Organizations can quickly generate insights in this dynamic digital economy if the data management systems promote efficiency and holistic data architecture. The evolution towards self-service insights, real-time analytics, and the in-flexible data management systems drives the change to modern cloud data warehouse solutions. As per Saxon data experts’ interactions with many industry leaders, the major challenges inhibiting the success of data warehousing are:
- About 85% of Data teams face challenges in loading data into data warehouses – the main reasons are complex data types and formats, data silos, legacy technology.
- Around half of business applications operate in silos. Many CDOs say that the data silos hold back rapid insights.
- Most of the IT leaders believe that data collection, storage, and management processes need to be unified and transformed for rapid decision making
Migration to cloud data warehouse can give the advantage of:
- Investment in hardware systems and resources
- Massive parallel processing to improve speed for complex queries
- Cost-efficient and they can be set up very easily
The most popular cloud data warehouses in the market are – Azure Synapse Analytics, Amazon Redshift, Google BigQuery, IBM Db2 Warehouse, Snowflake, Oracle, and Yellowbrick. Let us look at the most critical success factors of Snowflake and Azure Synapse in this blog.
Snowflake – A brief Overview
Snowflake is a flexible data warehouse-as-a-service built on top of any public cloud. Snowflake’s architecture offers three independent, scalable layers for big data – storage, compute, and services. All the aspects of data storage are also automatically managed, including structured and unstructured data.
Some differentiators of Snowflake:
- Scaling the virtual warehouse is simple and it is easy for users to run high volume queries in seconds.
- A multi-cluster architecture to address concurrency issues.
- Organizations can share the data to anyone without hassles, whether or not they are Snowflake customers.
Do you think you need a partner like Saxon for Snowflake implementation? Let us look at the benefits of a partner approach:
- Resolve your unique data challenges and offer solutions for all the possible use cases.
- Implement, integrate, and manage Snowflake’s architecture in a reliable and cost-effective approach.
- Harness the true value of your data innovations with a customized cloud migration approach and Snowflake data asset migration priorities.
Azure Synapse – A brief Overview
Azure Synapse is a part of a consolidated cloud platform that spans on-premise and cloud and includes all infrastructure, security, data governance, and other data management integrations. It brings together the storage, processing, and analysis of big data. It also facilitates integration with Azure ML and Power BI for your immediate insights and single workload for all your business intelligence needs.
The main advantages of Azure Synapse:
- Azure Active Directory and Azure Purview for enhanced data security and integrated governance.
- A primary data repository for all data forms in Azure Data Lake.
- Azure Synapse analytics is 14 times faster and costs 94% less than the similar cloud solutions.
- Azure Synapse connects to data lakes in seconds, installs and runs in minutes.
- Users can integrate GitHub in easy steps and the Azure DevOps for CI/CD
The benefits of moving to Azure Synapse with data consulting services partner like Saxon:
- Maximize the RoI of Azure Synapse data warehouse transformation.
- End-to-end data and analytics expertise leveraged for faster time to insights and unified self-service analytics.
- Data management and algorithms in every business process to enable agility and competitive differentiation.
Snowflake vs. Azure Synapse – A few Critical Differences
Snowflake vs. Azure Synapse – As a modern cloud data warehouse solution, both offer unparalleled security, scalability and flexibility. But we outlined a few critical differences for you to decide between Snowflake vs Azure Synapse.
SaaS vs. PaaS
Snowflake is a multi-cloud SaaS solution built on Microsoft Azure, AWS, and Google Cloud storage options. Technically speaking, the data warehouse resides in the Snowflake cloud instance independent of the distributed public cloud. Snowflake also maintains a collection of VM instances for compute and ANSI compliant SnowSQL.
Azure Synapse is a PaaS solution with a free Azure Synapse workspace development environment and integrates numerous Azure resources. Azure ML, Azure Data Factory, Azure AD, Azure Purview, and Power BI comes tightly coupled with Azure Synapse for agile insights and faster processing.
Compute Resources and Cost
Snowflake vs Azure Synapse – Both use the underlying SQL databases designed for cloud data warehouse needs, but the approach to compute resources is different.
Snowflake decouples compute resources from SQL databases. Any compute resource (warehouses) in Snowflake can be run concurrently to use the same SQL database. Users can load the data and query the data simultaneously without any performance concerns. Another key feature of Snowflake is that the
compute resources stop after a certain period of inactivity. You can reduce your spending in processing when someone forgets to turn off a significant computing resource.
There is a different computing process in Azure Synapse, unlike Snowflake. A dedicated SQL pool is leveraged with SQL database for cloud data warehousing. Multiple SQL pools can leverage the same SQL database at the same time. SQL commands are distributed over the compute nodes based on the required SQL pool performance level. There is no automatic pause feature, but you can manually pause the dedicated SQL pool or with an API operation.
Snowflake operates on pay-as-you-go model, and the computing cost is calculated on the usage per second. Azure Synapse, on the other hand, is billed on hourly basis.
Management of Source Control
Azure Synapse and Azure Data Factory are fully integrated with code version control to Azure DevOps and Github. Users can configure a git repository in easy steps for Synapse workspace, SQL scripts, and ADF pipelines remain tightly integrated with the workspace. The native approach is simple for users to opt out from any other source control system.
Snowflake does not seem to have a built-in source control connector; some third-party systems leverage Snowflake API events to sync changes.
Data Integration Tools
Snowflake relies heavily on third-party tools for data integration; they provide APIs and drivers for their data integration partners’ networks. Sometimes you can find a better solution based on your use case/challenge with these partners.
Azure Synapse leverages Azure Data Factory, the best-in-class data integration and transformation tool; Databricks and other Azure resources also add an advantage.
A few more
Snowflake – The Cons
- Snowflake is highly scalable, flexible and the compute cost is as per the usage. It is often possible that users can exceed their service and realize it only in the billing. Small businesses find Snowflake expensive for their use.
- For continuous loading of data, Snowflake users can use Snowpipe, but it seems challenging for many use cases.
Azure Synapse – The Cons
- Azure Synapse does not support XML data types and parsing.
- Looks easy to learn upfront, but it is not so; significant learning needed.
- A dedicated SQL pool does not support the table features like computed columns, indexed view, sequence, sparse columns, surrogate keys, synonyms, triggers, unique indexes, and user-defined types.
- It poses challenges with cross-database queries.
Before you select the best cloud data warehouse, you must consider the use cases, existing cloud vendors, and the goals for data management. Azure Synapse or Snowflake – a trusted partner, can accelerate your time to value and RoI.
Grab the opportunity! Talk to our experts to know more about the best cloud data warehouse solution for your needs.