed NoSQL database that auto scales and supports multi-regional writes, with Core(SQL), MongoDB, Table, Gremlin, and Cassandra APIs for semi-structured data.

Create a Cosmos DB account with the Core (SQL) API, then build a database, container, and documents, and query them with SQL to store and retrieve todo items.

Create an Azure Cosmos DB account with MongoDB API, then use Data Explorer to build a database, collection, and documents, configure a shard key, and run filters.

Create an Azure Cosmos DB account for the Table API using a serverless provisioning model, then add a session table and key-value entities with partition keys to store JSON data.

Learn how to create a graph in Azure Cosmos DB using the Gremlin API, add vertices and edges, and query the graph with Gremlin commands.

Explore how Azure Cosmos DB delivers single-digit millisecond responses at scale through horizontal partitioning, flexible schema, and multiple consistency levels from strong to eventual, across multiple APIs.

Review the Cosmos DB structure, from account to database, container, and item, and compare terminology across SQL API, Cassandra API, MongoDB, Gremlin, and Table API.

Learn how Cosmos DB stores data by partitioning items into logical partitions using a partition key, grouped into physical partitions and replicated for high availability and scalability.

Cosmos DB modes: provisioned throughput requires configuring request units with multi-region options and latency guarantees. Serverless scales automatically in a single region and is billed per hour for request units.

Explore Azure Cosmos DB scenarios, including its serverless auto scaling, data structure with accounts, databases, containers, and partitions, and API options like Gremlin API, Core(SQL) API, and MongoDB API.

Discover Azure storage, including block storage, file storage, blob storage, and Azure table storage, and learn about durability options like LRS, ZRS, GRS, and GZRS across region pairs.

Explore how Azure disk storage works for virtual machines, comparing managed and unmanaged disks, and choosing disk types from Standard HDD to Ultra for performance and availability.

Learn to set up Azure file storage, create a storage account, and share files across Windows, Linux, and macOS using SMB or NFS for VMs with performance and redundancy options.

Learn how to create and configure Azure file shares in a storage account, set quotas and tiers, and connect from Windows, Linux, or macOS using authentication options.

Explore Azure blob storage as scalable object storage for unstructured data, with containers, block/append/page blobs, and Azure Data Lake Storage Gen2 for enterprise analytics and hierarchical folders.

Explore Azure storage features, including queue service for decoupling and a NoSQL table store, while comparing Cosmos DB table API with Azure Tables and multi-region read/write capabilities.

Explore scenarios for Azure storage, including when to reuse storage accounts, create file shares with Azure Files, and apply geo-zone-redundant storage and archive tier to optimize availability and cost.

Teach others to deepen your understanding and improve your communication by simplifying complex ideas. Sharing knowledge boosts confidence, expands your learning, and fosters empathy from the learner’s perspective.

Explore fundamentals of data analytics, from big data and data warehousing to Spark, batch and streaming pipelines, ETL and ELT, using Azure Synapse Analytics, Azure Data Factory, and Power BI.

Explore how data analytics converts raw data into intelligence by ingesting, processing, and storing data in warehouses or lakes, then analyzing and visualizing to reveal trends and inform decisions.

Explore the five data analysis categories: descriptive, diagnostic, predictive, prescriptive, and cognitive analytics. Learn how to monitor KPIs, generate alerts and reports, predict outcomes, and apply ML-driven insights.

Explore the 3Vs of big data—volume, variety, and velocity—and compare data warehouses with data lakes, including cloud storage options and analytics reading data stored in formats like parquet.

Explore de-normalized star schema to model data for fast analysis, with fact and dimension tables in relational data warehouses, enabling easy querying.

Explore three key Azure data analytics services: Azure Synapse Analytics, Azure Data Factory, and Power BI, covering end-to-end analytics, data integration, ETL/ELT pipelines, data warehousing, and visualizations.

Explore Hadoop, Apache Spark, and Databricks, comparing storage with HDFS, processing with MapReduce and in-memory Spark, and SQL querying with Hive.

Explore Apache Parquet, an open-source columnar storage format that enables high compression for big data, supported by Azure Data Factory, Azure Data Lake Storage, and Azure Synapse.

Explore Hadoop and Spark on Azure with HDInsight and Databricks, running Apache Spark workloads and integrating with Azure Synapse Analytics and Azure Data Factory pipelines for ETL, ELT and analytics.

Search for Azure Databricks in the Azure portal and create a Databricks workspace with a resource group, using the Standard pricing tier and networking and tags to run Spark jobs.

Launch the Azure Databricks workspace, create a one-node cluster, and explore data science and machine learning with notebooks and PySpark using Azure Open Datasets; terminate the cluster.

Master parallel processing by separating storage data lake and compute, provisioning on-demand nodes to run ad-hoc jobs, and terminating them after completion for pay-as-you-go efficiency, with Spark and Azure Synapse.

Explore batch pipelines in Azure, learn how batch processing buffers data, defines run conditions, and orchestrates with Azure Data Factory from data lake storage to an analytical data store.

Explore streaming pipelines in Azure, processing real-time data as it arrives using Event Hubs or IoT Hub, then generating analytics and reports with Stream Analytics or Spark.

Compare stream and batch processing: batch handles large data with high latency, suitable for complex storage or analysis, while stream delivers low latency for simple storage or short-interval aggregations.

Compare etl and elt: extract transform load vs extract load transform, with parallel processing in etl and iterative transformations in elt using a target datastore such as Azure Synapse Analytics.

Explore Azure Synapse Analytics for end-to-end analytics, including data integration, data warehouse, pipelines, and SQL or Spark processing, with data lake Gen2 storage and multiple data sources.

Discover Azure Data Factory, a fully managed serverless data pipeline service for ETL and ELT, ingesting from Redshift, BigQuery, Oracle Exadata, Teradata, and Azure data services with data flows.

Showcases building a data lake storage Gen2 and a SQL Server database, using Azure Data Factory and Synapse Analytics to move data as csv into the lake.

Create an Azure Data Factory pipeline to copy data from an Azure SQL Database to Azure Blob Storage as a CSV file, using a copy task.

Explore Azure Data Factory components, including pipelines, activities, linked services, datasets, and integration runtimes, and learn how data movement, data transformation, and control activities orchestrate workflows.

Learn to create an Azure Synapse Analytics workspace, attach a Data Lake Storage Gen2 account, and configure the default SQL pool for integrated analytics.

Compare Azure Synapse Analytics with Data Factory and explore end-to-end analytics, including pipelines, data lake storage, SQL pools, and Spark pools for analytics.

Explore getting started with Power BI in Azure data fundamentals, creating datasets, dashboards, and reports from a sample financial file, and learn to upload, import, and visualize data in workspaces.

Learn to create Power BI reports by adding and configuring visualizations such as bar, line, pie charts, treemaps, maps, and tables using country, sales, and profit.

Create and save Power BI reports using AI to generate visualizations from a published dataset, exploring map, column, table, pie, and donut charts and using the ask a question feature.

Explore Power BI components, from reports and dashboards to the Power BI service, desktop, and report builder, and learn to share, pin visuals, and create paginated reports across data sources.

Explore Power BI terminology, including workspaces, datasets, reports, dashboards, and paginated reports. Understand how each component connects: datasets feed reports, reports aggregate visuals, and dashboards display tiles from multiple reports.

Review essential data analytics workflow. Ingest with Azure Data Factory, IoT Hub, and Event Hub; store in Data Lake Gen2; analyze with Azure Synapse or Databricks; visualize with Power BI.

Categorize analytics as descriptive, diagnostic, and cognitive, including semantic text analysis and sentiment reviews, while exploring Azure Data Factory data movement, control flow, linked services, and Power BI service.