Picture this: You’re looking to book a flight either because you want to treat yourself to a nice vacation or a business trip is coming. Naturally, you go to your preferred airline website—or Google Flights.
Once you choose the dates and hit the “Search” button, there’s always a brief pause before getting the results. In that pause, the airline website connects to global distribution systems (GDS), in real-time, to check flight schedules. This process, between hitting “Search” and getting flights’ availability, is called real-time data ingestion.
Real-time data ingestion ensures the responsiveness of your systems with minimal delay (in a matter of seconds or milliseconds). In turn, you get great user experience and, in other scenarios, vital information beyond flight bookings.
In this article, you’ll not only learn what real-time data ingestion is but also, its architecture, how it works, use cases, benefits, and challenges.
What Is Real-Time Data Ingestion?
Real-time data ingestion is the process of collecting and processing data as soon as it’s generated, ensuring users have access to the most up-to-date information. In a world where seconds and even milliseconds matter, data starts losing value after it’s generated. That’s why real-time data ingestion is so important.
The main goal of real-time data ingestion is to minimize the delay between when the data is generated and when it’s available for access.
This goal is achieved thanks to two key characteristics:
- Low latency: Minimizing the delay between when the data is generated and when it’s ready to use.
- Continuous data processing: A constant flow of information ensures data freshness.
It’s important to note that latency and freshness are two different terms when it comes to data ingestion. Latency is the delay between the data being ingested and its availability for analysis. Freshness is more about how recent this information is.
Consider this: Quick access to data (low latency) might be less valuable if the information is weeks or months old. On the other hand, if the data is only seconds old (freshness), it becomes significantly more valuable for making agile decisions.
The Real-Time Data Ingestion Pipeline
The real-time data ingestion pipeline begins at the point of data generation and ends when the processed data is stored for further analysis. However, the most interesting part of the process occurs in the middle, where the data is transformed and optimized.
The middle part of the pipeline is pivotal in the process of data ingestion as it not only ensures that you get timely but also refined information.
Here’s how the real-time data ingestion pipeline works:
Figure 1: Data Stream Processing
This is where real-time data ingestion begins. The data sources are the systems or devices that generate data in real-time. These sources could include IoT devices, sensors, applications, or other databases.
Data Ingestion Layers
The data ingestion layers handle different aspects of data ingestion to ensure the efficient flow of information.
Here are the typical layers:
- Ingestion layer: The layer that collects raw data from the data sources. You’ll often use a distributed message broker to ingest and publish the data along with message queues.
- Processing layer: This is where data is transformed by adding information and normalizing formats for consistency. The result of this layer is information cleaned, validated, and ready for analysis.
- Storage layer: As its name suggests, this layer stores the enriched data for further analysis.
As a side note, the processing layer can be split into transformation and enrichment layers.
However, the ingestion, processing, and storage layers are the backbone of an efficient data flow.
End User Access Layer
This is the point where the end-user interacts with the stored (and enriched) data. It acts as a gateway involving interfaces, APIs, or other query tools to facilitate the process of retrieving and analyzing data.
The gist of it is that the access layer allows you, the end user, to request or input commands to obtain insights and improve decision-making.
Real Time Data Ingestion vs. Batch Ingestion
If there’s real-time data ingestion, there must be another process that doesn’t involve data in real-time, right?
There are two types of data ingestion methods, real-time data ingestion and batch ingestion.
Real-time data ingestion, as you already know, is the process of collecting data as soon as it’s generated, ensuring the user has access to the newest information. This process involves a continuous stream of data that’s processed in seconds.
Batch ingestion collects data in increments over a specific period to then process the entire batch. Unlike real-time data ingestion, which processes data in near real-time, batch ingestion accumulates a large volume of data to process it periodically. The process is depicted in the following figure.
Figure 2: Batch Processing
Batch ingestion is often associated with ETL (Extraction Transform Load) because of its scheduled executing and handling of large volumes of data. However, these concepts are fundamentally different. Batch ingestion can be part of the ETL process during the Extract and Load phases, but it doesn’t focus on Transformation.
Real-Time Data Ingestion: Use Cases
Real-time data ingestion is important because it provides organizations with responsive systems which empowers their decision-making.
Here are some real-life examples to solidify its importance in the following industries:
When you think about investment portfolios and stocks your mind immediately shifts to that big black screen showing trends, graphs, and numbers—or probably the Wolf of Wall Street movie. Either way, we’re on the same page.
Investment portfolios are subject to changing market conditions. And the financial market is dynamic with fluctuations in currency rates, stocks, and commodity prices, to mention a few. A sudden price drop could mean losing millions of dollars, if you don’t act fast.
Financial firms use real-time data to analyze the components of your portfolio and their performance. If market conditions change, risk management systems can trigger responses in real-time to mitigate losses.
A plant with bustling assembly lines, programmed machinery, and well-documented processes may not look like a place to leverage quick decision-making. But that couldn’t be farther from the truth.
An industrial plant can experience equipment failures, deviation from quality standards, and, of course, human errors. IoT sensors placed in the infrastructure of a manufacturing plant can generate continuous streams of data regarding performance metrics on machines or quality parameters from products.
The role of real-time data ingestion here is to process IoT-generated data for immediate analysis. This enables different teams to receive alerts when issues arise and act in near to ensure the quality of their processes.
Energy consumption forecasting is vital for utilities to anticipate demand and optimize resource allocation.
We already saw how in the financial sector, real-time data helps analyze investment portfolios while in manufacturing, IoT sensors provide data for quick-decision making. In the energy sector, predictive algorithms use real-time data to forecast consumption patterns. By monitoring gird health and user behavior, utilities can adjust energy generation in advance to minimize the cost during peak demand periods.
Think of this as a way of optimizing energy production, resource allocation, and efficiency.
The optimization of the water distribution system also relies on real-time data to monitor flow rates, pressure, and quality. Real-time information, from distribution networks, aids utilities in optimizing pumping stations and detecting leaks.
The integration of sensors and data analytics allows utilities to respond to issues quickly. This minimizes water losses, maintains the resilience of the water distribution system, and delivers a reliable water supply to consumers.
Still, that’s just the tip of the iceberg. Real-time data can also be used to monitor the environmental impact of water treatment processes by tracking the discharge of treated water into natural water bodies, assessing its quality.
Yes, social media also uses real-time data ingestion. Every time you like, share, or comment on a post, there’s data being generated to analyze your behavior. As a result you get real-time content recommendations that enhance your user experience but also make it harder for you to get off from the app.
This content personalization technique using real-time data ingestion is what makes social media so addicting.
Real-Time Data Ingestion Benefits
We’ve come a long way, so you probably can discern some of the benefits already. However, here’s a rundown of the real-time data ingestion benefits:
- Near real-time decision-making: Real-time data ingestion allows organizations to make immediate decisions based on the most up-to-date information.
- Competitive advantage: Real-time insights can help industries adapt to market shifts, giving organizations a competitive edge.
- Early detection of problems: The continuous monitoring of processes through real-time data helps detect problems as they occur, minimizing the impact on operations.
- Improved user/customer experience: Real-time data supports responsive systems which better the user or customer experience as they can get timely information, reducing digital frustration.
- Operational efficiency: Timely insights can make a big difference in operational expenses if issue resolutions or opportunities for improvement are implemented early.
Real-Time Data Ingestion Challenges
Now, the concept of real-time data ingestion sounds great. But with all good things, there are also challenges.
Here are the data ingestion challenges you should be aware of:
- Data quality: Ensuring the quality and consistency of incoming data in real time, especially from diverse sources, can be complex.
- High costs: With the growing demand for data, companies have to allocate more of their budget to pay for data storage solutions.
- Security and compliance: When you’re dealing with sensitive information in real-time, you’re also exposed to data breaches and legal complications.
- Performance issues: As data volumes increase, companies can face performance issues like bottlenecks and slower processing times.
- Scalability: The growing demand for data makes scaling real-time data ingestion systems a priority, so their ability to provide real-time insights isn’t hindered.
Data has become an even more valuable resource in recent years, and the companies that aren’t collecting it or have data management tools in place are getting left behind.
The good news is, not all companies need to implement real-time data ingestion in their processes. Besides the fact that it can be more expensive than batch ingestion, their processes may not require immediate insights.
To ensure your investment is worth it, make sure to consider the specific needs of your organization.
Real-Time Data Ingestion
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