What Are the Differences in Cloud Data Warehouses: From Azure Synapse to Databricks

Data collected from the internet continues to increase exponentially annually. So naturally, this data needs to be stored somewhere – that is where cloud data warehouses come in. To accommodate and take advantage of all the data, big players like Amazon, Google, and Microsoft have built warehouses with unique features, making them different from each other.

The purpose of data warehouses

The various data warehouses hold and manage data so platforms can analyze them. The technology in cloud data warehouses is what most organizations use to handle and analyze their data.

These warehouses centralize the data from several choices using MPP architecture to store and process data using several servers. Despite similar tasks, the warehouses do things differently.

The top four data storage solutions have vital operational areas. The companies continue to add features as they see their needs shift into other avenues. However, like the floppy disk of years gone by, the current cloud storage data systems could become obsolete if developers create a more efficient and cost-effective solution.

Manage data

Cloud data warehouses have the technology to store and compute data. Many organizations rely on Striim Cloud data streaming and integration. Organizations that rely on cloud data warehouses pay less for storage than for computing, and the different warehouses assess fees differently.

For example, Synapse, Snowflake, and BigQuery charge different fees for storing and computing for businesses to use when scaling. Organizations can use columnar storage to access data somewhere other than the cluster and to scale data independently.

Redshift uses local storage in columns without separating storage and computing fees. The warehouse uses the theory that storage and computing go hand-in-hand. Redshift understands that users need to access local columns to get data quickly and process it even faster. Unfortunately, the processing speed reduces the ability to scale storage independently.

Stopping and starting the data clusters

Organizations like to stop and start their data clusters because they don’t all need to run 100 percent of the time. Redshift, Synapse, and Snowflake charge fees while queries run. Customers save money by pausing or stopping the clusters when they don’t need them. Being able to pay for one cluster for computing helps businesses save money.

Businesses appreciate being able to pay for storage separately, then pay for computing when needed. Snowflake has automatic suspend and resume features. The warehouses automatically suspend when a cluster isn’t being used for queries or computing. Once a query begins, the charges continue.

Synapse offers similar features, but users have to access Azure Functions to automate the suspend and resume features. Snowflake stops and starts automatically in a dynamic way, but Synapse does not.

Customers who use Redshift do not get to stop and start their clusters. However, users can work around the missing feature by deleting the cluster after capturing a snapshot, then restoring it when finished. Users will need to learn coding to access the stop and start features, again without the dynamism in Snowflake.

Since BigQuery doesn’t use servers, customers do not need to pause their clusters. The data warehouse charges fees when customers execute a query. They do not charge customers if they aren’t computing.

Storage and computing models

Cloud data warehouses rely on two models for querying and computing: resource provisioning and serverless. In the resource provisioning model, businesses use a node cluster based on what they need for computing. In this model, customers pay for what they use and can scale in and out as needed.

Customers who use the serverless model expect the cloud provider to take care of operational responsibilities.

The customer pays for the queries and the data used. Redshift and Synapse use clusters of nodes as the infrastructure in the data warehouses. In Amazon Redshift, the leader node runs the compute nodes, while Synapse uses a control node to do the same job. These controlling nodes distribute the queries to compute nodes for processing and analysis.

Despite using standard storage methods, Redshift and Synapse use similar methods for queries and computing.

Snowflake runs like Redfish and Synapse but uses a virtual warehouse for computing. These independent warehouses use several computing clusters and resources to meet customer needs. BigQuery is serverless, so it does not use clusters or resources. Instead, BigQuery helps with scalability and availability for queries and computing.

Azure Synapse is new to the serverless storage model. Redshift also offers a serverless storage option called RedShift Spectrum that lets customers access data for queries.

Wrap up

Cloud data warehouses provide more than storage solutions. The key to success is offering valuable features that integrate with the platforms businesses use today. The warehouses use machine learning and artificial intelligence to serve customers better and give them the tools they need for real-time analytics.

 

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