IoT data analytics

Our software meets GDPR, CDPA, HIPAA, PCI-DSS, and other requirements with:

Built-in security measures. We ensure that IoT data is protected throughout its lifecycle, from collection to storage and transmission with data encryption, access controls, and user authentication mechanisms.

Consent management. Our software allows users to easily provide and withdraw consent, ensuring compliance with consent-based data requirements.

Data retention and deletion. We build software that lets our clients efficiently manage IoT data retention periods and facilitate secure data deletion when required.

Audits and logging. We incorporate audit functionalities, enabling clients to track data access, modifications, and other relevant activities. 

Ongoing monitoring and updates. We constantly monitor changes in data regulations and standards. As new requirements emerge, we update our software solutions accordingly to ensure ongoing compliance for our clients.

The Yalantis approach includes a comprehensive architecture consisting of:

Data ingestion, which involves receiving and processing incoming data streams. In IoT platforms, we use message brokers like Apache Kafka or RabbitMQ. 

Data processing and analytics, which involves various operations such as filtering, aggregating, and transforming data. For this, we use stream processing frameworks like Azure Stream Analytics, AWS Kinesis, Apache Flink, Apache Spark, or Apache Storm.

Real-time decision-making based on incoming data. For instance, we implement predictive maintenance in industrial IoT solutions to detect anomalies and trigger actions in real time. For this, we integrate decision-making engines and machine learning models into the streaming data pipeline.

Data storage and retention for further analysis or compliance. We commonly use databases like Apache Cassandra or time-series databases like InfluxDB for storing real-time data.

Data visualization and reporting through real-time dashboards and reporting tools to provide meaningful insights to users and stakeholders. 

Our approaches include but are not limited to: 

Scalable infrastructure: We design and implement scalable infrastructure using cloud platforms such as AWS, Google, and Azure to scale resources based on the data load and optimize performance.

Distributed architecture: We opt for a distributed architecture, where different components can run on multiple nodes. This allows the system to scale horizontally by adding more nodes when demand increases.

Load balancing: We implement load balancing mechanisms to evenly distribute incoming data and processing workload across multiple nodes. 

Data partitioning: If a system involves storing data, we use data partitioning techniques to spread data across multiple nodes or shards to ensure that data storage and retrieval operations are distributed and not bottlenecked by a single resource.

Replication and redundancy: We duplicate critical components and data to create redundancy and ensure the system remains available even if one location or zone experiences an outage.

Yes. To simplify data analytics in IoT, we implement:

Customizable dashboards: Clients can define key performance indicators (KPIs) and select specific data visualizations that align with their industry and operational requirements. 

Industry-specific templates: We offer industry-specific templates that are designed based on our understanding of different industries, ensuring that clients can quickly set up dashboards tailored to their specific needs.

Data contextualization: Integration with various data sources and systems ensures that generated dashboards and reports provide actionable insights within the context of the industry and its unique challenges.

Advanced analytics and alerts: Our software includes advanced analytics capabilities to perform industry-specific analysis, anomaly detection, and predictive analytics. Additionally, we can set up alerts and notifications to ensure diligent real-time monitoring and a timely response to any arising issues.

Yes. In terms of data management in IoT, we provide:

Compatibility with standard data formats and protocols. Our software supports standard data formats and protocols commonly used in the industry, ensuring smooth integration with existing systems.

APIs. We provide robust APIs that enable seamless communication between our software and other systems and streamlined IoT data exchange.

Custom integration solutions. In cases with specific integration requirements, we have the expertise to develop custom integration solutions to bridge the gap between our software and clients’ existing systems.

Real-time data synchronization. We enable real-time IoT data synchronization between systems to ensure that data remains consistent and up to date across platforms.

Yes. Software solutions Yalantis develops can analyze real-time data from Internet of Things devices to identify potential maintenance needs. We enable mechanisms that continuously monitor streaming IoT data, applying advanced analytics and machine learning algorithms to detect patterns, anomalies, and deviations. By establishing baselines and using predictive models, our software can forecast maintenance requirements and provide timely alerts and notifications. This helps our clients with proactive planning, prevents unplanned downtime, and optimizes equipment performance. 

Our team also ensures that our software integrates with existing maintenance systems, facilitating IoT data flows and allowing for efficient management of identified maintenance needs.

At Yalantis, we specialize in providing comprehensive data analytics for IoT and designing solutions capable of handling diverse data collected from various IoT devices, even when data formats and structures differ. Our built-in mechanisms ensure effective processing of complex data, allowing seamless integration with different IoT devices and enabling data collection from a wide range of sources.

To address variability, we employ Internet of Things data standardization techniques to ensure consistency across devices. This ensures accurate analysis and provides meaningful insights. Additionally, our solutions leverage advanced methods, including machine learning, for IoT data processing and analysis.

We design and implement scalable infrastructure to handle large volumes of IoT data from multiple devices without compromising performance. Our solutions are customizable to cater to the unique characteristics of each Internet of Things device, ensuring that IoT data processing and analysis are tailored to specific requirements.