BDB Data Pipeline

Within the realm of data and analytics ecosystems, data pipelines and engineering play crucial roles in ensuring the seamless flow and effective utilization of data.

  • Data pipeline refers to a structured sequence of processes that transport, transform, and manage data from various sources to a designated destination.
  • This orchestrated movement of data is essential because it enables organizations to harness the power of their data by making it accessible and usable.
  • A well-designed data pipeline ensures that data is collected, cleansed, transformed, and loaded into storage or analytical systems in a consistent and efficient manner.

Challenges

Building and Maintaining Infrastructure

  • >> Takes significant time and cost to build
  • >> It is combination of multiple tools and complex integration
  • >> Requires highly skilled IT team to build the pipeline

Numerous tools and a sluggish integration process

  • >> Integration of multiple tools can lead to a bottleneck in the process.
  • >> Larger integration and testing cycles
  • >> Complex build , test and deployment cycle

Monitoring and Failure
Management

  • >> Scaling and monitoring challenges
  • >> Process failure and back fill / reprocessing challenges
  • >> Difficult to build resilience and auto correction

Solutions

Higher Productivity & Faster Time to Market

  • Effortless Prototype to Production Transition: Achieve a seamless shift from prototype to production phase, ensuring a smooth transition in the data engineering process.
  • Abundance of Pre-Built Components: Utilize a wide array of out-of-the-box components, enabling rapid development and assembly of data engineering solutions.
  • Maintenance-Free Spark Deployment: Implement Spark deployment with zero maintenance required, allowing data engineers to focus on tasks beyond routine upkeep.
  • Significant Resource and Time Savings: Realize a substantial 60% reduction in both resource costs and time consumption, optimizing the efficiency of data engineering operations.

Fault Tolerence & Resilience

  • Data Integrity: Resilience mechanisms prevent data loss or corruption by ensuring that failed processes do not lead to compromised data quality.
  • Automatic Recovery: Data pipelines designed with fault tolerance can automatically recover from failures, reducing the need for manual intervention and speeding up the recovery process.
  • Isolation of Failures: Resilient pipelines are designed to isolate failures in one part of the pipeline from affecting other components, thereby preventing cascading failures.

Extesibility Via Notebook

  • Flexible Exploration and Testing: Leverage notebooks to easily explore and test new data engineering concepts, methodologies, and transformations, promoting experimentation.
  • Customizable Data Transformation: Notebooks enable data engineers to craft custom data transformation logic, tailoring it to specific project requirements and ensuring accurate data processing.
  • Cross-Functional Integration: Enable cross-functional teams to contribute to data engineering pipelines by using notebooks as a common platform for collaboration and integration.

Pre-Build Components

BDB Data Pipeline Features

Event Driven Process Orchestration

  • An Event-driven Architecture that triggers Events to communicate between decoupled services is common in modern applications built with microservices.
  • Event Components in the Data Pipeline have built-in consumer and producer functionality. This allows the component to consume data from an event process and send the output back to another Event/Topic.

An Event-driven Architecture, has 3 items :-

  1. Event Producer [Components]
  2. Event Stream [Event/Topic]
  3. Event Consumer [Components]
  • In the above pipeline, the first component produces data which is sent to the event topic.
Data Pipeline solutions
Data Pipeline solutions

Drag and Drop Interface

Assembling a data pipeline is very simple. Just click and drag the component you want to use into the editor canvas. Connect the component output to an event/topic.

  • Easy to learn
  • No coding skills needed
  • You can build and deploy a pipeline within hours
  • Pre-build test framework
BDB Drag and Drop Interface

Self Service low code

  • A wide variety of out-of-the-box components are available to read, write, transform, and ingest data into the BDB Data Pipeline from a wide variety of data sources.
  • Components can be easily configured just by specifying the required metadata.
  • For extensibility, we have provided Python-based scripting support that allows the pipeline developer to build complex business requirements that cannot be met by out-of-the-box components.
BDB Self Service low code

Real time & batch Orchestration

  • Real-time processing deals with streams of data that are captured in real-time and processed with minimal latency. These processes run continuously and stay live even if the data info has stopped.
  • Batch job orchestration runs the process based on a trigger. In the BDB Data Pipeline, this trigger is the input event Anytime data is pushed to the input trigger, the job will kick start. After completing the job, the process is gracefully terminated. This process can be near real-time. Also, it allows you to effectively utilize the compute resources.
BDB Real time & batch Orchestration

BDB Data Pipeline allows you to operationalize your AI/ML Models in a few minutes. The Models can be attached to any pipeline to get the inferences in real-time. Then the inferences can either be used in any other or get shared with the user instantly.

DataOps:

  • Establish progress and performance measurements at every stage of the data flow.
  • Where possible, benchmark data-flow cycle times.
  • Automate as many stages of the data flow as possible including BI, data science, and analytics.
BDB DataOps

  • BDB Data Pipeline identifies the need for process scaling by measuring the resource utilization and the process lag.
  • In-build process scaler reads multiple process-metrices and automatically marks the scale-up or scale-down process.

The flexibility of deploying across any cloud platforms like:

  • AWS
  • Azure
  • Google

as Well as On-Premise infrastructure.

Cloud Agnostic & Hybrid Deployment

  • The Pipeline Process Monitoring Feature allows users to monitor the progress and performance metrics through the monitoring dashboard.
  • This dashboard provides full visibility of the compute resources utilization like CPU and memory utilization along with logs and no. of records processed by each component.
  • All metrics generated by pipeline components can be integrated with Enterprise monitoring software.
Pipeline & Process Monitoring

Reliability, Scalability & Maintainability

Fault Tolerance is the property that enables a system to continue operating properly in the event of the failure (or one/ multiple faults within) of some components within the pipeline workflow.

  • Self Healing:- If a containerized app or an application component fails or goes down. Kubernetes re-deploys it to retain the desired state.
  • Rolling updates:- incrementally replace your resource's Pods with new ones, with available resources. Rolling updates are designed to update your workloads without downtime
  • Auto Scaling:- Based on pre-configured metrics processor can be automatically scaled based on certain threshold breaches
Fault Tolerant and Auto Recovery
  • Load Balancing:- Distribute traffic to servers between available process, there by increasing process relaibility

  • Any Customer can build custom components based on the component development framework and deploy the container to the platform registry.
  • Once deployed these components work as regular off-the-shelf components.

Custom Transformers via scripting

These are components that allow you to directly use scripting languages like:

  • Python
  • NodeJS
  • Perl to transform the data.
Custom Integration and Extensibility

  • Parallel Processing is becoming ever more important as data volumes and computational loads are increasing but the speed of processors is not.
  • The way out of this knot is to take advantage of more processors but in a scalable manner.
  • You can run multiple instances of the same process to increase the speed.
  • This can be done using the auto-scaling feature
Parallel & Distributed Processing

Data Engineering and Analytics Use Cases

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