production-ready Data-Flow Platform on Kubernetes
We automate production-ready data platforms in your cloud so you can focus on core work.
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Creating cloud based Data Flow solutions at scale is challenging!
Creating a production-level solution to move data at scale takes a lot of work, from integrating many open source technologies, setting up and integrating all of the different components. It's a challenge to scale and difficult to troubleshoot. snapblocs can help you spend less time on infrastructure work, freeing up more time and resources to deliver projects that achieve your business goals.
Deploy your Data Flow solution in minutes!
There is no need to endure complex architectural design, installation, configuration, or scripting. Just click to create your Data Flow Platform on Kubernetes and let snapblocs automate the rest!
- Simplify your Data Flow implementation
- Process real-time streaming or bulk data at volume
- Self-Service low-code - DevOps can automate provisioning
- Full lifecycle management - start, update, terminate, pause, resume, clone, move, etc.
- Built-in Elastic Observability = logging, metrics, Application Performance Monitoring (APM)
- Deploy into your cloud, control your infrastructure and data
Reduce your development time, free up resources, and focus on more important work!
Choose the Data Platform you need for data ingestion, replication, and synchronization
Depending on your needs, you can deploy Data Flow with all the bells and whistles or just the minimum Kafka-only solution bloc.
Focus on what you do best
snapblocs dpStudio automates many data platforms on Kubernetes, including Data Flow.
- Low-Code, lowers skill requirements
- Deliver projects faster, better
- Free up scarce resources
- Focus on your core work
- Less tech debt
snapblocs automates architecture
snapblocs automates based on the "well-architected" guides such as "AWS Well-Architected for AWS" and Google "Cloud Architecture Framework." for the provisioning and configuring of production-grade Kubernetes clusters and workload deployment into the clusters.
snapblocs Architecture-as-a-Service delivers instant value
Day 2 level operations out of the box
- High availability
- Data protection
- Data security
- Configurable alerts
- Health checks
- Cost optimization
- Easy debugging of topic data
- Easy overriding of Kafka parameters
- Scaling on demand
- Graceful shutdown
- Pause, Resume cluster without data loss
snapblocs Data Flow Platform includes
snapblocs Data flow blueprints combine multiple best-in-class open-source technologies into ready-to-go solution-blocs on Kubernetes.
Amazon EKS - Google GKE - Microsoft AKS
Depending on which cloud provider you use, Amazon's EKS, Google's GKE, or Microsoft's AKS is utilized to provision snapblocs Data Flow Platform instances into your cloud account.
Kubernetes is an open-source container-orchestration system for automating application deployment, scaling, and management. It is used to deploy selected Components.
Kafka is used for building real-time data pipelines and streaming applications by integrating data from multiple sources and locations into a single, central Event Streaming Platform.
Elastic is used to provide observability (monitoring, alerting, APM) for answering questions about what's happening inside the system just by observing the outside of the system.
Grafana is used to build visualizations and analytics to query, visualize, explore metrics, and set alerts for quickly identifying system problems to minimize disruption to services.
StreamSets Data Collector
StreamSets Data Collector creates continuous data ingest pipelines using a drag and drop UI within an integrated development environment (IDE).
Apache Nifi is used to create a data processing for data transforming, routing, curation.
Example Data Flow use cases
Data Flow is the perfect solution when you need reliable data movement from input data sources to your target data destinations via stream mode or bulk mode for data ingestion, replication, and synchronization.
Process and analyze streaming data to provide real-time insights and actionable intelligence. Create new products and services or improve business operations.
- Event sourcing
- Website activity tracking
- Log aggregation
- Commit log
- Stream processing
Stream Data Ingestion
Ingest data in real-time as they arrive. Good for real-time data-driven decision processing for improving customer experience, minimizing fraud, and optimizing operations and resource utilization.
Bulk Data Ingestion
Ingest blocks of data that have already been stored over a period of time. It is often used when dealing with huge amounts of data and/or when data sources are legacy systems that cannot deliver data in streams. Bulk ingestion is suitable when:
- Data freshness is not a mission-critical issue
- You are working with large datasets and are running a complex algorithm that requires access to the entire batch – e.g., sorting the entire dataset.
- You get access to the data in batches rather than in streams
- When you are joining tables in relational databases
Replicate data from one data repository to another data repository. For example, replicate MySQL data to Postgres in real-time using Change Data Capture.
Data Synchronization between data centers & clouds
Synchronize datasets from one data repository to another or between multiple data centers or Clouds.
Get instant Day-2 Level Operations
Self-service fully automated Kafka clusters deployed in your cloud environment. Click to deploy, scale at will.
- Self-service for DevOps and agile teams
- Automated Day-2 operational Kafka clusters
- Best practice security, dashboards
- Configure, deploy, manage and monitor
- Pause, resume, clone, scale
- Built-in observability
snapblocs Data Flow Platform compared
The reality of DIY Data Platforms
Building your Data Flow solution and going from POC to production requires a significant investment from multiple engineers over many months.
- Effort and resources are underestimated
- Project timelines become extended
- A lot of reinvention reduces time on core work
- Knowledgeable resources are scarce
- Security and dashboards are often inadequate
- Full-featured solution not feasible for small IT teams
Hosted Data Flow solutions?
Hosted managed Data Flow solutions promise simple turnkey solutions, but data becomes spread across more data locations.
- Increased data movement in/out of vendor VPCs
- Creates privacy, security, latency, and cost issues
- Not as cost-effective with high event volumes
snapblocs Data Flow Platform vs other vendors comparison chart
|Self-service provision to cloud
|No vendor lock-in (Open source)||Yes||No||Yes|
|Run on Kubernetes||Yes||No||Manual installation|
|Loosely coupled architectures||Yes||No||No|
|Lower risk for integrating other open-source||Yes||No||No|
|Low-code for data pipeline||Yes||Yes||Yes
No (Confluent Kafka Platform)
|Expanding range of use cases||Yes||Limited||Limited|
|Full lifecycle features*||Yes||No||No|
|Built-in observability||Yes||Yes||Minimum or none|
|Pay-as-you-go Pricing Model||Yes||Yes / No||Yes / No|
|Recurring license / subscription fees||Small||Medium-large||None|
|Skills & resources||Modest||Modest||Many skills & resources required|
|Large number of sources and destination connectors||High ,
Streamsets DC +
|Handle backpressure||Yes||Yes / No||Yes / No|
* Powerful lifecycle features like pause, resume, clone, and move the Kafka cluster
Leverage additional snapblocs platforms
snapblocs offers a pre-fab blueprint library that combines multiple best-in-class open-source technologies into ready-to-go solution-blocs on Kubernetes
- Data Platforms for Moving, Ingesting, Transforming, Processing, Storing, Analyzing, and Presenting data.
- PLUS, Development Platforms for Kubernetes and Microservices.
snapblocs makes it easy to Deploy and Manage Data Platform stacks
How to deploy a stack
This video will explores Component Configuration, Stack Deployment and Stack Teardown.
How to manage the lifecycle of a stack
This video demonstraights Pausing, Resuming, Cloning and Moving stacks.
snapblocs SaaS based dpStudio supports multi-Cloud
snapblocs dpStudio supports the major cloud providers, including Amazon Web Services, Google Cloud Platform, and Azure.
Leverage our Data Flow Platforms on Kubernetes as an infrastructure abstraction: Configure once and run in any cloud.
Click to deploy, scale at will
Start your free Data Flow Platform on Kubernetes today
Deploy data flow solutions and many other data platforms using the snapblocs blueprint library.
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Tutorials and How-to-Videos show how dpStudio and our blueprint library can reduce your development cycle.
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