When you hear the sound of a Ferrari, you’ll find that sound so unique, which is a result of years of hard work by the designing engineers, connecting the driver’s experience to the car. Similarly, the data processing engine plays the role of connecting the user experience to the data. If you want to dive deep into the data solutions implementation, joining Informatica Classes will help you learn the various aspects of data needs and their fulfillment.

When you talk about data management in an organization, data processing engines receive the data pipelines, conceptualize the business sense, either simple or complicated.  Then you can process data on various frameworks like Apache Spark in optimized, streaming, or batch-wise approach in cloud or on-premises.

Many data engines are available in the market, but just like selecting a car for your use, you search for different main features and differentiators that change your opinion from one to another. Informatica is designing data processing engines for at least 25 years. Over this time, it has implemented top-class and enterprise-ready data engines to assist different data workloads on-premises or in the cloud.

8 Key Concepts of Data Processing Engine

Informatica with its strong experience, these are 8 concepts of data processing engine that you should know when evaluating various data platforms:

Validation:

A lot of design tools normally produce an XML or JSON depiction of a data pipeline.  The data engine usually revalidates the definition of pipeline and substitutes placeholder parameters with actual parameters generated while processing. If the data pipeline displays reusable components of a pipeline or mapplets, they are also extended.

Optimization:

Design tools enable the users to create data pipelines in a simple step-by-step process. And, the data processing engine has to ensure that the data pipeline is logical and easy to maintain, so it is suitably interpreted to code processed in that engine. For instance, if the data pipeline is translating data from a relational table and implements a filter, it is suitable to push that filter down to the relational database. This simple way of optimization has the following advantages:

  1. Quickly translating data from a relational table when you do it on a small subset of the data
  2. A relational database engine allows speedy reads by using database index
  3. Combining the steps between “read” and “filter”, this method eradicates the need for unnecessary data flow

Code Generation & Pushdown:

After validation and optimization of the pipeline, it needs to be translated into an optimized code to carry workloads regarding the transactional, database, big data, and analytical. The data processing engines present two modes of code translation to support various computations of workloads that are: native and ecosystem pushdown.

The data processing engine of Informatica provides its own execution environment with its native-mode capabilities. For execution, the ecosystem pushdown mode translates the data pipeline into another abstraction, such as Spark or Spark stream processing.

Resource Acquisition:

The execution of the data pipeline may fail and result in loss of computing resources without an appropriate resource acquisition upfront, and you may fail to notice SLAs. But, while using Informatica’s native execution mode of the data processing engine, it will hold back the resources where the engine is processing, such as on Linux or Windows.

If it is in pushdown mode, the data processing engine will obtain the necessary resource right from the ecosystem like AWS Redshift, Spark, Azure SQL or a relational database. In the streaming condition, where the processing of workload is continuous, the resource strategy should be flexible and should consider the received streaming data.

Runtime:

When the data processing pipeline is validated, optimized, translated and necessary resources are acquired, it is required to process the code and run. The data processing engine should be capable of running low-level data operations. It must store data in memory efficiently, reduce marshaling and unmarshalling of data, maintain buffer management, etc. Informatica’s native engine is customized for competent run-time processing and Apache Spark utilizes Project Tungsten to attain efficiency.

Monitoring:

When processing a task, an efficient data processing engine must show the progress and its health-related data. Monitoring must present meaningful insights into data, which can be made possible by monitoring UI, API or CLI. Monitoring varies delicately for different batches and streaming workloads. For example, due to the continuous streaming of workloads, you will have to monitor data volume versus the number of jobs run under process. 

Error Handling:

The data processing engine must be able to detect an error condition and resource allocations for cleanup, temporary documents, and files, etc. Error handling can be achieved at the data engine level and all processing engines will follow the same format or can be done at the data pipeline level, where every pipeline holds its own error handling directions. Similar to monitoring, here also the errors are handles separately between batch and streaming workloads. When an error takes place in a batch workload, this task can be started again and the processing of data occurs in the next workload invocation. While in real-time streaming mode, restarting option might not be available out there.

Statistics Collection:

After the completion of the task, the data processing engine should have to record various statistics like total runtime, status, the runtime of every single transformation, and the number of requested resources and used. The noted information is recorded and made available for use in future optimization tasks, particularly for the “Resource Acquisition” step.

Summary

Here you’ve covered a few concepts of data processing engines that will help you to learn how a data processing engine works like a central component for a data platform. Into the deeper details, you’ll get to learn the further details of vast concepts and capabilities of data engines, such as push-down optimization and serverless compute. But before you get into details, you have to know about creating various data processing pipelines in Informatica’s Cloud server.

If you want to learn more technical aspects, tips and tricks, data needs and their solutions, etc. joining Informatica Classes will help you to earn the best practical and technical knowledge about various concepts. ExistBI is authorized Informatica Partners and offers custom or fit-for-purpose Informatica training in the United States, United Kingdom, and Europe. Contact us today for more details.

 

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