The Microsoft Azure data warehouse is growing fast. In today’s data warehouse architectures, a data warehouse is a central repository of consolidated data from one or more sources, storing current and historical data. This data can be used for reporting and analysis. In most cases, using Azure Synapse Analytics and Azure SQL Database has proven to be the right choice for a data warehouse. This article will help you find the right technology.
Azure Synapse Analytics is a cloud platform as a service (PaaS) that offers the Azure platform to provide complete on-demand analytics services or customized resources without servers. The main components are Synapse SQL Tank, Spark, Synapse Pipelines and Studio Apps. This article will focus on Synapse SQL Tank, which refers to the generic Azure Synapse resource for data warehouses (OLAP). Azure Synapse SQL Pool is designed as a massively parallel processing (MPP) system with a scalable architecture that distributes data processing across multiple nodes.
On the other hand, Azure SQL Database is a fully managed PaaS data engine that supports most database management functions and is particularly suited for OLTP workloads based on multiple symmetric processing. Azure SQL DB offers deployment options such as standalone databases, elastic pools and managed instances. This article describes the deployment options of Azure SQL DB compared to Azure Synapse.
What Is Azure Synapse Analytics?
Azure Synapse Analytics, formerly Azure SQL Data Warehouse, has evolved into a borderless analytics service that combines enterprise data warehouses and big data analytics. Azure Synapse combines these two worlds into a single environment that enables data collection, preparation, management and presentation for business intelligence and machine learning.
Azure Synapse vs SQL Database
Workload Types
Azure Synapse is ideal for OLAP workloads with clearly defined read and write tasks. This approach accelerates large workloads and complex queries by decoupling and parallelizing complex tasks. In this case, data is usually stored in a denormalized form using a schema.
Due to a large number of short reads and low data load, Azure SQL Database can perform these tasks more efficiently. This is also true for normalized data stored in multiple tables.
Scaling As A Function Of Demand And Cost
Azure Database PaaS allows you to scale service levels according to workload needs. With SQL Common Compute and linear scaling per storage unit, Azure Synapse provides more granular data processing for critical operations such as complex aggregations, serialization, and large amounts of data. Computation can be interrupted even when there is no query in the dataset, significantly reducing computational overhead.
Azure SQL DB consists of a service layer that ensures that data is processed correctly. With a simple data warehouse query model and low overhead, Azure SQL DB provides an easily maintainable data warehouse with an estimated cost model.
Synapse Interval, Backup And Replication
The Azure Synapse solution allows storing data in a snapshot format. Azure Synapse can be used to recover data for business continuity and disaster recovery. This is useful when you create a copy of a database for testing or deploying a built-in option for automatic and customized recovery over a specified time. An eight-hour recovery point objective (RPO) is currently supported, and snapshots of the last seven days are available in Azure Core. At this stage, geodata can be backed up daily.
Azure SQL DB also supports active geographic replication (Azure Synapse relies primarily on storage replication and does not synchronize with the core server).
Benefits Of Using Azure Synapse Analytics
Powerful Insight
The deep integration of Power BI and Azure Machine Learning extends the ability to discover insights from any data and apply machine learning models to any intelligent application. This significantly shortens the time to value.
Unmatched Security
Azure Synapse software offers the most advanced security and privacy features on the market. These features are built into Azure Synapse and include automatic threat detection, strong data encryption and granular access control.
Flexibility
Azure Storage is highly resilient because the computer and storage components are separate. Computing can grow on its own. Resources can be added and removed during query execution.
Integrated Power Bi Application
The Power BI workspace integrates directly into the Synapse application. Synapse Studio provides access to reports and databases and makes it easy to create new databases and reports from data processed in Synapse Azure.
In addition, SQL Serverless looks like a traditional SQL database, making it easy to perform advanced analytical queries during import. Power BI used to be aimed at business users, but with these changes, it has been moved into the hands of data scientists. This is a logical and highly recommended move.
Integration With Machine Learning
Synapse Analytics has machine learning (ML) capabilities that can be used for a variety of purposes. In general, the Machine Learning algorithms uses to collect and understand data. With Azure Data Factory, you can create data pipelines that transform business data into an machine-learning-friendly format and generate insights from that data using reports built with Apache Spark or serverless SQL pools.
Explore The Data Lake
Some file formats are not easy to analyze, so additional tools are needed. For example, highly compressed Parquet files are great for archiving but are difficult to read. In Synapse, you can right-click on a file and open it using SQL.
Allocating Resources
In Azure Synapse Data Warehouse, resource allocation is measured in Data Warehouse Units (DWU). This measures the critical resources allocated to the SQL data warehouse, such as CPU, memory, and IOPS; increasing the number of DWUs improves resources and performance.
Redundant Storage
Synapse Data Warehouse stores all data in redundant volumes on the local server and Azure Premium. Multiple copies of synchronized data are stored in the local data center, allowing transparent backup in the event of a local failure. Synapse Data Warehouse also uses snapshots stored in Azure to perform regular automatic backups of active (non-offline) databases.
PolyBase
Azure Synapse Data Warehouse and PolyBase provide users with a unique ability to move data across the ecosystem and create advanced hybrid scenarios using native and non-relational data sources.
Summary
Azure SQL Database is ideal for data warehouses with small data volumes and low workloads. Azure Synapse and SQL Pool can handle large amounts of data for more complex data warehouses.
Azure SQL Data Warehouse and Azure Synapse are variants of Microsoft’s Azure PaaS platform, but their original purpose is slightly different. Azure SQL DB is designed for OLAP workloads. However, this does not mean that Azure Synapse is required for data warehouses. With Existbi’s Azure and BI Consulting, we can help you choose the right solution.
