Data platform design patterns with best-practice | snapblocs

Proven Architecture Blueprints

    snapblocs Architecture Blueprints provide proven architecture that delivers common Data Platform use cases, making it simple to provision and manage Data Platform stacks. snapblocs Architecture Blueprints consist of open source components and proven design patterns based on common real-world implementations. They are designed based on standard and widely accepted best-practice industrial approaches.

    For example, each snapblocs Architecture Blueprint includes dashboards for observability (metrics, logs, APM) and alerts to analyze running stacks and gain actionable insights quickly and securely. snapblocs follows the cloud provider recommendations.

    For example, AWS EKS requires separate public and private Subnets for securing Kubernetes worker nodes. snapblocs identifies private Subnets among the Subnet list provided for running Kubernetes worker nodes in private Subnets, making it harder for an attacker to reach them. snapblocs automatically creates the load balancer and loads public-facing services onto public Subnets.

    Following are the Architecture Blueprints provided, but not limited to:

    1. Kubernetes+ Platform provides the managed service for Kubernetes on a Cloud platform (AWS EKS, Google Cloud GKE,  Azure AKS, etc.). Use the snapblocs UI to create a Kubernetes stack by configuring Cloud platform and Kubernetes options such as VPC, Subnets, Kubernetes version, and cluster capacity.

    2. Microservice+ Platform provides complete lifecycle management of a microservice on the Kubernetes+ Platform. Use the snapblocs UI to provision/pause/resume/monitor/teardown the microservice on the managed Kubernetes platform.

    3. Kafka+ Platform provides the Managed Event Streaming Platform using Confluent Kafka on Kubernetes to move data from various input data sources to target data destinations in the stream. Use the snapblocs UI to create a Managed Event Streaming Kafka stack by configuring the data flow options, such as input source and target destination settings, as well as operational configuration for target runtime environment such as Kubernetes on a Cloud platform (AWS EKS, Google Cloud GKE,  Azure AKS, etc.)

    4. Elastic Stack+ Platform provides the ability to aggregate logs from all systems and applications, analyze these logs, and create visualizations for application and infrastructure monitoring, faster troubleshooting, and security analytics on the Kubernetes platform.

    5. StreamSets Data Collector+ Platform provides data ingestion pipelines integrated with ETL processing for streaming, batch, and change data capture (CDC). Use the snapblocs UI to create a data ingestion pipeline stack by configuring the data ingestion options, such as input source and target destination settings, as well as operational configuration for target runtime environment, such as Kubernetes on a Cloud platform (AWS EKS, Google Cloud GKE,  Azure AKS, etc.)

    6. Data Flow Platform provides the managed service using Kubernetes to move data from various input data sources to target data destinations in a stream or bulk mode. Use the snapblocs UI to create a Data Flow stack by configuring the data flow options, such as input source and target destination settings, and operational configuration for target runtime environment such as Kubernetes on a Cloud platform.

    7. Data as a Service Platform provides a managed service using Kubernetes to simplify access, accelerate analytical processing, secure and mask data, curate datasets, and provide a unified catalog of data across all data sources. Many consumers, such as BI tools, data science platforms, and dashboard tools, can now consume all the data as if it's a single, high performant data source through ODBC and JDBC no matter the data structure.

    8. Data Lake Platform (coming soon) provides a managed service using Kubernetes for delivering a set of integrated solutions (Data Flow, Data Transformation, DaaS, Data Lake). It eliminates extensive data modeling and ETLs usually required by the schema-on-write data warehouse solutions. Instead, the Data Lake Platform uses the schema-on-read solutions enabling fast data ingestion and consumption for both structured and non-structured data, which increases the time-to-value for meeting fast-changing business requirements. It also provides Metadata (Catalog) Management to make data visible and easily accessible to consumers.

    9. Data Transformation Platform (coming soon) provides a managed service using Kubernetes for converting data from one format or structure into another format or structure. Use the snapblocs UI to create a Data Transformation stack by configuring the data transformation options, such as data source/target locations and data transformation settings. Data Transformation can be done in a real-time stream mode using a data pipeline or in a bulk batch mode using ETL, depending on the settings and use cases.
    And more blueprints will be coming later.

    Select a Data Platform from the catalog of Architecture Blueprints and easily adjust the default settings to the desired settings based on the deployment environment, including cluster scale sizing, networking, security, high availability, etc. Also, add or subtract Components from the pre-defined set of Components, from the Architecture Blueprints, according to the use case.

    Benefits:
    • Ensure optimal integration of components to maximize ROI.
    • Accelerate deployment to realize benefits more quickly.
    • Reduce project risk by leveraging a proven architecture validated by standard industry practices.
    • Reduce costs by addressing sizing, deployment, and ongoing operational needs.

    Architecture Blueprints include:
    • Logical architecture and diagrams.
    • Pre-curated components with default configurations.
    • Integration, configuration, and sizing guidelines.
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