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3 minute read
Many modern companies are expanding their departments and including new analytics programs. The idea is often to buy the best-in-class and pricey tools without keeping in mind the actual requirements and how the organization will use these tools.
There is a misconception that all the data powering analytics needs to be streaming and immediately processed; while it can be the fastest solution, it is not always the most optimal one. It is true that the data acquisition should be happening in real-time but the response required by the algorithm does not always need to be instantaneous.
In this article, we’d like to discuss the differences between real-time and batch analytics, and some common misconceptions that many companies stumble across in their data-driven journey.
According to the reaction time, data processing can be categorized in two main categories: real-time or batch. It’s important to understand how this reaction time is different from the time required to actually obtain such data.
Real-Time processing
Real-time processing involves the processing of data in a short period of time. In general, the reaction time is a matter of milliseconds for systems with a low latency.
Real-time processing finds many applications in financial analysis for stock markets, and bank ATMs, where it’s crucial for the platform to immediately analyze and process time-sensitive data.
One of the most widely used tools for real-time data processing is Spark. If you’re interested in understanding what Apache Spark is, check our article on how to leverage Fyrefuse with Spark here.
Challenges of real-time processing
Real-time has many advantages but it comes with some limitations as well.
The reality is that such systems are incredibly hard to implement and they rely on a completely new level of software abstraction. There’s the need to introduce ways to check which task to prioritize and how to manage different events as input data.
Another challenge specific to real-time analytics is referring to the data quality. In case of flawed data collection over a single pipeline, a lack of data quality will also be propagated throughout the entire analytics workflow.
How a company can assess if real-time is a good fit for them
When modern businesses are looking at how they use data to make decisions and evaluating if “real-time” is really necessary, there are a few steps to guide this analysis.
- Understand the data flow: Study the process of data ingestion and analysis, how often a decision is made, who is making the decisions. This will give you an idea on the time your company need to process the data. Keep in mind that if humans are part of the final data consumptions, speeding up the whole process by a small amount of time won’t add so much benefits as you can imagine, compared to the actual costs.
- Define “real-time”: Define tools and how your team plans to use them. This review should point to a couple of systems that should cover your needs for both real-time and batched data. Then look at how these tasks correlate with the needs of the whole company.
- Quantify your needs: Define who the decision-maker is in this process, the frequency, and the maximum latency that your project can admit. Look at what processes need quick unprocessed data, and what needs a more in-depth analysis. Dividing these needs may seem to add an unreasonable amount of work, but in practice it saves money and makes each system more efficient.
Batch processing
Batch processing occurs when a portion of data is collected over a time period, stored somewhere and finally processed.
For example, imagine that your team acquires daily transactional data and stores them in a file. At the end of the month, there is the need to analyze such data, and in order to do so, the company processes this large file containing single smaller transactions.
Batch data processing is an extremely effective way of processing large amounts of data and it also helps to reduce the operational costs that businesses might spend while processing single blocks on data with higher frequency.
Moreover, it doesn’t require specialized data entry skills to support its functioning and it’s generally way more intuitive and widely adopted.
Once started, the team can have a full control on how the processing is going (there is not a black box effect) and the running can be scheduled according to the business needs.
Challenges of batch processing
The main problem is related to the cost management behind the choice of adopting batch processing. Indeed, many companies adopt batch processing to save money, while actually the infrastructure and storage require a relevant amount of expenses in the beginning. The team will need to be trained to understand what a batch is and how to schedule it, how batches are triggered and how to deal with notifications.
Conclusion
Ultimately, one size does not fit all for the analytics industry. Making a decision of which method select depends on the current business system.
Your team should focus on the various conditions like the type and volume of data and time that the data needs to be processed.
Moreover, the final challenge is trying to figure out the best way to deal with the huge amount of data that is being generated and moved.
Fyrefuse can help manage data flows both in real-time and batch to reduce efforts and pain on scaling robust data solutions.
