snapblocs vs. Cloud Provider Platforms

snapblocs vs. Cloud Provider Platforms

Compared to cloud provider solutions, snapblocs offers some key strategic benefits:
  1. Architecture as a Service: Architecture Blueprints that combine vendor building blocks into a complete, production-ready platform solution
  2. Multi-cloud support, portability & solution abstraction
  3. 3rd party open source technology integration
  4. Inter-cloud enablement by sharing data among multiple cloud providers
Cloud providers
Several cloud providers enable building cloud-based data platforms. For example, AWS, Azure, or GCP each provide native ways to implement data lakes, data pipelines, data warehouses, machine learning platforms, and end-to-end analytical data platforms as well as other solutions.

3rd party open source technology
Open source is great, but for mission-critical applications, it can prove challenging to manage and support in a standardized way, at scale. Installing each open source component on Kubernetes is time-consuming and error-prone due to a lack of central visibility, inconsistent security practices, and complex management processes.

To that end, snapblocs combines the power of SaaS with best of breed open source products delivered via Architecture as a Service in a single, unified platform. snapblocs significantly reduces the time required to install, integrate and check compatibility for individual components.

For example, installing and configuring Elastic ELK (Stack) on Kubernetes and limiting HTTPS connections for Kibana with basic authentication is not available out of the box. It requires researching it from within the open community forums and a lot of work to make them secure. Making Elasticsearch connect securely to other components on Kubernetes is also not an easy task. However, snapblocs can deliver best practice solutions via hardened integrated platform blueprints.

The data lake use case
AWS Lake Formation is a service for creating a data lake platform. It uses AWS-specific services such as Amazon Redshift, Amazon Athena, Amazon EMR for Apache Spark, and AWS Glue. Azure Data Lake and Google Data Lake use their respective native services to build data lake platforms. 

Each cloud-native solution has specific ways to build data platforms that are not portable to other cloud providers. Popular 3rd Party open-source tools used in the Cloud provider environments still require a lot of time and effort to be used successfully.

Portability and multi-cloud
Many businesses use multi-clouds to meet various business goals or find themselves in that situation by default. Long-term strategic options include consolidating into one cloud vendor or embracing multi-cloud. The latter strategy includes greater flexibility, reduced costs, avoiding vendor lock-in, and tapping into specific regional cloud providers. 

Currently, supporting a multi-cloud strategy requires learning each cloud provider's native solutions and implementing unique ways to monitor, operate and maintain business applications on each cloud provider's infrastructure.

Inter-cloud enablement
  • Multi-cloud is where a service or product runs on the infrastructure of more than one Cloud service provider.
  • Inter-cloud is where data integrates or transfers between cloud service providers as part of logical application deployment. Inter-cloud would ensure that infrastructure on one Cloud can use data beyond its reach by taking advantage of synchronized local data replicated from other cloud providers. 
As many businesses adopt multi-cloud, they have segmented data on each cloud that is hard to access in real-time reliably due to the remote cloud network connections. To overcome the data access limitations, they need to replicate the required data among infrastructures on multiple clouds.

The snapblocs Data Flow platform easily allows replication or synchronization of data among multi-cloud infrastructures. It enables a logical deployment of snapblocs Architecture Blueprints deployable in more than one Cloud. Exchanging data between cloud environments is easy and consistent. Lifecycle management is also more straightforward and more consistent. The Data Flow Architecture Blueprint uses Kafka MirrorMaker to reliably copy data from a source Kafka cluster to a destination Kafka cluster in near real-time.

For example, the snapblocs stack of Data Flow running on Google GCK can capture any changes on MySQL (either using StreamSets DC or Debezium) and relay Postgres or other changes destinations through the stack running on AWS. Since stacks on each multi-cloud derive from the same snapblocs Architecture Blueprint, it is easier to manage business applications and data through the snapblocs. 

snapblocs Architecture Blueprints
To avoid being locked into a cloud provider, snapblocs provides a catalog of pre-built Architecture Blueprints using popular open-source components. Architecture Blueprints are neutral to cloud providers. Even though Architecture Blueprints use each cloud provider's Kubernetes native services, snapblocs abstracts them; EKS on AWS, AKS on Azure, GKE on Google GCP. Therefore, provisioning and management of data platform stacks become essentially identical among each of the cloud providers. 

Architecture Blueprints use open source software components instead of cloud provider's native services (except cloud services managed by Kubernetes such as computing node, storage, etc.). Each data platform stack, built from Architecture Blueprints, is almost identical no matter what cloud provider is used to deploy the stack. Consistency substantially reduces resource effort and lowers the cost of developing and managing supporting stacks in multi-cloud environments.
In summary
Architecture Blueprints allow rapid deployment of proven, complete solution architectures ready for creating business value. snapblocs reduces the time to value and leverages the best cloud provider platforms and 3rd party open source solutions.
By decoupling apps from the underlying cloud-native infrastructure, data platforms built with snapblocs are flexible enough to run services across multiple clouds, on-premises, and even edge locations.


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