Now think of a Ferrari. The sound of its engine is no coincidence. Its entire success is the result of countless hours of design effort. And all this is done to keep you connected to the car.
A data processing engine works similarly. Its job is to connect your data with those who need it. How is this possible? Well, Informatica training can teach you all this.
What Does a Data Processing Engine Do?
In any organization, data flows through pipelines. Some pipelines are simple, some are complex. In both cases, a data engine ensures the pipeline is used to its full potential. They can work in environments like Spark. They can work in batch mode. They can work on real-time streams. They can be cloud-based. They can be on-premises.
Informatica has been building these engines for over 25 years. This is reflected in the versatility and functionality of their engines. There are many data engines available in the market. Choosing a data engine is a lot like choosing a car. You check its features and select according to your priorities. And this is where Informatica training comes in.
8 Key Concepts of Data Processing Engines
Here are eight concepts that you will learn in most informatics training courses. Each of them plays an important role in data processing.
1. Validation
In most cases, design tools create a pipeline using XML or JSON format. Before executing the pipeline, the engine performs validation. The engine replaces placeholders with actual values and any reusable parts.
2. Optimization
Design tools help you design your pipeline step by step. However, a smart engine also seeks shortcuts.
For example, if your pipeline reads data from a table and then filters it, a good engine will pass the filter down to the database layer. The results are obvious:
- Faster reading of small amounts of data
- Faster lookups based on database indexes
- Fewer processing steps from reading data to filtering
This is one of the first things you learn when studying informatics.
3. Code Generation and Pushdown
After completing various tests, the pipeline needs to be converted into executable code. Informatica tutorials provide two ways to do this: native and pushdown mode. In native mode, the Informatica engine processes the work, while in pushdown mode, the work is submitted to a different system. This can be Spark or Spark Streaming.
4. Resource Acquisition
Pipelines require computing resources to process. Without this processing, jobs will be canceled, and deadlines will not be met. Informatica reserves the necessary resources when in native mode. This can be on Linux or Windows.
In pushdown mode, computing resources are provided by the target platform. These include AWS Redshift, Azure SQL, and Spark. Flexibility is crucial for streaming jobs, as new data arrives constantly.
5. Runtime
This is where the real magic happens. An efficient engine performs tasks quickly. It manages memory effectively. It minimizes unnecessary operations. It optimizes its data buffer management. Informatica’s native engine is designed for this purpose. Apache Spark uses Project Tungsten to achieve the same objective.
6. Monitoring
What you can’t see, you can’t fix. A good engine shows you the job’s status as it runs. This can come through a dashboard. It can come through an API. Or it can come through the command line. Monitoring differs depending on the type of job:
- Batch jobs track status and progress
- Streaming jobs track the amount of data over time
Both topics are discussed in depth in Informatica training. They require completely different troubleshooting steps.
7. Error Handling
Jobs fail sometimes. That’s the reality. A reliable engine catches the error. Then it cleans it up. It deletes temp files. It frees up the resources that were used.
You can handle errors at the engine level, where a specific set of rules applies to each job. Or you can handle them at the pipeline level, where each pipeline can define its own rules. Batch and streaming jobs also handle failures differently. Batch jobs can often resume where they left off. But streaming jobs usually can’t resume in the same way.
8. Statistics Collection
When a job is finished, the engine keeps a log of what happened. This includes the total runtime, the job status, the time each step took, and the number of resources used.
This data is not just for record-keeping. It helps with future planning. It is most important when making resource decisions later.
Why This Matters for Your Career
The eight concepts mentioned above define the fundamental principles that govern the performance of data platforms. Other areas worth learning include push-down optimization and server-less computing.
First, you should familiarize yourself with the basics. For this reason, start building pipelines on Informatica’s cloud-based tools. ExistBI is a Certified Training Partner of Informatica University, offering official and customized Informatica training courses in the US, Canada, the UK, and Europe. ExistBI employs over 100 Informatica-certified engineers. Training is provided on-site or through virtual instructor-led classes, tailored specifically to your organization’s needs.
Are you ready to expand your knowledge of data processing engines? Then take an Informatica training course as your next step. Contact ExistBI today to choose the best training for you.
