Data is certainly the new currency in today’s digital economy, but keeping up with changing enterprise data and increasing business information needs is still a struggle. This explains why companies are moving data from their traditional infrastructure to the cloud to measure data-driven decisions. This ensures that a company’s valuable resource — data — is regulated, trusted, easily managed and accessible.
While enterprises agree that cloud-based technologies are key to ensuring data management, data security, privacy, and process compliance across the enterprise, there is still an interesting debate about how to process data faster. That’s the trade-off between batch and stream processing.
Each approach has its pros and cons, but the choice depends on your business needs. We’ll delve into which use cases need batch processing and which use cases need stream processing.
What is the difference between batch and stream processing?
A batch is a collection of data points combined at a specific time interval. Another term commonly used for this is data window. Stream processing, used to process continuous data, is the key to turning big data into fast data. Both models are valuable, and each can be used to solve a different use case. They can even “merge” and do data Windows, or microbatches, in the data stream.
While the batch model requires a set of data collected over time, streaming requires data to be fed into an analysis tool, usually in real-time microbatches. Batch processing is often used when dealing with large amounts of data or data sources from traditional architectures, and processing data directly in a stream is not feasible. By definition, batch data also needs to be loaded into some type of storage, database, or file system for all the data needed for batch processing before it can be processed. Sometimes, before starting the analysis phase, the IT team may sit idly by and wait until all the data is loaded.
Stream processing can also be used to process large amounts of data, but batch processing works best when you don’t need real-time analysis. Because stream processing is responsible for processing data in motion and providing analysis results quickly, it can produce near-instant results using platforms such as Apache Spark and Apache Beam.
For example, Talend recently released Talend Data Streams, a free Amazon Marketplace application powered by Apache Beam that simplifies and speeds up the ingesting of large amounts of Data in real time.
Is batch processing necessarily better than stream processing?
Whether you prefer batch processing or support streaming, the two will “converge” better. While stream processing is best suited for use cases where time is important and batch processing works well when all the data is collected, this doesn’t mean one is better than the other – it really depends on your business goals.
However, we are seeing a significant shift in companies trying to leverage flow processing. According to a recent survey of more than 16,000 data specialists, the most common challenges in data science include dirty data, overall access, or data availability. Unfortunately, streaming tends to exacerbate these challenges because the data is in motion. Addressing these accessibility and data quality issues is key before jumping ship to real-time streaming processing.
When we talk to companies about how they collect data and accelerate innovation, they often say, “They want real-time data,” and we ask, “What does real-time mean to you?”
Business use cases may vary, but real-time depends on the ratio of event creation or data creation to processing time, which could be per hour, every five minutes, or per millisecond.
Why did the company switch from batch to stream? Let me draw an analogy. Imagine you’ve just ordered a batch of beer from your favorite brewery and the guests are ready to drink. But before you can drink, you have to rate the beers based on hop flavor and edit online reviews to rate each beer. Getting from one beer to another can take a long time if you know you’re going through the same, repetitive process with every new beer you drink. For a business, beer is the equivalent of pipeline data. Instead of waiting until you have all your data to process, you can microbatch it in seconds or milliseconds (which means you can drink beer faster!). .
Why stream processing?
If you haven’t used streaming in a long time, you might be asking, “Why can’t we batch as before?” Of course you can, but if you have a lot of data, it’s easy when you need to extract the data, but hard when you need to use it.
Companies see real-time data as a game changer. But without the right tools, achieving this remains a challenge, especially because businesses need to deal with the increasing volume, variety, and types of data from many different data systems, such as social media. At Talend, we see that companies often want to have more flexible data processing so they can accelerate innovation and respond to competitive threats more quickly.
Sensors from wind turbines, for example, are always on. As a result, data flows continuously. Because there is no data to start or stop, the typical batch method of ingesting or processing this data is obsolete. This is a perfect use case for using flow processing.
Big Data Debate
It is clear that enterprises are shifting the priority of real-time analysis/flow processing to real-time collection of actionable information. While outdated tools cannot handle the speed or scale involved in analyzing data, today’s database and flow processing applications are ready to deal with today’s business problems.
Here’s what’s important in the big data debate: Just because you have a hammer doesn’t mean it’s the right tool for the job. Batch and stream processing are two different models, and it’s not a game of either, it’s a judgment call on how to determine which is best for your use case.