DP-900: Microsoft Azure Data Fundamentals Course - Feb 2024

Create a free Azure subscription to access resources, and activate a $200 credit for 30 days. Sign in with a Microsoft account and explore always free services for 12 months.

Navigate the Azure portal to create, manage, and monitor resources through a web-based dashboard, customize views, and leverage global search to find services and resources, including marketplace options.

Compare infrastructure as a service, platform as a service, and software as a service, highlighting how much you manage versus how much the vendor handles in cloud deployments.

Activate a Microsoft Learn sandbox to keep using the Azure portal for free after the 12-month credits expire, then switch directory and practice for up to four hours.

Explore data fundamentals: structured, unstructured data, Azure services, batch and streaming data, relational concepts like columns and keys, analytics techniques (descriptive, diagnostic, predictive, prescriptive, cognitive), and ETL versus ELT.

Define data and its three types—structured, semi-structured, and unstructured—using ERP, CRM, and banking examples, then map each type to cloud storage and OLTP/OLAP processing.

Compare OLTP and OLAP workloads: OLTP handles a huge number of small transactions on current data, while OLAP runs complex queries on historic data in data warehouses.

Explore roles in data workloads such as database administrator, database engineer, and data analyst, and map them to end-to-end data flow from OLTP to data models, pipelines, and insights.

Distinguish between transactional (OLTP) and analytical (OLAP) processing, showing how high-volume transactions feed cleansed data into analytics to drive strategic business decisions.

Explore the characteristics of relational data, including tables, rows, columns, and primary and foreign keys, and how normalization and joins enable consistent, scalable queries for online transaction processing applications.

Explore relational data structures, including tables with primary and foreign keys, and indexes and views, to optimize data organization and speed up data access.

Learn four Azure provisioning options: command line interface, portal, PowerShell, and resource manager templates, to automate creating databases and SQL servers.

Discover why SQL database in Azure offers a fully managed relational database as a service with mission critical capabilities, elastic pools, and high-uptime scalability backed by an SLA.

Explore three Azure SQL Database deployment options: single database, elastic pool, and managed instance, each with distinct resource provisioning in memory, storage, or a virtual network.

Explore Azure SQL deployment options—single database, elastic pool, and managed instance—and learn basic firewall setup, connectivity, and database provisioning in a hands-on demo.

Learn how PostgreSQL, MariaDB, and MySQL can move to Azure cloud or edge. Use Azure database migration service for a fully managed, scalable deployment with automatic backups and SSL security.

Identify query tools for databases, including portal editor, SQL Server Management Studio, data studio, Visual Studio Code, and command line utilities, and compare visualization and execution plans.

Learn to build SQL queries using select from where group by having and order by, including counting addresses by city and state in the adventure work database.

Identify the right Azure data offering for relational and non-relational workloads, from SQL Server for transactional data to Cosmos DB for non-relational datastore and blob storage for unstructured data.

Explore non relational databases, their types like key-value, documents, column, and graph, and how to deploy, secure, and manage them with Azure Cosmos DB APIs.

Compare traditional RDBMS limitations in performance, scalability, and flexibility. Explain how NoSQL enables horizontal scaling, handling unstructured data, and schema flexibility for big data and real-time applications.

Explore how relational SQL databases differ from NoSQL, with fixed schemas and tables versus dynamic, multimodal data models, and vertical versus horizontal scaling for transactional versus big data use cases.

Explore four NoSQL database types—key-value stores, document stores, column stores, and graph databases—and how they differ in structure, queries, and real-world use cases.

JSON stands for JavaScript object notation, a lightweight, easier-to-read format like XML. It uses curly braces for objects and square brackets for arrays, with strings, numbers, booleans, or nested objects.

Explore Microsoft Azure's NoSQL offerings, from infrastructure and platform as a service to storage, data lake, and Cosmos DB, including blob, table, and file storage options and APIs.

Explore Azure storage services overview, including storage accounts and blob, file, queue, and table storage, with notes on durability, availability, encryption, and regional redundancy.

Provision an Azure storage account in the portal, select subscription, resource group, region, and storage type, then explore containers, blobs, file shares, queues, tables, and replication options.

Learn how data redundancy secures availability and durability by creating multiple copies: locally and zone-redundant storage, and geo-redundant storage with read-only access in secondary regions, balancing cost and availability.

Explore Azure blob storage, a binary large object store with block, page, and append blobs, using a flat container structure for streaming, logging, backup, and archiving.

Explore NoSQL table storage as a key-value, semi-structured database with partitions and flexible fields, distinct from relational databases and foreign keys.

Explore azure file share storage as a fully managed, cloud-linked file storage that enables concurrent on-premises and cloud access via SMB or NFS, supporting Windows, Linux, and macOS.

Learn how Cosmos DB evolved from DocumentDB to solve global distribution and scalability, making a JSON document store easy to architect and manage with few clicks.

Provision a Cosmos DB account in the Azure portal by selecting subscription, resource group, account name, and the Core SQL API; configure location, global distribution, networking, encryption, then create.

Explore analytics workloads and data warehouse concepts, outline learning objectives, and compare with transactional databases, covering batch and real-time processing and key services like data factory and Power BI.

Discover how data warehouse gathers data from systems, applies extract, transform and load to clean and organize, stores data in facts and dimensions, and enables filtering, reporting, and data mining.

Data warehouse unifies data from multiple systems, improves data quality and relevance, enables easy access for non-experts, ensures consistent KPI reporting, and drives a data-driven culture for faster growth.

Explore the star schema design pattern for data warehouses, identifying fact and dimension tables, numeric values like total sales, and the snowflake variant with normalized dimensions.

Compare cloud data warehousing with on premises solutions, highlighting capital expense savings, platform as a service offering, scale storage and compute, and time to insight.

Compare traditional on premises warehousing with cloud architectures that separate compute and storage, ingest data with data factory, and emphasize data quality and dimensional modeling.

Explore how a unified analytics workspace combines data pool, data factory, notebooks, and spark jobs into an end-to-end analytics solution.

Design batch processing architectures for large static data, select storage and analytics tools, and orchestrate ingestion and reporting with data factory.

Explore real time processing architecture, from real time message injection to streaming processing, analytical data stores, and analytics and reporting, designed for high volume and low latency insights.

Azure Stream Analytics is a managed real-time analytics service that ingests, processes, and outputs data streams from sources like event hubs, IoT devices, and logs to Power BI or storage.

Batch processing collects large data and processes it later, while stream processing handles data in real time with low latency. The caption cites examples such as credit card bills and stock market alerts, and contrasts the advantages and drawbacks of batch versus stream processing.

Explore how a data lake serves as a scalable repository for all data types, including unstructured data, stored in native or raw format and ready for ingestion and transformation.

Enable hierarchical namespace on a storage account to create data lake storage gen2 and understand the differences from gen1 on hadoop dfs and blob storage for scalable big data analytics.

Azure Data Factory is a cloud data integration service that copies and transforms data with 80 connectors, enabling visual, code-free pipelines across on-premises and cloud sources.

Explore the essential Azure data factory components, including integration runtime, activities, data sets, linked services, sources, destinations, and pipelines, and learn how they collaborate to move and transform data.

Discover how a pipeline groups multiple activities, how a dataset represents data stored in a linked service, and how data flows from a source through linked services and connections.

Learn how to use Azure Data Factory triggers to run, execute, and initiate pipelines, including scheduling and recurrence options.

Explore how Azure Databricks fuses Spark with Azure cloud to process, clean, and wrangle data directly in data lake, with native Azure Active Directory authentication and unified billing.

Map Azure data services to a modern data platform from load to visualize, covering relational, unstructured, and streaming data with Event Hubs, Stream Analytics, Data Factory, Polybius, and Power BI.

Explore how Power BI provides cloud-based reporting for visualizations and dashboards from diverse data sources. Build data models with calculations, use quick insight, and access dashboards on mobile.

Explore the building blocks of Power BI—visualisations, datasets, reports, dashboards, and tiles—and learn to connect to Excel, SQL Server, Oracle, Facebook, and MailChimp, filter data, and build insightful visuals.

Learn the end-to-end workflow from connecting and getting data to visualizing and sharing the report, including cleaning with Power Query using M, modeling relationships, and exploring charts and maps.

Explore data visualization and reporting to transform data into actionable business insights. Learn common chart types—bar/column, line, matrix, treemap, key influencer, and scatter—that illuminate trends and support business intelligence.

Explore five data analytics techniques—descriptive, diagnostic, predictive, prescriptive, and cognitive—and learn how they reveal what happened, why it happened, future trends, and recommended actions using artificial intelligence.

Explore Azure relational database security layers, including network firewalls, access management, authentication and authorization, auditing, advanced threat protection, encryption in transit and at rest, dynamic data masking, and vulnerability assessment.

Explore Azure queue storage as a decoupling mechanism for producer and consumer apps, using queues to buffer messages and enable asynchronous processing of tasks like image download and resize.

Explore Azure disk storage for virtual machines, linking operating system and data disks, and choosing between managed and unmanaged disks with standard, premium, or ultra SSD options.

Configure secure storage access using storage account keys, shared access signatures, and dual Active Directory authentication with RBAC and ACL, plus firewall and virtual network rules to enforce least privilege.

Cosmos DB introduces multiregional writes, letting any data center read and write, update data locally, and replicate across all centers for seamless global data access.

Examine Cosmos DB consistency levels—strong, bounded stillness, session, consistent prefix, and eventual—and learn to balance availability and latency while applying per-request overrides.

Explore Cosmos DB security options, including role-based access control, network security, key management, cors, private endpoints, and the advanced security alert feature.

master inserting and querying data in Cosmos DB by creating a database and container, adding JSON items, using Data Explorer, and running queries with partition keys and throughput considerations.

Explore four NoSQL provisioning options—portal, CLI, PowerShell, and ARM templates. The lecture emphasizes using ARM templates for production deployments and saving the JSON configuration for future multi-environment deployments.

Paginated reports are designed to print across multiple pages, ensuring all content prints when published. Create and preview them with the report builder, then publish to Power BI service.

Explore interactive reports in power base for real-time drill-down charts with hover detail. Behind a power base server, real-time changes like add or remove columns, sort, and filter reveal insights.

After dp-900, explore Azure certification paths for data administrator, data scientist, data engineer, and solution architect, and use recommended courses with practice tests, quizzes, and notes to help you certify.