Why look beyond Azure Synapse Analytics

Azure Synapse Analytics provides a comprehensive platform for data warehousing and big data analytics, integrating various components like SQL pools, Spark pools, and Data Explorer pools within a single workspace (Microsoft Docs). Its strength lies in deep integration with the broader Azure ecosystem, offering a unified experience for users already committed to Microsoft's cloud services. This integration simplifies data governance, security, and identity management when operating entirely within Azure.

However, organizations may seek alternatives for several reasons. For multi-cloud strategies, a vendor-agnostic solution might be preferred to avoid vendor lock-in and distribute workloads across different cloud providers. Performance characteristics and cost models can also vary significantly between platforms; while Synapse Analytics offers a pay-as-you-go model, specific workloads might find more cost-effective or performant solutions elsewhere depending on their scale and usage patterns (Azure Pricing). Additionally, some teams may prefer platforms with different architectural approaches, such as those with highly decoupled compute and storage, or specialized features for specific types of analytics that are not core to Synapse's offerings.

Top alternatives ranked

  1. 1. Snowflake — The Data Cloud platform with decoupled storage and compute

    Snowflake is a cloud-native data warehousing solution known for its architecture that decouples storage and compute, allowing independent scaling of resources. This design enables users to pay only for the compute resources consumed and the data stored, without needing to provision or manage underlying infrastructure (Snowflake Official Site). Snowflake supports a wide range of data workloads, including data warehousing, data lakes, data engineering, data science, and secure data sharing. It offers a SQL interface and is compatible with various BI and data science tools, making it accessible to a broad user base.

    Best for: Organizations requiring a highly scalable, flexible, and performant data platform across multiple clouds, emphasizing ease of use and minimal administration.

    See our in-depth Snowflake profile for more details.

  2. 2. Google BigQuery — Serverless and highly scalable analytics data warehouse

    Google BigQuery is a fully managed, serverless enterprise data warehouse designed for large-scale data analytics (Google Cloud BigQuery). It enables super-fast SQL queries using the processing power of Google's infrastructure. BigQuery's serverless architecture means there is no infrastructure to manage, and it automatically scales to meet demand, simplifying operations. It offers strong integration with other Google Cloud services, including machine learning capabilities through BigQuery ML and real-time data streaming. BigQuery is particularly well-suited for interactive analysis of massive datasets and real-time operational analytics.

    Best for: Enterprises seeking a fully managed, serverless data warehouse for large-scale analytics, real-time data processing, and deep integration with Google Cloud's AI/ML ecosystem.

    See our in-depth Google BigQuery profile for more details.

  3. 3. Amazon Redshift — Petabyte-scale cloud data warehouse

    Amazon Redshift is a fully managed, petabyte-scale data warehousing service that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools (Amazon Redshift). Redshift is built on columnar storage technology, which optimizes query performance for analytical workloads. It offers various node types and scaling options, including RA3 instances with managed storage, which separate compute and storage for independent scaling. Redshift integrates with a wide array of AWS services, including S3 for data lakes and various analytics and machine learning tools.

    Best for: AWS-centric organizations requiring a powerful, scalable data warehouse for complex analytical queries, deep integration with the AWS ecosystem, and fine-grained control over infrastructure.

    See our in-depth Amazon Redshift profile for more details.

  4. 4. CockroachDB — Distributed SQL database for global applications

    CockroachDB is a distributed SQL database built on a transactional key-value store, designed for global deployments and high availability (Cockroach Labs). Unlike traditional data warehouses, CockroachDB is primarily an operational database that supports OLTP (Online Transaction Processing) workloads. It offers strong consistency, ACID transactions, and horizontal scalability, making it suitable for applications that require a resilient and globally distributed data layer. While not a direct data warehousing competitor, it can serve as a transactional backend for real-time analytics on operational data, especially for applications needing multi-region data distribution and survivability.

    Best for: Applications requiring a globally distributed, highly available, and strongly consistent SQL database for transactional workloads and operational analytics, rather than traditional batch data warehousing.

    See our in-depth CockroachDB profile for more details.

  5. 5. Vitess — Database clustering system for horizontal scaling of MySQL

    Vitess is a database clustering system for horizontally scaling MySQL (Vitess Official Site). It combines the scalability of a NoSQL database with the relational database features of MySQL. Vitess can shard MySQL databases to handle large traffic loads and data volumes, making it a solution for applications that have outgrown a single MySQL instance. While not a data warehouse, Vitess is relevant for organizations with significant MySQL investments that need to scale their operational data layer for high-throughput transactional analytics or to feed real-time dashboards from sharded OLTP systems. It provides features like connection pooling, query rewriting, and sharding to manage massive datasets and high query rates.

    Best for: Organizations looking to scale MySQL for high-traffic applications and large datasets, offering sharding and clustering capabilities for operational data, rather than a dedicated analytical data warehouse.

    See our in-depth Vitess profile for more details.

  6. 6. RethinkDB — Open-source, real-time data stream database

    RethinkDB is an open-source, NoSQL database designed to store JSON documents and push real-time updates to applications (RethinkDB Official Site). It specializes in real-time data streaming, making it suitable for applications that require live updates, such as chat applications, real-time dashboards, or collaborative tools. While not a traditional data warehouse like Synapse Analytics, RethinkDB can be used for operational analytics where real-time insights are paramount, particularly for streaming data pipelines that feed directly into applications. Its changefeeds feature allows applications to subscribe to data changes and receive updates instantly.

    Best for: Real-time applications and operational analytics requiring immediate data updates and push notifications, rather than complex analytical queries on historical datasets.

    See our in-depth RethinkDB profile for more details.

  7. 7. OpenStack — Open-source cloud computing platform

    OpenStack is a set of open-source software tools for building and managing cloud computing platforms for public and private clouds (OpenStack Docs). It provides infrastructure-as-a-service (IaaS) components, including compute, networking, and storage. While OpenStack itself is not a data analytics platform, it provides the underlying infrastructure upon which data warehousing and big data solutions can be built. Organizations can deploy open-source data analytics tools like Apache Spark, Hadoop, or even custom data warehouses on an OpenStack cloud. This provides maximum control and customization but requires significant operational overhead compared to managed cloud services.

    Best for: Organizations seeking to build and manage their own private cloud infrastructure for data analytics, requiring full control over the environment and the ability to customize components, at the cost of increased operational complexity.

    See our in-depth OpenStack profile for more details.

Side-by-side

Feature Azure Synapse Analytics Snowflake Google BigQuery Amazon Redshift CockroachDB Vitess RethinkDB OpenStack
Category Unified Analytics Platform, Data Warehouse Cloud Data Platform, Data Warehouse Serverless Data Warehouse Cloud Data Warehouse Distributed SQL Database (OLTP) MySQL Sharding System (OLTP) NoSQL Real-time Database IaaS Cloud Platform
Primary Use Case Large-scale data warehousing, big data processing, enterprise data integration Data warehousing, data lakes, data engineering, data science, secure data sharing Serverless analytics, real-time operational analytics, interactive query of large datasets Petabyte-scale data warehousing, complex analytical queries Globally distributed transactional applications, high availability Horizontal scaling of MySQL for high-traffic applications Real-time applications, live updates, streaming data Building private clouds, infrastructure for custom data solutions
Architecture Unified workspace with SQL, Spark, Data Explorer pools Decoupled storage and compute, multi-cloud Serverless, columnar storage, MPP Columnar storage, MPP, managed storage (RA3) Distributed SQL, transactional key-value store Proxy layer for MySQL sharding Distributed document store, push changefeeds Modular IaaS components (compute, storage, network)
Management Managed service by Microsoft Fully managed service Fully managed, serverless Fully managed service Managed service available, self-hosted option Self-hosted, community-driven Self-hosted, community-driven Self-managed infrastructure
Scalability Independent scaling of SQL, Spark, Data Explorer pools Independent scaling of compute and storage, near-unlimited Automatic, serverless scaling Scales compute and storage, RA3 instances decouple Horizontal, multi-region Horizontal sharding of MySQL Horizontal scaling Scales based on deployed hardware
Pricing Model Pay-as-you-go (compute, storage, data processed) Usage-based (compute, storage, data transfer) Usage-based (query, storage, data transfer) On-demand, reserved instances (compute, storage) Consumption-based, node-based Infrastructure costs + operational overhead Infrastructure costs + operational overhead Hardware + operational costs
Compliance SOC 2 Type II, GDPR, ISO 27001, HIPAA, PCI DSS SOC 2 Type II, GDPR, HIPAA, PCI DSS, FedRAMP, ISO 27001 SOC 1/2/3, GDPR, HIPAA, ISO 27001, PCI DSS, FedRAMP SOC 1/2/3, GDPR, HIPAA, ISO 27001, PCI DSS, FedRAMP SOC 2, HIPAA, GDPR N/A (user's responsibility) N/A (user's responsibility) N/A (user's responsibility)
Ecosystem Integration Deep with Azure services Multi-cloud, broad third-party integrations Deep with Google Cloud services Deep with AWS services Standard SQL drivers, ORMs MySQL ecosystem JSON-aware applications Open-source tools, custom deployments

How to pick

Choosing an analytics platform involves evaluating your specific requirements against the strengths and weaknesses of available solutions. Here's a decision-tree approach to guide your selection:

  1. Cloud Strategy:

    • If you are heavily invested in the Azure ecosystem and prefer a unified experience within a single vendor, Azure Synapse Analytics is a strong contender due to its deep integration and comprehensive feature set (Microsoft Docs).
    • If your organization operates in a multi-cloud environment or prefers vendor neutrality, Snowflake offers a platform that runs across major cloud providers, providing flexibility and avoiding lock-in (Snowflake Official Site).
    • If you are primarily an AWS user, Amazon Redshift provides tight integration with other AWS services and is optimized for data warehousing within that ecosystem (Amazon Redshift).
    • If you are primarily a Google Cloud user, Google BigQuery offers a serverless, highly scalable solution with strong integration into the Google Cloud ecosystem (Google Cloud BigQuery).
    • If you require building your own cloud infrastructure for maximum control, OpenStack provides the IaaS foundation, but demands significant operational expertise (OpenStack Docs).

  2. Workload Type:

    • For traditional batch data warehousing, complex analytical queries, and business intelligence, dedicated data warehouses like Azure Synapse Analytics, Snowflake, Google BigQuery, or Amazon Redshift are ideal.
    • For real-time operational analytics, applications requiring immediate data updates, or streaming data processing, RethinkDB's real-time capabilities or CockroachDB's distributed OLTP properties might be more suitable.
    • For scaling existing MySQL transactional databases to handle high traffic and large datasets, Vitess is a specialized solution for horizontal sharding.

  3. Scalability and Performance:

    • If you need highly elastic and independent scaling of compute and storage, Snowflake's architecture is designed for this.
    • For serverless auto-scaling that handles fluctuating workloads without manual intervention, Google BigQuery excels.
    • If you require fine-grained control over compute clusters for performance tuning, Amazon Redshift offers various node types and configuration options.

  4. Cost Model:

    • Evaluate the pricing models based on your expected data volume, query complexity, and compute usage. Consumption-based models (like BigQuery, Snowflake, Synapse) can be cost-effective for variable workloads, while provisioned models (like Redshift for certain configurations) might be better for consistent, predictable usage.

  5. Operational Overhead:

    • For minimal operational burden and fully managed services, Snowflake, Google BigQuery, and Azure Synapse Analytics are designed to reduce administrative tasks.
    • For self-managed solutions like Vitess, RethinkDB, or an OpenStack-based deployment, be prepared for increased operational complexity and the need for specialized in-house expertise.

  6. Data Governance and Security:

    • All major cloud data platforms offer robust security and compliance features. Ensure the chosen alternative meets your industry-specific compliance requirements (e.g., HIPAA, GDPR, PCI DSS).