Ingestion and processing of real-time IoT data

Data ingestion: the first step to a sound data strategy

Businesses can now churn out data analytics based on big data from a variety of sources. To make better decisions, they need access to all of their data sources for analytics and business intelligence (BI).

An incomplete picture of available data can result in misleading reports, spurious analytic conclusions, and inhibited decision-making. To correlate data from multiple sources, data should be stored in a centralized location — a data warehouse — which is a special kind of database architected for efficient reporting.

Information must be ingested before it can be digested. Analysts, managers, and decision-makers need to understand data ingestion and its associated technologies, because a strategic and modern approach to designing the data pipeline ultimately drives business value.

What is data ingestion?

Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization. The destination is typically a data warehouse, data mart, database, or a document store. Sources may be almost anything — including SaaS data, in-house apps, databases, spreadsheets, or even information scraped from the internet.

The data ingestion layer is the backbone of any analytics architecture. Downstream reporting and analytics systems rely on consistent and accessible data. There are different ways of ingesting data, and the design of a particular data ingestion layer can be based on various models or architectures.

Data Ingestion Parameters

• Data Velocity – Data Velocity deals with the speed at which data flows in from different sources like machines, networks, human interaction, media sites, social media. The movement of data can be massive or continuous.
• Data Size – Data size implies enormous volume of data. Data is generated from different sources that may increase timely.
• Data Frequency (Batch, Real-Time) – Data can be processed in real time or batch, in real time processing as data received on same time, it further proceeds but in batch time data is stored in batches, fixed at some time interval and then further moved.
• Data Format (Structured, Semi-Structured, Unstructured) – Data can be in different formats, mostly it can be the structured format, i.e., tabular one or unstructured format, i.e., images, audios, videos or semi-structured, i.e., JSON files, CSS files, etc.

Big Data Ingestion Key Principles

To complete the process of Data Ingestion, we should use right tools for that and most important that tools should be capable of supporting some of the fundamental principles written below –

• Network Bandwidth – Data Pipeline must be able to compete with business traffic. Sometimes traffic increases or sometimes decreases, so Network bandwidth scalability is biggest Data Pipeline challenge. Tools are required for bandwidth throttling and compression capabilities.
• Unreliable Network – Data Ingestion Pipeline takes data with multiple structures, i.e., images, audios, videos, text files, tabular files data, XML files, log files, etc. and due to the variable speed of data coming, it might travel through the unreliable network. Data Pipeline should be capable of supporting this also.
• Heterogeneous Technologies and System – Tools for Data Ingestion Pipeline must be able to use different data sources technologies and different operating system.
• Choose Right Data Format – Tools must provide data serialization format, that means as data comes in the variable format so converting them into single format will provide an easier view to understand or relate the data.
• Streaming Data – It depends upon business necessity whether to process the data in batch or streams or real time. Sometimes we may require both processing. So, tools must be capable of supporting both.

Challenges in Data Ingestion

As the number of IoT devices increases, both the volume and variance of Data Sources are expanding rapidly. So, extracting the data such that it can be used by the destination system is a significant challenge regarding time and resources. Some of the other problems faced by Data Ingestion are –

• When numerous Big Data sources exist in the different format, it’s the biggest challenge for the business to ingest data at the reasonable speed and further process it efficiently so that data can be prioritized and improves business decisions.
• Modern Data Sources and consuming application evolve rapidly.
• Data produced changes without notice independent of consuming application.
• Data Semantic Change over time as same Data Powers new cases.
• Detection and capture of changed data – This task is difficult, not only because of the semi-structured or unstructured nature of data but also due to the low latency needed by individual business scenarios that require this determination.

That’s why it should be well designed assuring following things –

• Able to handle and upgrade the new data sources, technology and applications
• Assure that consuming application is working with correct, consistent and trustworthy data.
• Allows rapid consumption of data
• Capacity and reliability – The system needs to scale according to input coming and also it should be fault tolerant.
• Data volume – Though storing all incoming data is preferable; there are some cases in which aggregate data is stored.