Streaming ETL

TOP ETL: Streaming ETL

Streaming ETL (extract, transform, load) is a process that allows companies to extract data from multiple sources and then load it into the destination system. It differs from batch ETL because of its continuous operation, which means you can get data as it changes in real time. This blog post summarizes streaming ETL and how it works so you can make more informed decisions for your business.

Why Should You Care about ETL?

ETL is an essential component of all business intelligence (BI) systems because it takes raw data in its original form (such as PDFs or spreadsheets) and converts it for use by other software applications on a computer or mobile device.

The main reason you should care about ETL is that most BI tools cannot handle large volumes of unstructured data, such as social media posts, images, videos and documents, so they need ETL to do the heavy lifting for them.

Why Streaming ETL?

Streaming ETL moves data as the system receives it. For example, a streaming ETL system will continuously monitor an event stream and extract information from it before performing transformations on that extracted info. At the same time, additional processes run against other streams for load balancing or failover purposes.

Data is what fuels the world. Businesses can't afford to sit around and wait for days, or even hours, before they get their hands on it. They need their data as soon as possible so they can base decisions on current trends rather than those of the past.

The "batch ETL" model does not work well in modern business environments because outdated information can cause serious problems, including missed deadlines. It could lead to a financial loss down the line if you're basing your decision-making process solely off old data when there's new data to review.

Technology is developing rapidly, and you need to stay prepared for the future. Processes like fraud detection and real-time payment processing require continuous data streams to provide information as soon as it's available.

A good example of a process that uses streaming ETL, credit card fraud detection works to identify and stop credit card theft. When you swipe your credit card, the transaction data goes to an application that then performs a transform step to join it with additional information about you before applying algorithms to determine if there are signs of fraudulent activity. It then approves or declines your transaction.

Banks and credit card issuers can reduce fraud losses by billions a year by using streaming ETL. As soon as you make your purchase, the institution can determine if it is legitimate within seconds. This process makes the transactions that much more secure for both parties involved.

Streaming ETL Architecture

Data is cyclical. You need to extract it from its source, process it, and then safely store it in a data repository for reuse by application software as required. Destinations vary, but they typically include large repositories with the ability to support multiple applications or other systems focused on specific tasks and analytics.

In traditional ETL architectures, there are three main components: an input system (source) that feeds information into your middleware (ETL tool), which processes it before passing it off to the destination of choice — usually some type of database server where all relevant pieces of information can rest until they're needed again, without clogging up individual databases within each respective app/system's storage limits.

When you use real-time streaming ETL architectures, sources are the left side of a pipeline that feeds information to your stream processing platform. The backbone for many other applications, including streaming ETL processes, this application is where streams from these sources merge into one unified system so they can process with ease in all parts in the pipeline.

Destinations such as databases store the output created by an upstream process. When it's time for them to receive new incoming data feeds and after a successful run has completed, they will either extract data themselves or publish directly onto the software without stopping anywhere along the line. The system can deliver data simultaneously to other applications and repositories.

The Future of Streaming ETL

The future of streaming ETL is difficult to predict, but it is sure to transform how companies approach data processing. At the moment, streaming ETL is the best solution for organizations that are having a difficult time with traditional batch ETL pipelines.

Some companies may want to use a streaming ETL pipeline in addition to their traditional batch pipelines, depending on the specific use case they are tackling. It is also possible that companies will migrate from using their existing databases and data stores altogether in favor of leveraging distributed file systems or object storage solutions.

Companies can then access this pool of information through different APIs, which return data as soon as it's available rather than waiting for an entire load cycle to complete before returning any results.

Streaming ETL Helps to Future-Proof Your Business

So what does this mean for your company? Well, it could potentially have significant implications. For example:

The reason that streaming ETL can help detect fraud earlier is because you're not waiting on a batch process to finish before you are alerted of any anomalies or irregularities across all your customer records — instead, alerts go out within seconds or minutes of an event happening, so they won't go unnoticed like they do when you are using traditional Enterprise Data Warehouse (EDW) based analytics tools.

Streaming ETL also offers near-real-time insights into facts like customer lifetime value, predictive forecasting and other information vital to running a successful enterprise today. This may allow your company to react more quickly when customers change their behavior patterns towards your products or services.

In 2021, most businesses are moving marketing and branding efforts online through social media. Social media however is an infinite stream of data where users are constantly making new social posts to their pages every day. Streaming ETL is the future of data processing. Your business will be more effective and successful if you are able to process information on social media in real-time as it comes into your database, eliminating unnecessary delays that can cause bottlenecks or worse: a missed opportunity.

Stream processing offers the potential to rethink how data flows in and out of an organization's systems by enabling iterative processes that are responsive at a fine-grained level. This is better than relying on batch techniques, which can be time-consuming because they involve staging large data sets before analysis or transformation.

The world of data is changing. With the rise of cloud applications, we are seeing a shift in focus from traditional batch ETL to streaming ETL with real time stream processing. This means that customer information can be automatically extracted, transformed then loaded to any destination you want within milliseconds. Implementing streaming ETL for your business will now future-proof it against changes in the industry. You'll be able to adapt more quickly than companies that don't have this capability, and you may also identify new opportunities for your data.