The Omni-Structured Data Management Platform
by developers for developers
Agenda
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What is APIRO?
What makes us unique
Our vision
Demo
In a few words
Flexibility – Scalability – Customizability – Extensibility - High Availability
Apiro in a nutshell
We provide a wide range of features that will not be detailed on this slide. Let’s have a look at what makes us truly different.
Technology, source and format agnostic
Apiro handles any data format, from any source, using any technology
Generative AI at the core
Innovative AI integrations to categorize and augment data in real time (including LLMs an LaMMA)
Optimized for real time
Despite not being an ETL tool, the platform comes with in-built advanced Features
Extensibility - scriptability
Customize the platform as per your requirements with barely any boundaries
Clustering and High Availability
SLA 99.95%
The custering model is sharded multi-master with robust HA
Apiro for DevOps
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GitOps advantages
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Extensibility and Customization: DevOps teams can customize their workflows and integrate GitHub with other tools in their ecosystem seamlessly. This flexibility enables DevOps teams to tailor their infrastructure and data management processes to meet specific requirements and preferences.
Providing DevOps teams a unified platform for collaboration, version control, automation, and community-driven innovation. This integration empowers DevOps teams to streamline their workflows, improve productivity, and accelerate the delivery of high-quality data infrastructure and configurations.
Centralized Repository: Integrating Apiro with a CMDB (GitOps?) and GitHub allows DevOps teams to centralize their entire infrastructure and configuration management workflow in one place. This simplifies access, collaboration, and management of all components involved.
Collaboration and workflow: pull requests, code reviews, and issue tracking, facilitate collaboration among team members working on infrastructure, configurations, and data management tasks. By leveraging these features, DevOps teams can ensure transparency, accountability, and continuous improvement in their workflows.
Versioning and History: GitHub's version control allows for easy rollback to previous versions in case of errors or issues, providing a safety net for managing changes effectively.
Community Contribution and Sharing: DevOps teams can leverage the CMDB feature to discover and reuse pre-built infrastructure templates, configuration scripts, and data management tools contributed by the broader community.
Automation and Continuous Integration: integration with CI/CD (Continuous Integration/Continuous Deployment) pipelines enables automated testing, building, and deployment of infrastructure and data management code. DevOps teams can automate the validation and deployment of changes to ensure consistent and reliable delivery of data infrastructure updates.
Omni-source and Omni-format
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Flexibility matters. Our clients come from different industries with specific needs, tools and data formats
Omni
source
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Omni
format
Tech
agnostic
Apiro can source data from any type of sources, even unconventional such as email attachments
Each clients has specific tools which may use different file format. Apiro handles any data format without any limitation
As a central data management tool, Aprio was developed to be technology agnostic thus can be easily integrated within any IT configurations of the clients
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Apiro’s capacity to tackle virtually any data challenges, in any organization
Being cloud agnostic, or having the ability to deploy and manage applications across multiple cloud environments without being tied to any particular provider, offers several advantages
Cloud provider agnostic
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Flexibility:: You can choose the cloud services that best suit your needs for each specific task or project. This flexibility allows you to mix and match services from different providers based on factors like cost, performance, and geographic availability.
Resilience and Redundancy: Multi-cloud architectures can improve resilience and redundancy by spreading workloads across different cloud providers and regions. This helps mitigate the risk of downtime due to outages or other infrastructure issues affecting a single provider.
Performance Optimization: Depending on the location of your users or specific requirements of your applications, different cloud providers may offer better performance in certain regions or for specific services. Being cloud agnostic allows you to choose the most optimal infrastructure for each scenario.
Compliance and Data Sovereignty: Some industries have strict compliance requirements regarding where data can be stored and processed. Being able to deploy across multiple cloud providers gives you more options to ensure compliance with regulations and data sovereignty laws.
Extensibility
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AI Data processors
Data source
Data sinks
ExecutionDomain feature and integrated Maven
Allows clients to use their own java libraries
Rest Extensions
Clients can add their own Rest APIS and benefit from using the integrated security features.
Integration API
Clients can develop their own processing pipeline extensions such as validators, datapoint processors, event listeners, data sources and data source transformers in total autonomy
Accessible HTTP/REST feature
Easily call out to client microservices as an alternative to ExecutionDomains
Customizability
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Script driven
Inline Extension Point Scriptability
Script aware configuration
inline scripts can be embedded directly into a processor's configuration json
Script Surface Extensions
Flexible processing pipeline
Providing a flexible and extensible processing pipeline, whether with or without AI, empowers organizations to efficiently manage and derive value from their data assets while adapting to changing business requirements and technological advancements.
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Modularity
A well-designed pipeline breaks down complex processing tasks into modular components, making it easier to understand, maintain, and debug. Developers can focus on improving or replacing individual modules without affecting the entire system.
Customization
Users can tailor the pipeline to their specific use cases and preferences. This customization enhances user satisfaction and productivity by providing the tools and workflows that best suit their requirements.
Adaptability
An extensible pipeline can easily accommodate new data sources, processing algorithms, or business requirements. This adaptability enables the platform to evolve along with the organization's needs without requiring significant reengineering efforts.
Scalability
Flexibility allows the platform to scale according to changing data volumes and processing requirements
Automation
AI algorithms can automate various data processing tasks, reducing the need for manual intervention and speeding up the overall processing time.
Future-proofing
It can easily incorporate new AI algorithms, data formats, or processing techniques as they emerge.
Integration
Integration becomes smoother with a flexible pipeline. Whether it's integrating with external data sources, downstream analytics tools, or other applications within the organization's ecosystem, a flexible pipeline facilitates seamless data flow.
Continuous Improvement
AI systems can learn from new data and feedback, continuously improving their performance over time. This iterative learning process enhances the effectiveness of the processing pipeline. Apiro will be able to “develop and optimize itself, autonomously”.
Flexible processing pipeline
With or without AI
Proactive monitoring
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Early Error Detection: Identifying invalid data early in the processing pipeline allows for prompt resolution before it propagates further downstream. This prevents the accumulation of errors and reduces the likelihood of data corruption or misinterpretation, saving time and effort in troubleshooting and rectification.
Automated Remediation: The platform's built-in validators, processors, and alerts can automatically trigger remediation actions for certain types of invalid data, streamlining the data correction process. Automated updates or transformations can be applied to fix common data issues, reducing manual intervention and ensuring timely data availability.
Flexibility: Offering the option for manual or automated update of invalid data provides flexibility to accommodate different use cases and user preferences. Users can choose to intervene manually for complex or critical data issues, while routine or well-defined errors can be handled automatically through predefined processes.
Enhanced Productivity: Automating the resolution of common data errors frees up valuable time for data stewards and analysts to focus on more strategic tasks, such as data analysis, interpretation, and decision-making. This improves overall productivity and efficiency within the organization.
Alerting Mechanism: Alerts generated by the platform notify relevant stakeholders about the presence of invalid data, enabling timely intervention and resolution. Alerting mechanisms can be configured to notify designated individuals or teams via email, dashboard notifications, or integration with external monitoring systems.
Continuous Improvement: By systematically detecting and addressing invalid data, the platform supports a culture of continuous improvement in data quality. Feedback loops can be established to analyze the root causes of data errors, identify recurring patterns, and implement preventive measures to minimize future occurrences.
By proactively detecting and flagging invalid data, the platform ensures that only high-quality data enters the downstream processing pipeline. This helps maintain data integrity, accuracy, and consistency.
Apiro as a primary data source
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Centralization, consistency, integrity, trustworthiness, efficiency, scalability, and comprehensive data management capabilities are advantages contributing to better decision-making, operational excellence, and competitive advantage for organizations
Data Centralization: Centralization simplifies data access, reduces redundancy, and ensures consistency across the organization. It also streamlines data governance and security practices by providing a single point of control.
Operational Efficiency: Users can access, manipulate, and analyze data directly within the platform, eliminating the need to switch between multiple systems or manually integrate disparate data sources. This reduces data silos, accelerates decision-making, and enhances overall productivity.
Data Quality Control:. It enables the implementation of validation rules, data cleansing processes, and quality assurance checks at the point of data entry or ingestion. This proactive approach helps maintain high data quality standards throughout the data lifecycle.
Data Consistency and Integrity: All data accessed by users and applications originates from a trusted and authoritative source. This promotes data consistency, integrity, and accuracy, which are essential for making informed decisions and conducting reliable analyses.
Single Source of Truth: Having a definitive source of data eliminates confusion and discrepancies that may arise from using multiple, potentially conflicting data sources. It fosters trust in the data and ensures alignment across departments and stakeholders.
Scalability and Performance: It ensures scalability and performance as data volumes grow. The platform is designed to handle large datasets efficiently, with features such as data partitioning, indexing, and caching. It can accommodate evolving business needs and support high-performance data processing and analytics at scale.
Comprehensive Data Management:
Data ingestion, storage, transformation, integration, and analysis. This holistic approach enables organizations to manage their entire data ecosystem within a unified platform.
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Apiro’s declarative Framework
Our declarative framework provides a way to specify desired outcomes or behaviors without explicitly programming the steps to achieve them..
Instead of writing code to implement these processes, users can express their requirements declaratively, specifying what they want to achieve rather than how to achieve it. This simplifies the development and maintenance of data processing logic, making it more accessible to users with varying levels of technical expertise.
Our declarative framework allows users to define data consolidation, validation rules, and event-triggering conditions using a declarative language or configuration-based approach.
APIRO performs validation checks to verify that the data meets predefined criteria or standards, such as data type, format, range, or consistency rules. Validating data helps identify and correct errors or discrepancies early in the data processing pipeline, minimizing the risk of using faulty data for analysis or decision-making.
DATA VALIDATION
Event triggering involves automatically initiating actions or workflows based on predefined conditions or events detected in the data. Events could be specific data patterns, thresholds, anomalies, or business rules. When an event occurs, the platform triggers predefined actions, such as sending notifications, executing data transformations, updating databases, or invoking external services. Event triggering enables real-time or near-real-time responsiveness to changes or events in the data, facilitating timely actions and decision-making.
EVENT TRIGGERING
This involves bringing together data from multiple sources into a single, unified repository or data store. Consolidation is crucial for organizations dealing with disparate data sources, such as databases, files, APIs, or streaming data sources. By consolidating data, the platform enables users to access and analyze all relevant data from a centralized location, improving data accessibility and consistency.
DATA CONSOLIDATION
Focus on real time processing
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Banking
Batch processing: End-of-day reconciliations involve consolidating and matching transactions in batches, guaranteeing the accuracy of financial ledgers.
Real-Time processing: Real-time fraud detection through transaction monitoring helps prevent unauthorized or suspicious activities�
Marketing
Batch processing: Bulk promotional emails or newsletters are sent out using batch processing for consistent and timely deliveries to subscribers.
Real-Time processing: Real-time sentiment analysis scans online discussions and feedback so brands can gauge and respond swiftly to public opinion.
E-Commerce
Batch processing: Order management often employs batch processing where orders are grouped to streamline inventory checks and optimize dispatch schedules.
Real-Time processing: Real-time monitoring of user behaviors on platforms lets you provide instant product recommendations for enhancing the online shopping experience.
Accurate data, at all times
Logistics / Supply chain
Batch processing: Shipments and deliveries are grouped based on destinations. This helps optimize route planning and resource allocation.
Real-Time processing: Real-time tracking of shipments gives immediate status updates to customers and addresses any in-transit issues swiftly.
Retail
Batch processing: Once the store closes, inventory evaluations refresh stock levels and pinpoint items that need to be replenished.
Real-Time processing: Point of Sale (POS) systems process transactions immediately, adjusting inventory and offering sales insights on the spot.
Examples of real-time processing application for different verticals
Managing sensitive data with Apiro
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With the increasing emphasis on data privacy, security, and the need for high-quality data (especially in our AI era), specific features are required to comply with different regulations and compliance.
Data Masking and Anonymization: With growing concerns around data privacy and regulations like GDPR and CCPA, data masking and anonymization techniques are essential. These methods help protect sensitive information by replacing identifiable data with fictitious or masked values while preserving the data's utility for analysis and development purposes.
Synthesized Data and Augmentation: Synthesized data generation involves creating artificial data that mimics the characteristics of real data. This is useful when the real data is limited or sensitive. Augmentation involves enriching existing datasets with additional synthetic or real data to enhance its quality and diversity, which can improve the performance of AI models and analytics.
Bi-Temporal Data: Bi-temporal data management involves tracking data changes over time along two distinct axes: valid time and transaction time. Valid time represents when the information is true in the real world, while transaction time represents when the data was recorded or modified in the system. This capability is valuable for analyzing data evolution, historical trends, and auditing purposes.
Historical Data: Retaining historical data allows organizations to analyze trends, patterns, and behaviors over time, enabling better decision-making and strategic planning. Historical data is particularly valuable for predictive analytics, forecasting, and understanding long-term changes in business metrics.
Historical Data Edits and Logging: This feature enables tracking and auditing changes made to historical data over time. It ensures data integrity and accountability by providing a record of who modified the data, when the changes occurred, and what the previous values were. Historical data edits support compliance with regulations and internal governance policies.
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Let’s discuss!
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