Azure SQL Data Warehouse Synapse Analytics Service

Discover how Azure Synapse Analytics provides a fully managed, cloud data warehouse unifying enterprise warehousing and big data analytics, with scalable compute and seamless integration with the Microsoft data stack.

Compare cloud data warehousing to on-premises options, highlighting capital expense avoidance, platform as a service, on-demand scale, storage and compute separation, and massive parallel processing for faster insights.

Contrast traditional on-premises data warehouses with modern cloud architectures, highlighting separate compute and storage, diverse data sources, data quality, and the end-to-end data factory pipeline for analytics.

Explore how Azure Synapse Analytics unifies Data Factory, storage, data warehouse, and analytics into a single workspace, enabling end-to-end analytics with dedicated or serverless pools and Power BI visuals.

This demo guides creating a dedicated sequel pool as a separate service, detailing server setup, firewall rules, performance level, and pausing compute to save costs.

Connect a dedicated SQL pool with SSMS, configure firewall rules to authorize your laptop IP, and access AdventureWorks tables for analytics.

Create a new Azure Synapse Analytics Studio workspace by selecting subscription, resource group, region, workspace name, and a new data storage account with a file system, then open Synapse Studio.

Explore synapse studio v2 to link storage, develop scripts and notebooks, build pipelines, and monitor and manage end-to-end analytics from a single interface.

Demonstrates creating a dedicated SQL pool and an Apache Spark pool inside a Synapse workspace, with choices for performance, auto scale, and cost-saving pause.

Explore working with a dedicated SQL pool in Azure Synapse, create and load tables, insert millions of rows, run queries, and analyze taxi data with round-robin and hash distributions.

Explore and analyze data from different sources with an Apache Spark notebook in Synapse Analytics Studio, loading the New York taxi dataset from blob storage into a Spark database.

Analyze data with a serverless sql pool, compare it to a dedicated pool, and learn to link external data sources like blob storage and Spark to a serverless database.

Move data from SQL Server to a data warehouse using Synapse Analytics Studio's data factory, via the copy editor tool and a simple pipeline within the workspace.

Demo shows monitoring all resources, activities, and tasks in Synapse Studio, including pipelines, triggers, integration runtimes, Spark queries, serverless queries, and server pool queries, with logs and performance insights.

Discover Azure Synapse benefits, including limitless scale and worldwide infrastructure, a unified analytics studio for data prep, warehousing, and artificial intelligence tasks, with code-free tools and 85 native connectors.

Explore Synapse unified experience that empowers data professionals to connect, ingest, transform, and query data from multiple sources with a visual environment and multi-language support.

Explore cloud warehousing with azure synapse analytics service, a unified, paas data warehouse that outperforms on-premise architectures by reducing time to market and boosting development efficiency via synapse studio.

Explore cloud data warehouses and massive parallel processing in Azure SQL data warehouse synapse analytics service. Learn storage, data distribution, table types, distribution keys, and migration from on-premises to cloud.

Explore Azure Synapse MPP architecture, featuring a control node, compute nodes, data storage separation, data movement service, and data distribution units for parallel query execution.

Explore storage and sharding patterns in Azure SQL Data Warehouse Synapse Analytics Service, including replicated, round-robin, and hash distributions, for optimized parallel queries across compute nodes.

Learn how to distribute data in Azure Synapse using hash, round-robin, and replicated distributions to avoid skew, ensure even compute, and choose non-updatable keys with at least 60 distinct values.

Select the smallest data types and default lengths to save space and speed data movement across compute nodes, noting Unicode size and table types: clustered columnstore, heap, and clustered index.

Partitioning splits a table into partitions, letting queries target date-based ranges in data warehouses and improve performance. It eases loading, removal, and maintenance, but avoid too many partitions.

Apply dimensional modeling principles to fact and dimension tables in an Azure Synapse Analytics data warehouse, using hash or round-robin distributions and partitioning and indexing considerations.

Analyze data distribution strategies for migrating an on-premises data warehouse to azure synapse, comparing round-robin and hash distribution on the product and fact transaction history tables, with data type considerations.

Learn the Azure SQL data warehouse Synapse architecture, storage and compute billing, three sharding patterns—hash, round-robin, replicated—for distribution and partitioning ahead of migration.

Learn data loading and migration in the azure sql data warehouse synapse analytics service, focusing on MBP architecture and the differences between S.A.S. and Polybius loading methods.

Leverage parallel processing in Azure Synapse by scaling units, using batches of one million rows, and employing temporary tables with round-robin distribution for fast, minimally logged data loads.

Compare single client loading methods like SAS at your data factory or BCP with parallel loading via Polybius, highlighting control node bottlenecks and compute node scaling.

Loading via SARS can bottleneck the control node with many small values, while Polybius loads large data in parallel from adjure blob storage, bypassing the control node.

Explore how to load a small dimension table from on-premises to the Adjure data warehouse using an SSIS package, with straightforward source-to-destination mapping and connection managers.

Migrate an on-premises table to azure synapse analytics using polybase, exporting to a flat file, uploading to blob storage, executing the six-step load, monitoring, and verifying 60 distributions.

Load data from on premises to a data warehouse using data factory, moving a flat file from blob storage through a Polybius copy pipeline, with monitoring and verification.

Explore best data loading practices for a second data warehouse and the roles of control and compute nodes in migration, including polybius methods for large data.

Move data from blob storage to data lake, then to the Sanabis data pool using a data factory pipeline, with accounts and pipelines created.

Learn to migrate data from blob storage to a data lake and into the sign ups data pool using a data factory pipeline, via six steps.

Explore five layers of defense to secure your Azure data warehouse: monitor traffic, limit access by IP, enforce authentication and authorization, and apply data protection and encryption.

Enable advanced data security in Azure SQL Data Warehouse to discover and classify sensitive data, assess vulnerabilities, and detect anomalies with data discovery, classification, vulnerability assessment, and advanced threat protection.

Explore auditing in Azure SQL Data Warehouse to track database events and write audit logs to storage, Log Analytics, or Event Hub, enabling monitoring and compliance with a security standard.

Configure server-level firewall rules and virtual network rules in Azure SQL Data Warehouse to restrict access to approved IPs and subnets, with encrypted connections by default.

Enable transparent data encryption to protect data in transit with transport layer security. Encrypt data at rest for databases, backups, and logs using AES-256 with securely stored keys.

Dynamic data masking hides sensitive data for unauthorized users without altering the database, using functions: default, partial, email, and random, to mask values such as credit card numbers on demand.

Enforce authentication and authorization in Azure SQL Data Warehouse, using SQL or Active Directory authentication, granting granular permissions (columns, tables, views) and applying role level security for multitenant data.

Explore the five layers of defense in Azure Synapse Analytics, including data discovery and classification, vulnerability assessment, firewall and virtual network controls, authentication and authorization, and auditing with encryption.

Explore backup and restore, workload management, and monitoring tools in the Azure Synapse Analytics service portal, with tips on configuration optimization and safe demo resource deletion.

Explore scaling compute nodes, review activity and auditing logs, apply tags and security settings, and manage maintenance, geo backup, and connection strings for the Azure SQL data warehouse Synapse Analytics.

Explore streaming analytics, load data via the portal, query the data warehouse with the query editor, and build dashboards and reports with data factory and Power BI.

Back up your Azure Synapse data warehouse with snapshots and incremental backups that offer point-in-time recovery, and use automatic and user-defined restore points for safe rollback.

Explore Azure Synapse Analytics pricing, including compute and storage costs, reserved savings, and regional availability, with guidance on when to choose pay-as-you-go or reserved hours.

Learn to monitor an Azure Synapse data warehouse with query activity and plans, set alerts for resource usage, view metrics, and configure diagnose settings.

Monitor current and past service health to diagnose issues in Azure SQL Data Warehouse, and learn how to raise a Microsoft support ticket via the portal.

Learn how to manage data warehouse workloads in Azure Synapse by classifying queries, prioritizing high-importance jobs, and isolating resources to meet SLA during loading, transforming, and analytics.

Master deleting resources in azure synapse analytics by choosing to remove data factory, data warehouse, and blob storage individually or delete the whole resource group, understanding irreversible consequences.

Explore configuring and optimizing the data warehouse in Azure Synapse, including backup and restore, cost management, workload classification, importance, and isolation, query activity, alert metrics, diagnostic settings, and troubleshooting.

Explore the basics of data warehousing, why we need it, how it differs from transactional databases, and foundations like dimensional modeling, facts and dimension tables, star and snowflake schemas.

Consolidate data from ERP, CRM, emails, and spreadsheets by extracting, transforming, and loading it into a multidimensional data warehouse for reporting, slicing, and data mining.

Understand why a data warehouse is needed to consolidate disparate data, improve data quality and readability, ensure KPI consistency, and enable a data-driven culture that supports faster growth.

Design a user-friendly data warehouse that serves business users with consistent, validated data from multiple sources, enabling fast slicing and dicing, credible analytics, and a foundation for decision making.

Define user personas and their needs, align responsibilities and goals, and design clear data presentations to support decisions. Ensure data accuracy, relevance, periodic loading, simple interfaces, and updates for requirements.

Explore the differences between OLTP and OLAP, highlighting how OLTP handles many small transactions on current data, while OLAP analyzes historic, summarized data in data warehouses at massive scale.

Explore dimensional modeling for data warehouse design, focusing on facts and dimensions, star schemas, and overnight etl to deliver simple, understandable, high-performance analytics.

Defines facts as business measures and explains additive fact tables linked to dimension tables via foreign keys, highlights the time dimension, and explains composite keys in fact tables.

Explore how dimension tables describe the context of a business event by capturing who, what, where, when, how, and why, with product attributes like category and brand.

Explore star schemas with a central fact table and surrounding dimensions, and snowflake schemas with multi-layer dimensions; these models are easy to understand for business users and extendable.

Gain a concise recap of data warehouse concepts, including OLTP vs OLAP, dimensional modeling, and the roles of facts and dimensions, plus differences between star and snowflake schemas.