Choosing OLAP Storage: Azure Synapse Analytics

Previously, I described key metrics that should be considered when making the critical choice of OLAP storage for your needs in this article — How to Choose the Right OLAP Storage. Now, I have decided to apply this approach in practice and examine the most popular, as well as some of the not-so-popular, OLAP storages. The main goal is to discern the strengths and weaknesses of each OLAP storage solution and determine the most fitting use case for each.

What is OLAP Storage?

What is online analytical processing? Online analytical processing (OLAP) is software technology you can use to analyze business data from different points of view. In OLAP scenarios, datasets can be massive — billions or trillions of rows. Data is organized in tables that contain many columns, and only a few columns are selected to answer any particular query. Results must be returned in milliseconds or seconds. Basically, OLAP storage refers to storage optimized for analytical workloads.

Here is the list of OLAP storages in this research:

• ClickHouse
• StarRocks
• Apache Druid
• Snowflake
• Azure Synapse Analytics ⬅️You Are Here
• SQL Server With Columnstore Indexes
• AWS Redshift
• Google Cloud Platform (GCP) BigQuery

Azure Synapse Analytics logo

Overview

• Developers — Microsoft
• Type — Enterprise analytics service

Azure Synapse Analytics is an enterprise analytics service that accelerates time to insight across data warehouses and big data systems. It seamlessly combines the best of SQL technologies used in enterprise data warehousing, Apache Spark technologies for big data processing, and Azure Data Explorer for log and time series analytics.

This powerful platform simplifies data discovery for big data and reduces the time to insights. It’s designed as an all-in-one solution that brings together data warehousing and big data analytics. With Azure Synapse, you have the freedom to query data on your terms, using either serverless or dedicated resources, all at scale.

Storage Architecture & Semi-Structured Data Support

1. Storage Format:

Azure Synapse Analytics provides a flexible storage format for storing and analyzing data. The storage format primarily revolves around the concept of data lakes, which are repositories that store vast amounts of structured, semi-structured, and unstructured data in their native format until it’s needed.

Here are some key components and concepts related to Azure Synapse Analytics storage format:

• Unified Data Platform: Azure Synapse Analytics integrates with various data storage options within the Azure ecosystem, including Azure Data Lake Storage and Azure Blob Storage. This unified platform allows you to store data in its original format without requiring transformation or schema enforcement upfront.
• Parquet and Delta Lake: Two common storage formats used within Azure Synapse Analytics are Parquet and Delta Lake. Parquet is a columnar storage format optimized for analytics workloads, offering efficient compression and encoding techniques, which enable fast query performance. Delta Lake, on the other hand, adds transactional capabilities to data lakes, providing features such as ACID transactions, schema enforcement, and time travel.
• PolyBase: Azure Synapse Analytics includes PolyBase, a technology that enables querying external data sources such as Hadoop, Azure Blob Storage, and Azure Data Lake Storage directly from T-SQL queries. PolyBase allows you to leverage the data stored in various formats, including CSV, JSON, Parquet, ORC, and more, without needing to move or transform the data.

2. Separation of Compute and Storage:

Azure Synapse Analytics combines distributed query processing capabilities with Azure Storage. It uses a scale-out architecture where computational processing of data is distributed across multiple nodes. The key feature is the decoupling of compute and storage, allowing independent scaling of compute resources regardless of storage needs.

In this architecture:

Compute:

• Compute is separate from storage.
• For dedicated SQL pools, the unit of scale is a data warehouse unit.
• The control node optimizes queries and passes operations to compute nodes.
• Data Movement Service (DMS) moves data across nodes for parallel execution.

Storage:

• Azure Storage keeps user data safe.
• Data is stored and managed by Azure Storage, with a separate charge for storage consumption.
• Serverless SQL pools allow querying data lake files.
• Dedicated SQL pools allow querying and ingesting data from data lake files.

The benefits of this separation include cost-effectiveness, independent sizing, pause and resume capabilities, and seamless availability.

3. Semi-Structured Data Support:

Azure Synapse Analytics offers extensive support for semi-structured data, which includes data that doesn’t adhere strictly to traditional structured formats like rows and columns. Semi-structured data types encompass formats such as JSON, XML, Parquet, Avro, among others.

Here’s an overview of Azure Synapse Analytics’ support for semi-structured data:

• Integration with Various Data Sources: Azure Synapse Analytics seamlessly integrates with a wide range of data sources that generate semi-structured data. This includes data lakes, streaming data, IoT devices, and more, allowing for ingestion, processing, and analysis alongside structured data sources.
• PolyBase External Tables: With PolyBase, Azure Synapse Analytics enables the creation of external tables. These tables can query semi-structured data stored in Azure Data Lake Storage or Azure Blob Storage. PolyBase supports various file formats like Parquet, Avro, JSON, and delimited text files, enabling direct querying without the need for prior ETL processes.
• Support for OPENROWSET: Azure Synapse Analytics supports the OPENROWSET function, facilitating T-SQL queries directly on semi-structured data files. This feature provides flexibility in querying and processing semi-structured data within SQL Serverless Pools.
• Integration with Apache Spark: Azure Synapse Analytics integrates with Apache Spark, allowing for distributed processing of semi-structured data. This integration enables tasks such as data cleansing, transformation, and analysis on diverse data formats.

Deployment & Pricing

1. Deployment Model:

Azure Synapse Analytics offers two main deployment models:

• Integrated Analytics: This model combines data warehousing and big data analytics into a single service, enabling organizations to handle both structured and unstructured data. Integrated Analytics provides features such as data integration, data warehousing, big data analytics, and machine learning capabilities within a unified environment.
• On-demand (Serverless) SQL Pool: This model allows users to query data on an ad-hoc basis without requiring pre-provisioned resources. Users pay only for the data processed by their queries, making it a cost-effective option for sporadic or unpredictable workloads.

These deployment models provide flexibility for organizations to choose the approach that best fits their specific use cases, workload patterns, and budget considerations.

2. Fully Managed Service Options:

Azure Synapse Analytics is a fully-managed service hosted in the Microsoft Azure Cloud, so there’s no need to worry about patching or upgrading software. It’s scalable, allowing it to grow as your business does. Additionally, it integrates seamlessly with other Azure services to provide a comprehensive data platform solution.

3. Scalability:

Azure Synapse provides a flexible and cost-effective solution to handle large amounts of data, offering both manual and automatic scaling options to best suit your needs.

Here are some key points:

• Compute and Storage Separation: Azure Synapse separates compute from storage, allowing you to scale each independently. This means you can adjust compute power without affecting your data.
• Data Warehouse Units (DWUs): For dedicated SQL pools, the unit of scale is a DWU (Data Warehouse Unit). You can scale compute resources by increasing or decreasing DWUs.
• Serverless Scaling: For serverless SQL pools, scaling is done automatically to accommodate query resource requirements.
• Autoscale Feature: Apache Spark for Azure Synapse Analytics pools can automatically scale the number of nodes in a cluster instance up and down.
• Pause and Resume: You can pause compute capacity while leaving data intact, so you only pay for storage. Compute capacity can be resumed during operational hours.
• Scale Out: You can scale out compute for better performance, or scale back compute to save costs.

4. Pricing Model:

Azure Synapse Analytics offers several pricing models to accommodate different usage patterns:

1. Pay-as-you-go: This model is suitable for variable workloads. You pay for what you use, making it a flexible option for fluctuating data workloads.
2. Reserved capacity: If you have consistent usage, you can opt for reserved capacity to achieve cost savings.
3. Serverless: For on-demand query processing based on scanned data, the serverless model is ideal. It’s particularly useful for sporadic workloads.
4. Synapse Commit Units (SCUs): You can pre-purchase Azure Synapse Analytics Commit Units for any publicly available product under Azure Synapse except storage over the subsequent 12 months. Your Azure Synapse usage will draw from your pre-purchased SCUs at the individual product’s retail price until they are exhausted, or until the end of the 12-month period. Here are the purchase tiers and discounts for pre-purchase plan Synapse Commit Units:

Please note that actual pricing may vary depending on the type of agreement entered with Microsoft, date of purchase, and the currency exchange rate.

Managemant

1. Community/Support:

Azure Synapse has a robust community support system that includes several platforms. These platforms provide a wealth of resources and opportunities to connect with experts and other users, making it easier to find solutions and learn more about Azure Synapse Analytics.

1. Azure Community Support: This is a platform where you can ask questions, get answers, and connect with Microsoft engineers and Azure community experts.
2. Microsoft Community Hub: This hub hosts discussions about Azure Synapse Analytics, allowing you to connect with other users and experts.
3. Azure Synapse Analytics Community: This is a starting point for Synapse developers to find resources and collaborate with others in the community.
4. Stack Overflow: Azure Synapse has a presence on Stack Overflow, a popular platform for community responses to development questions.
5. Azure Support on Twitter: This is a platform connecting Azure customers to resources and support.
6. Support Ticket: If you encounter any issues, you can create a billing or tech support ticket.

Remember, the strength of community support often enhances the value of a product, and Azure Synapse is no exception. It’s always good to engage with these communities to stay updated and get the most out of Azure Synapse Analytics.

2. Documentation:

Azure Synapse Analytics has extensive documentation available to help users understand and make the most of its features. Here are the most useful sources of information:

1. Microsoft Learn: Microsoft Learn provides a comprehensive set of articles and tutorials on Azure Synapse Analytics. It covers everything from getting started guides to detailed explanations of specific features.
2. GitHub Documentation: Azure Synapse also has documentation available on GitHub. This includes step-by-step guides on how to use various features of Azure Synapse.

These resources provide a wealth of information and are a great starting point for anyone looking to understand Azure Synapse Analytics better. They cover a wide range of topics and provide detailed explanations, making it easier for users to get the information they need.

3. Ease of Management:

Azure Synapse Analytics is designed with a focus on ease of management. It offers a unified user experience that seamlessly integrates your analytical and data processing engines, data lakes, databases, tables, datasets, and reporting artifacts along with code and process orchestration. This provides a comprehensive and user-friendly interface for managing data analytics solutions. In terms of security management, Azure Synapse allows for the organization of users into groups with appropriate permissions profiling. This simplifies access control by using security groups that align with roles. The Integrate Hub within the Azure Synapse Analytics workspace is a key feature that aids in managing and creating data integration pipelines for data movement and transformation. For mission-critical workloads, users can easily optimize the performance of all queries with intelligent workload management, workload isolation, and limitless concurrency.

4. Learning curve:

Azure Synapse Analytics is a comprehensive and powerful tool, and like any such tool, there is a learning curve involved. However, Microsoft has provided numerous resources to help users climb this curve:

1. Microsoft Learn: Microsoft Learn provides a comprehensive set of articles and tutorials on Azure Synapse Analytics. It covers everything from getting started guides to detailed explanations of specific features.
2. Step-by-step Tutorials: There are step-by-step tutorials available that guide you through the major feature areas of Azure Synapse Analytics. These tutorials are the ideal starting point for someone who wants a guided tour through the key scenarios of Azure Synapse Analytics.
3. Introduction to Azure Synapse Analytics: This training module on Microsoft Learn provides an introduction to Azure Synapse Analytics, helping you understand the business problems that Azure Synapse Analytics addresses, describe core capabilities of Azure Synapse Analytics, and determine when to use Azure Synapse Analytics.

While the initial learning curve might seem steep, especially for those new to data analytics, the wealth of resources and community support available make the process manageable. As with any new technology, the more you use Azure Synapse Analytics, the more comfortable and proficient you will become.

5. SQL Support:

Azure Synapse supports Transact-SQL (T-SQL), which is the same query language used in SQL Server and Azure SQL Database.

Integration

1. Supported Data Sources:

Azure Synapse Analytics supports a wide range of data stores and formats via different activities such as Copy, Data Flow, Look up, Get Metadata, etc. Here are some categories of supported data stores:

• Azure Data Stores: Azure Blob Storage, Azure Cognitive Search Index, Azure Cosmos DB for NoSQL, Azure Cosmos DB for PostgreSQL, Azure Cosmos DB for MongoDB, Azure Data Explorer, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, Azure Database for MariaDB, Azure Database for MySQL, Azure Database for PostgreSQL, Azure Databricks Delta Lake, Azure Files, Azure SQL Database, Azure SQL Managed Instance, Azure Synapse Analytics, Azure Table Storage.
• Database: Amazon RDS for Oracle, Amazon RDS for SQL Server, Amazon Redshift, DB2, Drill, Google BigQuery, Greenplum, HBase, Hive, Apache Impala, Informix, MariaDB, Microsoft Access, MySQL, Netezza, Oracle, Phoenix, PostgreSQL, Presto (Preview), SAP Business Warehouse Open Hub, SAP Business Warehouse via MDX, SAP HANA, SAP Table, Snowflake, Spark, SQL Server, Sybase, Teradata, Vertica.
• NoSQL: Cassandra, Couchbase (Preview), MongoDB, MongoDB Atlas.
• File: Amazon S3, Amazon S3 Compatible Storage, File System, FTP, Google Cloud Storage, HDFS, Oracle Cloud Storage, SFTP2.
• Generic protocol: Generic HTTP, Generic OData, Generic ODBC, Generic REST.
• Microsoft Fabric: Microsoft Fabric Lakehouse, Microsoft Fabric Warehouse.
• Services and apps: Amazon Marketplace Web Service, Appfigures (Preview), Asana (Preview), Concur (Preview), data.world (Preview), Dataverse, Dynamics 365, Dynamics AX, Dynamics CRM, GitHub.

Please note that the types of data sources you can connect to depend on many factors such as model compatibility level, available data connectors, authentication type, and On-premises data gateway support.

2. Cloud Services Integration:

While Azure Synapse Analytics has some capability to interact with AWS and GCP services, its most comprehensive and seamless integrations are with Azure’s own suite of services. This feature enhances its effectiveness, especially within an Azure-centric cloud strategy, making it a powerful tool for data analytics and processing.

Microsoft Azure:

Azure Synapse Analytics is deeply integrated with Azure services, providing a unified experience for developing end-to-end analytics solutions.

• Power BI Integration: Azure Synapse Analytics has deep integration with Power BI, allowing you to visualize data and build dashboards in minutes.
• Azure Machine Learning Integration: Azure Synapse Analytics enables practitioners to easily apply machine learning models to all your intelligent apps without any data movement.
• Azure Data Explorer Integration: Azure Synapse Analytics integrates with Azure Data Explorer for log and time series analytics.
• Azure CosmosDB Integration: Azure Synapse Analytics integrates with Azure CosmosDB, allowing you to analyze NoSQL data.
• Data Integration: Azure Synapse Analytics contains the same Data Integration engine and experiences as Azure Data Factory, allowing you to create rich at-scale ETL pipelines without leaving Azure Synapse Analytics.
• Azure Synapse Link: Azure Synapse Link allows you to automatically move data from both operational databases and business applications without time-consuming extract, transform, and load (ETL) processes.

AWS Cloud:

Azure Synapse Analytics can interact with AWS services through various connectors. For instance, it can copy data from Amazon S3 using the Copy activity.

It’s important to note that while data can be moved between Azure and AWS, there isn’t a deep integration between Azure Synapse Analytics and AWS services like there is with Azure services.

GCP Cloud:

Similar to AWS, Azure Synapse Analytics can interact with Google Cloud Platform (GCP) services. For example, it can copy data from Google Cloud Storage.

While data can be moved between Azure and GCP, there isn’t a deep integration between Azure Synapse Analytics and GCP services like there is with Azure services.

3. SDK Support:

Azure Synapse Analytics provides SDK support for various programming languages to help developers interact with its services programmatically. Here are some of the key SDKs:

• Python SDK: Azure Synapse Analytics provides a Python SDK that allows developers to interact with and manage Azure Synapse resources. This SDK is particularly useful for data scientists and developers who prefer to work with Python.
• .NET SDK: Azure Synapse Analytics also provides a .NET SDK. This SDK is designed for .NET developers and allows them to manage Azure Synapse resources, and interact with the service. There are also specific libraries for working with Synapse Spark and Synapse Artifacts.

Please refer to the respective SDK documentation for more details on how to use these SDKs with Azure Synapse Analytics.

4. Supported Visualization Tools:

Azure Synapse Analytics supports a variety of visualization tools to help you gain insights from your data. Here are some key points:

• Synapse Notebook Chart Options: When using an Azure Synapse notebook, you can turn your tabular results view into a customized chart using chart options. You can visualize your data without having to write any code.
• Display Function: The display function allows you to turn SQL queries and Apache Spark dataframes and RDDs into rich data visualizations. The display function supports a wide range of chart types, including bar charts, scatter plots, line graphs, and more.
• Python and R Libraries: Azure Synapse Analytics provides access to popular open-source libraries for data visualization1.
• Integration with Power BI: Azure Synapse Analytics integrates with Power BI, a suite of business analytics tools that can connect to hundreds of data sources and can be used for ad hoc analysis.
• HTML Graphics: Azure Synapse Analytics notebooks support HTML graphics using the displayHTML function.
• Apache Spark: When using Apache Spark in Azure Synapse Analytics, there are various built-in options to help you visualize your data.

Performance

1. Insert operations

Azure Synapse Analytics is designed to handle large-scale data workloads, and its performance with respect to insert operations is optimized through several mechanisms:

• Transactions and Logging: Azure Synapse Analytics uses transactions during data modification. These transactions can be explicit or implicit. Single INSERT, UPDATE, and DELETE statements are all examples of implicit transactions. Changes to the SQL pool are tracked using transaction logs.
• Minimal vs Full Logging: Unlike fully logged operations, which use the transaction log to keep track of every row change, minimally logged operations keep track of extent allocations and meta-data changes only. Therefore, minimal logging involves logging only the information that is required to roll back the transaction after a failure, or for an explicit request (ROLLBACK TRAN). As much less information is tracked in the transaction log, a minimally logged operation performs better than a similarly sized fully logged operation.
• Group INSERT Statements into Batches: If you need to load thousands or millions of rows throughout the day, it’s likely that singleton INSERTS aren’t optimal. One way to solve this issue is to develop one process that writes to a file, and then another process to periodically load this file.
• Use PolyBase to Load and Export Data Quickly: PolyBase is the best choice when you are loading or exporting large volumes of data, or you need faster performance. PolyBase loads can be run using CTAS or INSERT INTO.

Microsoft regularly updates and enhances Azure Synapse Analytics with new features and performance improvements. These updates often include optimizations in query processing, security enhancements, and integration with other Azure services.

2. Updates operations

Azure Synapse Analytics offers robust capabilities for managing update operations efficiently within large-scale datasets. This is achieved through several key features:

• Transactional Logging: Azure Synapse Analytics employs transactional logging to record changes made during update operations. This logging mechanism ensures data consistency and durability by maintaining a record of all modifications. In case of failures or errors during updates, the system can use this log to recover and maintain data integrity.
• Optimized Data Handling: The platform utilizes optimized data handling mechanisms designed for processing large volumes of data efficiently. This includes techniques such as query optimization, parallel processing, and columnar storage formats, which collectively enhance the speed and scalability of update operations.
• MPP (Massively Parallel Processing) Architecture: Azure Synapse Analytics is built on a distributed, MPP architecture. This means that update operations can be parallelized and distributed across multiple compute nodes within the system. As a result, updates can be performed in a highly scalable manner, leveraging the combined processing power of the underlying infrastructure.
• Incremental Updates: Synapse Analytics supports incremental updates, allowing efficient modifications to datasets without the need to reload entire tables or datasets. This capability is particularly beneficial for scenarios where only specific portions of data require updating, minimizing computational overhead and reducing processing time.

3. Join operations

Azure Synapse Analytics is highly efficient in handling join operations, offering optimized performance through data distribution strategies, compatible joins, materialized views, and performance tuning options. Here are some important aspects:

• Data Distribution: In Azure Synapse Analytics, data is distributed across several distributions based on the distribution type (Hash, Round Robin, and Replicated). The type of join operation (Compatible Joins or Incompatible Joins) depends on the type of the joined table distribution type and location on the join (LEFT or RIGHT).
• Compatible Joins: These occur when two tables are joined on the same key and same data type. In this case, no data movements are required, which enhances query performance.
• Incompatible Joins: These occur when two tables are joined on different keys. In this case, additional data movements (e.g., BroadcastMoveOperation, ShuffleMoveOperation) are required, which can impact performance.
• Materialized Views: Materialized views in dedicated SQL pool provide a low maintenance method for complex analytical queries to get fast performance without any query change. They pre-compute, store, and maintain their data just like a table, reducing execution time for complex queries with JOINs and aggregate functions.
• Performance Tuning: Performance can be further optimized by setting up a cache for a better user experience, avoiding returning millions of records to a dashboard, and using scheduled refreshes to avoid parallel query executions that drain SQL serverless pool resources.

4. Aggregation queries

Azure Synapse Analytics is indeed well-suited for handling aggregation queries efficiently due to several key features and optimizations. Here’s why:

• Massively Parallel Processing (MPP) Architecture: Azure Synapse Analytics uses a distributed, MPP architecture. This means that queries can be processed in parallel across multiple nodes or compute resources. When you run an aggregation query, such as a GROUP BY or aggregation functions like SUM, COUNT, AVG, etc., the workload can be distributed across these nodes, enabling faster execution compared to traditional single-node databases.
• Columnstore Indexes: Synapse supports columnstore indexes, which are particularly efficient for analytics workloads involving aggregation. These indexes store and process data by columns rather than rows, making aggregations faster and more efficient.
• Query Performance Tuning: Azure Synapse provides tools for query performance tuning and optimization. This includes features like workload management, intelligent query processing, and performance monitoring. By leveraging these tools, you can further enhance the performance of your aggregation queries.
• Scalability and Elasticity: Azure Synapse offers scalability and elasticity, allowing you to scale compute resources up or down based on workload demands. This scalability ensures that even as your data grows or query complexity increases, you can maintain performance levels for aggregation queries.

2. Materialized View Support:

Azure Synapse Analytics supports Materialized Views, which are a powerful feature for improving the performance of complex analytical queries. Here are some key aspects of Materialized View support in Azure Synapse Analytics:

• Materialized Views vs Standard Views: Both are virtual tables created with SELECT expressions and presented to queries as logical tables. A standard view computes its data each time when the view is used, while a materialized view pre-computes, stores, and maintains its data in the dedicated SQL pool just like a table. This means that recomputation isn’t needed each time a materialized view is used, leading to faster performance.
• Benefits of Materialized Views: They reduce execution time for complex queries with JOINs and aggregate functions. The query optimizer in the dedicated SQL pool can automatically use deployed materialized views to improve query execution plans. This process is transparent to users, providing faster query performance and doesn’t require queries to make direct reference to the materialized views.
• Syntax: The syntax for creating a materialized view is CREATE MATERIALIZED VIEW AS SELECT. Only HASH and ROUND_ROBIN distributions are supported.
• Automatic Updates: Materialized views automatically get updated as data changes in the underlying tables. This offers simple maintenance operations.
• Execution Plan Automatching: A materialized view does not have to be referenced in the query for the optimizer to consider the view for substitution. This allows data engineers to implement materialized views as a mechanism for improving query response time, without having to change queries.
• Integration with Power BI: Power BI and Azure Synapse Analytics can determine and create the most optimal set of materialized views based on learning Power BI user queries and usage patterns.

3. Indexing:

Azure Synapse Analytics offers several types of indexes to optimize the performance of your data queries. Here are the key types of indexes and their uses:

• Clustered Columnstore Indexes: By default, Azure Synapse Analytics creates a clustered columnstore index when no index options are specified on a table. These indexes offer the highest level of data compression and the best overall query performance. They are usually the best choice for large tables. To create a clustered columnstore table, you can specify CLUSTERED COLUMNSTORE INDEX in the WITH clause, or leave the WITH clause off.
• Heap Tables: When you are temporarily landing data in Azure Synapse Analytics, using a heap table can make the overall process faster. This is because loads to heaps are faster than to index tables and in some cases the subsequent read can be done from cache. After data loading, you can create indexes in the table for faster query performance. To create a heap table, simply specify HEAP in the WITH clause.
• Ordered Clustered Columnstore Indexes: These indexes improve query performance by enabling efficient segment elimination, resulting in much faster performance by skipping large amounts of ordered data that don’t match the query predicate. When creating an ordered CCI, the dedicated SQL pool engine sorts the existing data in memory by the order key(s) before the index builder compresses them into index segments.
• Data Explorer Indexes: To complement existing SQL and Apache Spark analytics runtime engines, Data Explorer analytics runtime is optimized for efficient log analytics using powerful indexing technology to automatically index free-text and semi-structured data commonly found in the telemetry data.

In addition to these, Azure Synapse Analytics also supports Clustered Indexes and Nonclustered Indexes. The choice of index type depends on the specific requirements of your workload, the nature of your data, and the types of queries you need to support.

4. Streaming Ingestion:

Azure Synapse Analytics supports Streaming Ingestion, which is valuable for loading data with minimal delay between ingestion and query. Streaming ingestion is optimized for scenarios where very low latency, typically less than a second, is essential. It is designed to handle high volumes of data, particularly for operational processing across numerous tables, where each table receives a modest stream of data (a few records per second), but the overall ingestion rate is high (thousands of records per second).

There are two main types of streaming ingestion supported in Azure Synapse Analytics:

• Event Hub or IoT Hub: These hubs can be configured as table streaming data sources.
• Custom Ingestion: Custom ingestion necessitates the development of an application using one of the Azure Synapse Data Explorer client libraries.

Furthermore, Azure Stream Analytics facilitates real-time data processing, allowing you to ingest streaming event data into Azure Synapse Analytics for further analysis and reporting.

Strengths

1. Unified Data Platform: Azure Synapse Analytics integrates seamlessly with various Azure data storage options, supporting structured, semi-structured, and unstructured data in its native format.
2. Compute and Storage Separation: The architecture allows for independent scaling of compute and storage resources, enhancing flexibility and cost-effectiveness.
3. Semi-Structured Data Support: Extensive support for semi-structured data types like JSON, XML, Parquet, and Avro, enabling integration with diverse data sources.
4. Integration with Azure Services: Deep integration with other Azure services like Azure Data Explorer, Power BI, and Azure Machine Learning, providing a comprehensive analytics ecosystem.
5. Scalability: Offers both manual and automatic scaling options, including serverless SQL pools for on-demand query processing.

Weaknesses

1. Complexity for Beginners: Azure Synapse Analytics has a learning curve, especially for those new to data analytics and cloud platforms.
2. Limited Integration with External Cloud Platforms: While it supports interactions with AWS and GCP services, the integration is not as deep as with Azure services.
3. Cost Considerations: Pricing can be complex due to various deployment and pricing models, requiring careful planning to optimize costs.

Best use case

Azure Synapse Analytics is ideally suited for organizations seeking a unified platform to manage large-scale data warehousing, big data analytics, and machine learning tasks within the Azure ecosystem. It shines in scenarios requiring real-time data processing and low-latency streaming ingestion, making it a strong choice for IoT data analytics, continuous monitoring, and operational intelligence. Additionally, Azure Synapse Analytics is beneficial for businesses with fluctuating workloads, offering flexibility through serverless SQL pools for cost-effective ad-hoc querying and dedicated resources for more predictable analytics needs.

Worst use case

While powerful, Azure Synapse Analytics may not be the most optimal solution for smaller-scale or straightforward analytics projects with limited data volumes and scalability requirements. Organizations strictly constrained by budget considerations may find its pricing structure complex, particularly in scenarios where data usage patterns are unpredictable. Additionally, businesses heavily invested in non-Azure cloud environments may face challenges with integration and may not leverage its full potential without deep Azure ecosystem integration.

In summary, Azure Synapse Analytics offers a comprehensive and adaptable solution for overseeing large-scale data analytics and warehousing operations within the Azure environment. Its key strengths include a unified data platform, strong support for semi-structured data, seamless integration with Azure services, and flexible scalability options.

For new users, the learning curve associated with Azure Synapse Analytics can be significant, particularly for those unfamiliar with Azure’s broader ecosystem or complex data analytics tools. The platform’s advanced capabilities require a solid understanding of data warehousing concepts, SQL, and cloud computing principles. Training and onboarding efforts may be necessary to fully leverage its functionalities.

Organizations embarking on smaller or less complex analytics initiatives may find that Azure Synapse Analytics offers more power and capability than needed, leading to potential over-provisioning and increased costs. In such cases, exploring alternative Azure services tailored to specific analytics needs or opting for more streamlined data warehousing solutions could be more cost-effective and efficient.

Despite these considerations, Azure Synapse Analytics remains a robust choice for enterprises seeking a unified, scalable, and integrated platform for managing large-scale data analytics and warehousing operations within the Azure ecosystem. As organizations grow and their data needs expand, the benefits of Synapse Analytics in terms of performance, integration, and scalability are likely to outweigh the initial learning curve and cost concerns.

Always check the official documentation for the latest information on Azure Synapse Analytics.

For a more comprehensive understanding of how to assess this information, please refer to the key metrics outlined in the article How to Choose the Right OLAP Storage when making the crucial decision for your OLAP storage needs. To enhance your proficiency in data management, explore the Strategic guide on mastering data for software developers.