Data ingestion and Big Data management

Written by Luca Landolfi

The Internet of Things (IoT) is transforming industries and everyday life by enabling devices to collect and exchange data. With billions of devices generating massive volumes of data, effectively managing and utilizing this information is crucial. Data ingestion and big data management are the pillars that support IoT applications, ensuring that data is accurately collected, processed, and analyzed to provide actionable insights.

Data ingestion

Data ingestion is the process of collecting and transporting data from various sources to a storage or processing system where it can be accessed, analyzed, and utilized. In the context of IoT, this involves gathering data from a plethora of devices, sensors, and systems, often in real-time.?

The ingestion is a highly complex process that poses several challenges that need to be addressed in order to build an efficient and scalable solution:

• Volume and Velocity: IoT devices generate a massive amount of data at high speed, requiring systems capable of handling large-scale, real-time data streams.
• Variety: Data comes in various formats (structured, unstructured, semi-structured) and from different sources, necessitating robust systems that can normalize and integrate diverse data types.
• Quality and Consistency: Ensuring data accuracy, completeness, and consistency is critical, particularly for real-time applications where decisions are made based on incoming data.
• Latency: Minimizing the delay in data processing to ensure real-time or near-real-time data availability.
• Scalability: The system must scale horizontally to accommodate the growing number of IoT devices and the resultant data influx.

There are several approaches that are commonly used during the ingestion process:

• Batch Processing: Involves collecting and processing data in large batches at scheduled intervals.?
• Stream Processing: Handles continuous data streams in real-time, making it essential for time-sensitive IoT applications.?
• Edge Computing: Processing data at or near the source (the edge) to reduce latency and bandwidth use.?

Sensoworks platform offers an hybrid approach to data ingestion, mixing several of the aforementioned techniques. Data can flow in the platform from a number of different sources:

• Edge devices can directly communicate with the platform using one of the supported protocols, for example the industry standard MQTT
• The Sensoworks FOG gateway talks directly to edge devices, captures their data (in streaming or in batch, depending on the source), can transform the data and then send it to the platform

Data can be ingested into the platform using different protocols, such as:

• HTTP
• MQTT
• Kafka

Big data

Big data refers to extremely large and complex data sets that traditional data processing software and techniques cannot handle efficiently. The term encompasses the sheer volume, variety, and velocity of data being generated in today's digital world, which necessitates advanced tools and methods for storage, processing, and analysis.

When can data be considered “big”? Often the three Vs are used to define what kind of data fall under this denomination:

• Volume: Refers to the vast amounts of data generated every second from various sources such as social media, sensors, transactions, and more. The scale of data is often measured in terabytes, petabytes, and even exabytes.
• Variety: Indicates the different types of data—structured, semi-structured, and unstructured. This includes text, images, videos, sensor data, logs, and more, coming from diverse sources.
• Velocity: Describes the speed at which data is generated and processed. In many applications, data needs to be processed in real-time or near-real-time to be useful.

With little to no surprise the characteristics of the big data overlaps with those of the data treated by the ingestion phase, simply because once the data is ingested it must be stored inside a system that permits the efficient retrieval of information, in other words, a big data management system.

Common approaches to Big Data Management includes:

• Data Warehousing: Traditional data warehousing involves collecting and managing structured data from different sources. Solutions like Amazon Redshift and Google BigQuery are used for querying and analyzing large datasets.
• Data Lakes: A more flexible approach that stores raw data in its native format. Data lakes, such as those built on Hadoop HDFS or cloud storage solutions like Amazon S3 and Azure Data Lake, allow for storage of both structured and unstructured data.
• Hybrid Approaches: Combining data lakes and data warehouses to leverage the strengths of both systems, allowing for flexible data storage and structured querying.

Designing and implementing an efficient big data system is not simple. The modeling of the storage layer is dependent on the input data format and the analytics that need to be performed on the data to gain knowledge and information.

Some of the aspect that need to be considered during the choose of the big data system and the data modeling are:

• Performance: A Big Data system should be able to respond to analytics interrogations in a timely manner compatible with the business needs.
• Flexibility: A storage model should be flexible enough to adapt to evolving business requirements.
• Data normalization: The sources of data are almost always different, meaning that the data that they produce is of all kinds of shape and format. Often a normalization phase is necessary to make the data uniform and suited to be interrogated by analytics processes.

• Retention: Even if nowadays the storage is cheap, it’s not free. A cost-efficient system should implement the deletion of the oldest and useless data

Performance and flexibility are often orthogonal to each other, because the more flexible and generic a data model is, the less performance optimization can be implemented. A tradeoff must often be made between the two requirements.?

Conclusion

The ability to ingest, process, and analyze vast amounts of data in real-time provides significant competitive advantages, driving innovation and operational efficiency across various industries. As IoT continues to grow, the integration and enhancement of these technologies will be paramount in harnessing the full potential of connected devices and their data.

By understanding the challenges and leveraging the appropriate techniques and technologies, organizations can unlock the true value of their IoT data, paving the way for smarter, more informed decision-making and fostering a data-driven culture.